jcs journal vol.3, issue1

3

JCS Journal ISSN: 0799-3838

Published in Jamaica by Jamaica Computer Society

2 ¾ Ruthven Road,

Kingston 10, Jamaica, W.I

Tel: (876) 497-1442

Email:[email protected]

www.myjcs.com

Vol. 3, No. 1

Copyright © 2014 by Jamaica Computer Society

All rights reserved. No part of this publication may be reproduced or utilized in any form

or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, without the prior permission of the publisher

and copyright holders.

Editor:

Jovan Evans

Editorial Committee: Dean Smith (President – JCS)

Jovan Evans

Photographs by: Jovan Evans

All correspondence and subscription requests should be addressed to: Jamaica Computer Society

2 ¾ Ruthven Road,

Kingston 10, Jamaica, W.I

Tel: (876) 497-1442

Email: [email protected]

Subscriptions:

Single copies J$900; three or more copies J$600

http://www.myjcs.com

4

5. ICT as Enabler VTI Graduation Ceremony Address

7. JCS Governing Council

9. Careers in Cyber Security

11. Effective Mobile App Development

15. Biztech Forum

Conference Chairman Address

Conference Schedule

17. Conference Presenters

Carlton Samuels

Dr. Louis Shallal

Dr. Paul Golding

Yoni Epstein

Dr. Noel Cowell

Mervyn Eyre

Dr. Maurice McNaughton

21. Abstracts

Academic Perspective for an ICT led national economic

growth agenda: The role of ICT policy in productivity im-

provements.

Beyond eGovernment: Thoughts on Smart Government

Building High Performance Work Systems in Caribbean En-

terprise: Harnessing the Synergy of Human Capital & ICT

New ICT Consumption Models: Helping CIOs Make the Leap

Open Data & The Private Sector: Business Models & Opportu-

nities

25.Big Data Security

33.Big Data & Government

43.Governance of Big Data

5

A t a seminar held at the Uni-

versity of Technology

(UTECH) under the theme:

“Regulating the ICT Indus-

try and its Effects on Growth & Devel-

opment” on Tuesday, December 2,

2014; several presenters highlighted

the various efforts to promoted ICT as

an enabler:

1. Entrepreneurial efforts among

the younger ICT professionals,

such as; Connectimass, DigiJam,

Kingston Beta, and so on.

2. Curriculum improvements and

programs at almost all our Ter-

tiary and some secondary schools.

3. Introspection in Private sector

SMSEs and larger entities as well

as the public sector on ICT usage

and relationships with elements

of the ICT ecosystem.

4. The work being done to establish

a single regulator for the ICT sec-

tor.

This highlighted to me, that there is a

groundswell of concern for meaningful

growth. This growth must of necessity

be reflected in improved ICT global

rankings and national economic out-

put. While yet to attain critical mass,

there is still evidence of a missing link,

that ingredient that will bring com-

pleteness to the recipe. More and more

I am convinced that this ingredient

must come collectively in “we the ICT

people”. The solutions lie within US.

The focus has to be on the human capi-

tal, our people. The ICT profession is

ripe for attention;

1. There are so many so-called ICT

practitioners that give the profes-

sion a bad name, accepting assign-

ments for which they are not certi-

fied or competent, then doing a

poor job because the esoteric na-

ture of their tasks do not allow un-

informed oversight.

2. The value of the contribution of

many ICT professionals in both

private and public sectors is not

fully recognized in the remu-

neration that they receive as

well as the weight given to con-

tributions they make to busi-

ness strategy and tactical oper-

ations. Many just relegate

themselves to “do as they are

told” and just “keep the sys-

tems running” .

3. The need to have ICT govern-

ance input from the highest

levels is not fully appreciated;

so many boards especially in

the public sector languish with-

out a board vision of how ICT

should be leveraged to further

the organizational goals.

ICT Excerpts from the address by JCS President, Dean Smith at the

Graduation Ceremony of Vector Technology Institute (VTI),

held at the Knutsford Court Hotel on December 7, 2014

6

In a 2013 study at the JCS, we found that there is a strong

positive correlation between membership subscriptions

for the JCS and the World Economic Forum – Global In-

formation Technology Readiness (WEF GITR) index.

When JCS membership went up, the index went up, when

the membership went down, the index went down. While

there is no claim to causality (one does not cause the oth-

er), the evidence points to the fact that apathy within ICT

the profession (reflected in fall of JCS membership and

lack of involvement in JCS programs) does have a national

impact.

So many national initiatives that affected the ICT industry

came out of efforts and activities within the JCS. We re-

call the liberalization of the telecoms industry being a hot

topic of meaningful discussions when it was mooted for

the first time at a panel discussion in a JCS conference,

just prior to activities in that regard.

Now you have this opportunity as young professionals to

be catalysts for ICT leverage on a national scale. I chal-

lenge you therefore, to be agents of positive influence as

vectors and conveyors of a message: there is value in the

collective ICT intelligentsia of the ICT community. This

challenge is to examine and contribute to advancement of

some issues that have been identified by the JCS as critical

elements for serious ICT adoption:

1. Mandated continuous education programs at the JCS,

for professional development and capacity building.

2. Broadening a regulatory scope of professional over-

sight and rating of ICT professional; as proposed to

NICTAC

3. Revolutionizing vendor/client interaction that feature

good IT governance, business value and national in-

terest

4. Organization of Government of Jamaica’s (GOJ) ICT

efforts thru an effective national CIO

5. Island wide and comprehensive inclusion of all ICT

professionals in the JCS

All of these have the common thread of significant partici-

pation of ICT professionals with demonstrated value at the

personal, enterprise and national levels. As professionals,

you too can and should contribute as active members of

this intelligentsia. By looking at these issues or developing

new perspective in the many Special Interest Groups that

exist within the JCS, you can advance the programs that

are so necessary for Personal, Enterprise and National

growth. We look forward to your contributions.

The aim of the JCS is to be the national body that pro-

vides leadership in the promotion of the efficient and

effective use of Information Technology in Jamaica, pri-

marily through practicing professionals.

The high level objectives of the JCS include: setting

standards to ensure professional competence and pro-

fessional ethics, encouraging continuous education, pro-

moting social responsibility and professional steward-

ship as well as addressing membership interests.

I challenge you therefore, to be agents of positive

influence as vectors and conveyors of a mes-

sage: there is value in the collective ICT intelli-

gentsia of the ICT community.

7

Jovan Evans ………………………………..………………………………...Deputy President

Khari Bryan …………………………………………………………………………VP Finance

Brad Clark …………………………………………………………………….VP Membership

Glenice Leachman …………………………………………………………………..VP Events

Rohan Morris……………………………… ……... VP Certification Education Standard

……………..…………………………………………………..& Accreditation & VTI Campus

……………………………………………………………………………………Chapter Liaison

Dean Smith………………………………………………………. Immediate Past President

……………………………………………………………..…………& Conference 2015 Chair

Mervyn Eyre…………………………...………Council Member and President of JITSA

Richard Shaw……………………………………...…………………………Council Member

Douglas Williamson ………………………………………………………Council Member

Keita Mendes ………………………………...………………..CS Western Chapter leader

……………………………………………………………..& MBCC Campus Chapter liaison

Andre Thompson…………………………...……..MBCC Campus Chapter Coordinator

Maurice Coke……………………………………………….. PCC Campus Chapter liaison

Kerene Graham………………………………...…….PCC Campus Chapter Coordinator

Henry Osborne……………………………...………..NCU Campus Chapter Coordinator

Oliver Hylton………………………………...……...Utech Campus Chapter Coordinator

Governing Council

9

T he Cyber-security area of IT is one of the fastest grow-

ing fields, dynamic and ever-evolving. There are how-

ever emerging trends and career standards. Jamaica is

stepping up to match pace with the rest of the world.

There have been a few recent efforts to define cyber-security ca-

reers, highlighting the dearth of skilled personnel as well as the

need to immediately build capacity. The Jamaica Computer Soci-

ety (JCS) has joined efforts to support capacity building, having

participated in the development of the recently launch National

C y b e r - s e c u r i t y

Strategy. One of the

first steps, along

with industry ex-

perts, was to launch

the cyber-security

s p e c i a l i n t e r e s t

group (SIG). At its

inaugural meeting;

the topic was Team

Composition. The

gist of the proceeds of this meeting is presented here in an effort

of local practitioners; defining the needs of the local industry seg-

ment.

At last check (April 18, 2015) there were only sixteen (16) CISSPs

(Certified Information Security Professionals) designated certifi-

cations attributed to Jamaica. This CISSP certification is useful in

almost all areas of a career in cyber-security (See Table 1). In-

creasing this number is seen as a priority. The main certifying

bodies in cyber-security are ISACA (Information

Systems Audit and Control Association) and

ISC2 (International Information Systems Secu-

rity Certification Consortium) which does the

CISSP certification. There is also CompTIA

(Computing Technology Industry Association).

CompTIA has been established in Jamaica and

has a good working relationship with the JCS and

its training partners as well as several tertiary

institutions. The JCS-CS (SIG) is to work with the

local planning group for the formation of a local

ISACA chapter to conduct events geared towards

promoting the value of the certification as well as

other useful certifications in cyber-security field.

These certifications make professionals more

marketable to organizations locally and interna-

tionally. Certifications also give an indication of

the profession’s role on the team and industry

career.

Typically, IT security team will consist of defini-

tive roles and functions that may sometimes re-

side in a single person depending on the size of

the organization and IT resources available.

These functions may even be outsourced. Pre-

senter Derick Burton of Digicel group had this to

say:

“Whether you use a dedicated team approach or

embedded resources or even outsourcing as a

management strategy, Management sponsorship

is critical.”

“There must be an un-

derstanding that cyber-

s e c u r i t y i s n o t t h e

‘Business Prevention

Department’. Clear au-

thority and responsibili-

ties must be defined and

acceptance of risks ap-

propriatel y ac knowl-

edged.”

Staff retention may become a concern for em-

ployers, especially in the public sector and as

State Minister in the Ministry of Science Tech-

nology and Mining, Hon Julian Robinson stated

in his remarks at the JCS SC-SIG launch “We

know that our government pay scales for cyber-

security and other IT functions are very low in

A Statement by the Cyber-security Special

Interest Group (CS-SIG) of the Jamaica

Computer Society (JCS)

The JCS has joined efforts to support ca-

pacity building, having participated in the

development of the recently launch Nation-

al Cyber-security Strategy.

10

relation to the private sector but we still have to try to re-

cruit and retain good staff”. He went on highlight plans

that cut across several ICT initiatives, including the estab-

lishment of a Cyber-security Incident Response Team

(CIRT).

Role functions Expected Qualifications

Useful certifications

IT Security Engineer Network Security, Endpoint, Database BSc (IT) BSc (CS) BSc (CE)

CISSP MSCP MSCE Network+ Security+ Mobile+ SSCP

Manage security infrastructure IDS, Firewalls

Overlap with System Administrator Create Procedures, Standards and Guide-

lines Incident Response

Information Security Analyst

Systems design validation, System assur-ance

BSc (IS) MIS

CISM CRISC Security+ CISSP

Monitor systems / logs /reports /trends Risk assessment Recommend courses of action Policy writers Incident Response Penetration Testing / Vulnerability As-

sessment

System Architect Design of complex systems

BSc (IS) MIS MSc (IS) with specialization(s) in:

Enterprise Archi-tecture

System de-sign

ITIL/CObIT/MOF/ISO SABSA/TOGAF/ZACHMAN CISSP CISM CRISC ITSM CGEIT CISSP-ISSAP

Ensure consistency in solutions across enterprise

Business requirement analysis Define control objectives Contribute to policy Consultant for new projects & improve-

ment efforts

Information Auditor Management controls review BSc(IS) CISA CISSP ITIL/CObIT/MOF/ISO CSSLP Data security/integrity

Procedure integrity Software assurance

Digital forensics Investigation and recovery of data & metadata found in digital devices

BSc (CS) BSc (IT) BSc (IS)

CEH CISSP CCFP GCFE GCFA CSSLP

Law Enforcement support Most often related to suspected crimes

involving computers Providing expert witness in court cases

11

There is a closer relationship between the Mobile applica-

tion design and development process and the finished

product, than most people realize. With that said, the ef-

fectiveness of the mobile application development pro-

cess, does not guarantee that effectiveness of an applica-

tion. Effective mobile applications, solve a clearly defined

problem, continuously and is repeatable and scalable . An

effective development process should incorporate the fol-

lowing processes,

Problem Identification

Solution Design

Marketing

Product Support.

To illustrate our point we will examine the development

process of an application that will allow Jamaicans to send

packages of any type, from one place or person to another,

for a fee.

We will call the application “Gimmie & Gwaan.”The pro-

cess of Product Identification, involves sub activities,

which may include identifying the problem the application

will solve. The following are the sub activities of the Prod-

uct Identification process,

Focus Groups

Product Ownership

The focus group process does not have to be clinical in na-

ture, but can be as informal as soliciting opinions from

friends and family. However, when a more frmal approach

is taken, and a clinical style focus group is created the

“true” product emerges. Participants should represent

users in the mobile application’s ecosystem. The applica-

tion’s core functional elements are normally born from

this process, as the Focus Group Director should be

careful not to “lead” the group, but rather state the

problem vaguely and facilitate the idea flow from the

group, as they start defining the product as “they” see

it. This process sometimes result in a significantly dif-

ferent idea than the original problem statement.

The Product Ownership role is the responsibility of one

person, the “Product Owner,” This person plays sort of

a supporting cast role throughout the life of the Appli-

cation Development Process. This process involves con-

tinuous research and monitoring of the “market” and

the “product requirements” to determine if the prod-

uct is “still” viable. The application development pro-

cess can be halted for reasons that may include, but not

limited to,

Another entity has a legal claim

Application requires technology not yet available

or too costly to produce.

Market oversaturated with the similar solutions.

An effective Product Identification process, directly

drives the effectiveness of the mobile application devel-

opment process as it clearly outlines the features that

the application should have, as suggested by the focus

group. At this point, application design can and should

begin. It is important to point out that, the focus groups

and product ownership should continue perpetually

throughout the design process.

The Solution Design process of a mobile application is

arguable the most important process, as most people

think that if the application is well designed, then it’s

likely to be a successful application when deployed.

That could not be further from the truth. What increas-

es the success factor of the design process is the how an

application is designed. The following are the key fac-

tors that contribute to how an application is designed.

The Development Team

The Functionality Analysis

The Technical Design

The Testing Strategy

The Deployment Strategy

Kevin Moore, CTO NubeSystems

12

The Development Team

Product Owner

The person that continuously researches the market

industry to determine if the product is viable or is any

changes are necessary to make the product viable

Project Manager

This individual is responsible for ensuring that the devel-

opment process adheres to the required timelines and

that al features and functionalities have been met. Also

serves as liaison between the Product Owner and the de-

velopment team, as changes in functionality may be re-

quired, during the development process

Developers

These individuals will be responsible for the application

code, both on mobile device and server

Tester

This individual will continuously test the application as

each coding functional task is completed

The Functionality Analysis

The Functional Analysis process can make or break a

Mobile Application. This is the process where the results

of the focus group are grouped functionally and func-

tional elements are determined. These functional ele-

ments , also known as functional tasks, are normally

manageable and are decoupled units of work. Using an

Agile Development Methodology (outside the scope of

this article) , these units of work represents product back-

log items (PBI’s), aka items to eventually be completed,

and are grouped together in what is known as sprints.

Sprints represents, normally, a two week burst of an appli-

cation development effort, consisting of the following,

processes,

Grooming

Development

Testing

Retrospective

The grooming process can be a technical or functional

design session, which serves to provide clarification to the

team. The development is the code execution of what

came out of the grooming sessions. Testing is done imme-

diately on each functional element as they are completed

during the sprint. At the end of each sprint, a retrospec-

tive is done, discussing what went well, what did not, and

how to correct it if necessary, for the next sprint.

Technical Design

Technical design becomes easier to manage when the

functional analysis’ grooming process is carefully done.

The grooming process helps the team generate PBI’s

which are normally represented in a functionality matrix.

For Gimmie & Gwaan, the Functionality Matrix would

look like this:

13

Items highlighted in green represents items completed

and tested successfully. At this point it is important to

mention that technical documentation is key to effective

mobile application development. Each of the functional

elements in the functionality matrix, should have its own

mini-document potentially consisting of diagrams, code

snippets, algorithm design, screen mockups, data flows,

ER Diagrams, Acceptance Criterias and any other docu-

mentation necessary explain what the developer did or

intended to do .

The Testing Strategy

A mobile application development process certainly can-

not be considered effective, without the Testing compo-

nent. One of important characteristic in software devel-

opment regardless of the platform, is the ability to test

the decoupled portions of the application. Consequently ,

it is considered effective development when the applica-

tion is "testable." Staying with Agile methodology of de-

velopment, testing happens concurrently with develop-

ment

Observing the functionality matrix in figure 2. , you can

see that the function of the application is broken down

into functional group and further into smaller functional

elements. Each of these elements should be inde-

pendently testable. When a functional element is con-

sidered “code complete” and its status is changed to am-

ber, it is assigned to a tester for testing.

The documentation for the specific functional element

being tested, must includes a “acceptance criteria.” The

acceptance criteria outlines a set of behaviours that this

functional element should perform. In the Gimmie &

Gwaan functionality matrix, item B5 represents a high

level summary of the behavior of the B4 - Crud Pickups

functionality.

When the tester is asked to test the B4 functionality,

they will follow the “testing directions” provided by the

developer. These directions, may indicate which screens

to navigate to perform a the creation of the a pickup re-

quest , the retrieval, update and and subsequent deletion

of a pickup request. Once the acceptance criteria is met,

the functional element set to green indicating completed

and successfully tested. The PBI - product backlog item

B4, is now considered done. This testing process activity

happens is parallel to the development effort.

The Deployment Strategy

The attention span of mobile application users exacer-

bated by the competitiveness of the various mobile “App

Markets,” may cause your users to either lose interest in

your application, or be distracted by another more

“shiny” and “new” application promising the same func-

tionality. To combat this is consider effective mobile ap-

plication development , to “release quickly and often.”

Deployment of a mobile application can happen at any

time. If there is at least a single functional element com-

plete, the application can be released; in most cases not very useful, but I am making a point . The Gimmie &

Gwaan functionality Matrix in our example is considered

the minimum amount functionality for an initial release of this application. If you think about it for a second, there

are many other functionalities that we could add to the application to make it even more compelling. For exam-

ple, “real-time two-way” communication with the courier.

This is a nice to have feature, however, releasing the appli-

cation without it, would ruin the user experience..

The Marketing Strategy

One of the most challenging aspects of effective mobile

application development is letting the public know about

your application. The various mobile Application Market /

Stores and filled with millions of apps that all promise you

“the world.” How then do you get your application no-

ticed. The Gimmie & Gwaan mobile application has cul-

tural elements and would not likely be immediately un-

derstood by a global market. With that said, focus the

marketing on local the culture where app was designed

for, suggests that imagery of application should show mo-

torbike couriers as is common place in Jamaica.

Good old fashion marketing is proving to still be effective

in this new paradigm of “There’s App for That.” The intri-

cacies and discussion of advertising campaigns should

better be left to the pros and is not in scope in this article.

However, what can be said is that, effectively marketing

your mobile application should start the day the decision

to write the application was made.

The Support Strategy

One of the most common and effective mobile application

development practices, is the building-in of support relat-

ed characteristics into your application. The two most im-

portant ones are Analytics and Bug Reporting.

B11- Analytics, in the Gimmie & Gwaan functionality ma-

trix indicates functionality for analytics. This functionali-

ty reports user usage metrics back to the development

team. The team uses this information to support the ap-

plication by making changes based on the observed met-

14

rics. For example, if the team notices that a particular

functionality is not being used in the application by the

users, the team would either consider improving it or

removing all together.

Bug reporting is equally as beneficial, as the develop-

ment team can learn about issues in the application and

fix them even before the user has a chance to complain.

This provides an awesome user experience.

Finally, providing the traditional way for the user-

community to reach you, email, phone and snail-mail

address are all very important and key to the support

strategy. Even though your application may be very inex-

pensive ,U$0.99, users have the expectation that it will

work as advertised, without errors or glitches, always on

any device, and will fiercely defend that U$0.99. Being

proactive is still the most effective form of support.

Conclusion

This article described what an Effective Mobile Applica-

tion Development process looks like. It outlined that

application or solution that solves a specific problem was

created. We did this by using a focus group, that eventu-

ally determined the features of the application.

Once the application decision was made , we identified a

Product Owner, whose job it is to ensure that the applica-

tion was viable in the marketplace. Given the OK from the

Product Owner, a development team was formed and

their roles clearly outlined. The ability to test the applica-

tion to ensure quality, led us into a test strategy that ran in

parallel with the development effort, ensuring problem-

identification early and often.

With the application development and testing well under-

way, and the Product Owner’s continuous viability-

product-research, the marketing effort took shape. The

application was being marketed to the public and partners

were being solicited and formed. An application release

date is set only after the application support strategy is

finalized, and once released, the development team can

respond quickly and effectively to the users.

What makes this Mobile Application Strategy truly effec-

tive, is that the process is simple, repeatable, and scalable.

15

A s far as ICT systems go, the main elements

can be succinctly and mnemonically stated

as: Programs, People, Processes, Pieces of

equipment, Procedures and Policy. It is in-

deed timely and relevant for a focus on the “people” re-

source in these systems as Jamaica desperately needs to

leverage resources for economic growth and ICT will

play a huge role. The human resource offers immense

potential but must be harnessed with finesse. By far the

system element that can make the biggest difference, the

Jamaica Computer Society (JCS) recognizes the possibili-

ties and dearth that exist within the knowledge worker

operating in the Jamaican milieu.

This 2015 event; under the theme: “Human Capital in

ICT systems: the Jamaican Knowledge worker” seeks

to examine some of the factors that impinge on this im-

portant system element. The sessions have been orga-

nized to target specific audiences from CEOs through to

CIOs and ICT technologists.

We also want to take time out to honor some that have

made exceptional contributions to the ICT sector and

profession over many years. Exemplary projects executed

over the last 18 months will also be acknowledged. These

will be done at the Awards banquet. So we invite all to

come out and support them.

Message from Conference Chairman for Showcase

2015 and Awards Banquet Launch.

Dean Smith, Conference Chairman

16

Show

case

Show

case

S

how

case

Show

case

Slot 1

Workshop

Track 1

Slot 2

Presentation

Track 1

Slot 3

Tutorial

Track 1

Slot 4

Tutorial

Track 2

Slot 5

Exhibition

Exhibitors’ set up T

h

u

r

s

d

a

y

IT Governance

(ITIL founda-

tions)

Mobile

App Dev

(MAD) on

Android

Cyber threat

responses:

Cecil McCain

9:00am to

12:30pm

L U N C H 12:30 to 1:30pm

Vendor presentations slots 1 1:31 to 2:30pm

IT Governance

(ITIL founda-

tions)

Mobile

App Dev

(MAD) on

Android

Cyber threat

responses:

Cecil McCain

2:31 to 5:30pm

IT Governance

(ITIL founda-

tions)

Opening Ceremony and official tour

(8:30 to 9:00am)

Mobile

App Dev

(MAD) on

Android

Cloud Essen-

tials

8:30 – 10:00 a.m F

r

i

d

a

y

Opening plenary: Smart e-

ICT Govern-

ance (ITIL foun-

dations)

Defining BPO as an ICT growth busi-

ness: Yoni Epstein

Mobile

App Dev

(MAD) on

Android

Cloud Essen-

tials

10:05am to

11:30pm

Internet Governance: Its significant

role in developing nations: Carlton

Samuels ICT4D/IGS

11:35am to

12:30pm

L U N C H 12:30 to 1:30pm

Vendor presentations slots 2 1:31 to 1:59pm

IT Govern-

ance (ITIL

foundations)

The Mobile Money/e-wallet projec-

tions : Livingstone Morrison (Deputy

Governor-BOJ)

Mobile

App Dev

(MAD) on

Android

Cloud Essen-

tials

2:00 to 2:55pm

IT Govern-

ance (ITIL

foundations)

Open Data Business Models & Busi-

ness OpportunitiesDr Maurice

McNaughton(MSBM)

Mobile

App Dev

(MAD) on

Android

Cloud Essen-

tials

3:00 to 3:50

New ICT consumption models; helping

CIOs make the leap:

Mervyn Eyre (CEO, Fujitsu)

Mobile

App Dev

(MAD) on

Android

Cloud Essen-

tials

4:00 to 4:50

XSOMO Party 8:30pm

17

Slot 1

Workshop

Slot 2

Presentation

Slot 3

Tutorial

Slot 4

Tutorial

Slot 5

Exhibition

IT Governance

(ITIL founda-

tions)

Proposal papers from Utech

Data visuali-

zation with

R program-

ming lan-

guage

Cloud Es-

sentials

9:00 to10:30am

S

a

t

u

r

d

a

y

Panel Discussion: “The projected

impact on Jamaica’s GDP of greater

Cloud Es-

sentials

10:31am to

12:30pm

L U N C H 12:30 to 1:30pm

Vendor Presentations slots 3 1:31 to 1:55 pm

IT Governance

(ITIL founda-

tions)

Building High Performance Work

Systems: Human Capital & ICT

Data visual-

ization with

R program-

ming lan-

guage

Cloud Es-

sentials

2:00 to 2:50pm

Academic Perspectives for an ICT-

led national economic growth agen-

2:55 to 3:40pm

Closing Plenary: A proposed ICT leg-

islative agenda focus – 5 year plan:

3:45 to 4:30pm

A W A R D S B A N Q U E T

Key note speaker: Hon. Minister Phillip Paulwell

(4 Awardees in 2 sections; 3 Lifetime Achievement Excellence awards and 1 President’s award)

7:00pm to 10pm

18

Meet this year’s conference presenters

CARLTON SAMUELS Carlton is actively involved in the policy development pro-

cess of the Internet’s Domain Name System. He’s served

as a member of the Strategy Panel reviewing ICANN’s role

in the Internet Governance Ecosystem as well as a mem-

ber of the Expert Working Group for the Next Generation

Registration Data Directory Services for the Internet. He

was formerly a Vice-Chair of ICANN’s At-Large Advisory

Committee (ALAC) and a founding Director of the Carib-

bean Internet Forum.

He has held several senior executive positions in private

sector and academic environments, most recently as CIO

and University Director of IT at The University of the West

Indies. He now works mostly as an international ICT4D

consultant specializing in policy, strategy and regulatory

affairs and is an adjunct lecturer in Information Science

in the School of Library and Information Studies, Faculty

of Humanities and Education, the UWI at Mona. He was

recently appointed by the Cabinet of the Government of

Jamaica to chair the National ICT Advisory Council of

Jamaica.

_______________________________________________________

DR. LOUIS SHALLAL, PHD Formerly CEO, Shallal Strategic Services providing exec-

utive advice to senior government officials through a

dozen or more consulting associates. Prior to this,

served for eight years as Chief Information Technology

Officer for the Government of York Region catering to

the technology needs of over 3000 employees. He also

served as the CIO for the City of Hamilton and enjoyed a

career with the City of Ottawa and the Region of Ottawa-

Carleton as Director of Geomatics then Executive Direc-

tor of IT serving tech needs of over ten thousand em-

ployees. Prior to that, he held a number of senior and

strategic engineering and planning positions with the

Region of Ottawa-Carleton and the Ministry of Transpor-

tation of Ontario.

Dr Shallal has a Ph.D. in Civil Engineering from Car-

leton University, Ottawa and holds a diploma from a

Program for Senior Executives in State and Local Gov-

ernment, the Kennedy School of Government at Har-

vard University. He has served on boards and or execu-

19

tives of a number of organizations and associations in-

cluding the CIO Associations of Canada and the Munic-

ipal Information Systems Association in Canada.

________________________________________________

PAUL GOLDING, DBA Paul Golding, DBA is an Associate Professor and Dean of

the College of Business and Management at the Universi-

ty of Technology (UTech), Jamaica. He has also served

the University as Head of the School of Computing and

Information Technology. Golding is an academic, an in-

novator, consultant and philanthropist. He is a multiple

winner of several of UTech’s most prestigious awards for

excellence in; research community service, teaching,

and innovation.

Golding is a prolific writer and has authored publications

in several journals including the Journal of Information

Technology Teaching Cases, Journal of Information Sys-

tems Education, International Journal of Internet Proto-

col Technology and the International Journal of Multi-

media and Ubiquitous Engineering. He has also authored

several teaching cases highlighting Jamaican issues with

global relevance and is a contributor to the Jamaica

Gleaner. His current research interest includes Energy

Management Technologies, the use of Technology to

teach Deaf Students, Telecommunications, and Mobile

Commerce, Radio Frequency Identification (RFID). Dr.

Golding is married with one child.

___________________________________________________

YONI EPSTEIN Mr. Epstein currently holds a leading authority on the

subject of outsourcing in Jamaica and is tasked with the

responsibility of collaborating with industry players and

the Government to expand Jamaica’s outsourcing indus-

try in his capacity as President of the Business Process

Industry Association.

He has over sixteen (16) years of professional experience

in the contact center industry and was ranked one of the

50 most influential executives in Nearshore Outsourcing

in Latin America and the Caribbean. He also represents

the ICT-BPO cluster on several private and public sector

committees geared towards enhancing the growth and

sustainable development of the ICT/BPO sector.

As CEO of itel-BPO Solutions, Mr. Epstein organization

has 500 full-time employees across 5 sites located in

Montego Bay and Kingston, Jamaica and Freeport, Grand

Bahama; with the Montego Bay office awarded

“Employer of the Year” both in 2013 and 2014 by the

Montego Bay Free Zone Company. Prior to establishing

itel-BPO Solutions in 2012, Yoni served as Global Director

of Site Operations at Sandals and Beaches Resorts’ con-

tact centre; where he managed fifteen (15) business units,

400+ employees in six countries simultaneously.

Mr. Epstein pursued his education in Hospitality Man-

agement at the Florida International University and later

studied Culinary Arts at Johnson and Wales University in

Miami, Florida.

20

DR. NOEL COWELL Dr. Noel M. Cowell is a Senior Lecturer in Employment

Relations at the Mona School of Business and Manage-

ment at the University of the West Indies, Mona. He has

conducted research and published on different aspect of

employment relations and human resource manage-

ment and is currently involved with a project focusing on

human resource models for corporate transformation in

the Caribbean.

DR. MAURICE MCNAUGHTON Dr. Maurice McNaughton is Director of the Centre of Ex-

cellence for IT-enabled Innovation at the Mona School of

Business & Management (MSBM) and lectures courses

in Modelling & Decision Support Systems; IT Economics;

IT Governance & Strategic Use of ICT. He is a UWI Engi-

neering Graduate and holds a PhD in Decision Sciences

at Georgia State University. His research interest spans

the domain of emerging Open ICT ecosystems, and in-

cludes Open Source Software, Open/Big Data, Mobile and

Cloud Computing.

MERVYN EYRE Mervyn Eyre serves as President and Chief Executive

Officer of Fujitsu’s business in the Caribbean, Central

America and Mexico regions. He is responsible for man-

aging the strategic direction as well as general manage-

ment of Fujitsu’s regional operations, assets and capabili-

ties. Leveraging over 25 years of experience in the global

IT industry, Mervyn has led the introduction of managed

and cloud based IT services in the region, opening up

new consumption models to Caribbean governments

and enterprises.

21

of information and communications

technologies (ICT) is a key factor in

Jamaica’s productivity problem. It ar-

gues that productivity improvements

must be the centerpiece of economic

policy and ICT must be the catalyst for

improved productivity and transforma-

tive change. This paper will discuss

public policies and business practices

that are conducive to the adoption ICT

and economic growth.

__________________________________

Academic Perspective for an ICT led national eco-nomic growth agenda: The role of ICT policy in

productivity improvements. Paul Golding

There is an increasing body of work focusing on Jamaica’s productivity problem. A comparison between Jamaica and its

main trading partners such as the United Kingdom, Canada, United States, Brazil and Colombia and several Small Island

Developing States (SIDS) such as Barbados, St. Lucia, and Trinidad reveals that over the period 1990- 2013, Jamaica rec-

orded the lowest labour productivity levels and growth rates. Another study has shown that Jamaica’s GDP per capita,

when compared to the USA, has reduced from 31% in 1950 to 19% in 2013. In comparison, the study showed rapid growth

for Trinidad and Tobago increasing from 34% in 1950 to 61% in 2013. Productivity increases stem from a variety of fac-

tors, chief of which is the use of more and better “tools” by producers; in other words, the use of more and better ma-

chinery, equipment and software. This paper hypothesizes that more ubiquitous adoption – as distinct from production –

Productivity increases stem from a

variety of factors, chief of which is

the use of more and better “tools” by

producers;

22

The presentation will highlight the trends in adoption of

IT by government over the past two decades and take the

audience in a journey on memory lane tracing the surge

in use of internet-based services and the lessons learned

from eGovernment implementation. It will then consider

a number of IT legacies such as desk tops, data centres,

telephony, applications and data access or lack thereof that

are currently facing governments. Finally, it will illustrate

the strategic directions in the adoption of smart IT to re-

place these legacies in the areas of customer service, com-

munication and open data and pin point the essential ingre-

dients for success: Smart Collaboration for Smart Services.

Beyond eGovernment: Thoughts on Smart Government

Dr. Louis Shallal, P.Eng, Ph.D.

The presentation is not at all political in nature, in fact it is all about defining the building blocks for a smart government

which is defined simply as a government that utilizes Information Technology (IT) to Provide: (a) Effective and conven-

ient services to citizens and businesses, (b) Meeting certain level of service that citizens wish, (c) at a cost lower than

without information technology and (d) in a sustainable manner.

business performance, the idiosyncratic structural char-

acteristics, resource configuration and business context of

the Caribbean enterprise demands a distinctive approach.

The role of ICT and in particular, an emerging generation

of Agile Business Intelligence / Analytics applications is

key to enabling the modern HPWS. The paper presents

findings and insights from current research conducted on

the HR practices of top HR professionals associated with

successful Caribbean companies.

_________________________________________________________

Building High Perfor-mance Work Systems in

Caribbean Enterprise: Harnessing the Synergy of Human Capital & ICT

Drs. Noel Cowell & Maurice McNaughton

A High performance work systems (HPWS) is as a bundle

of synergistically aligned human resource manage-

ment practices, designed to support the strategic objec-

tives of the organization. While there is clear evidence of

the positive impact of high performance work systems on

Productivity increases stem from a variety of factors, chief of

which is the use of more and better “tools” by producers;

23

ing. To be relevant, CIOs will need to leap into the future

even as their role is being rapidly redefined over the next

5 years. Join us in this presentation where we will share

with you;

1. The emergence of the Third Platform of computing

and how the digitization of industries and the inter-

net of everything is redefining the role of ICT

2. New ICT consumption models and their impact to

business and IT service delivery organizations

3. The changing role of technology management and

the CIO

4. How CIO’s can make the leap in embracing new ICT

consumption models and lead innovation in the en-

terprise.

Those CIO’s that most readily accept the new reality of

hybrid IT and reconfigure their IT leadership to manage

a radically different landscape, will find they are better

positioned for the future. In particular, they will find

themselves more able to innovate, less vulnerable to se-

curity risks and system-performance issues, and, more

responsive to the demands of their respective markets.

Join the conversation and find out how you can take the

leap!!

New ICT Consumption Models: Helping CIOs

Make the Leap Mervyn Eyre.

The emergence of the “Third Platform” of computing - the

convergence of social interaction, mobility, cloud and in-

formation – are transforming the way that people and busi-

nesses relate to technology. The adoption and consump-

tion of ICT is rapidly changing, and with it the role of the

CIO. Additionally, hyper-connectivity and the digitization

of industries have increased the pressure on organizations

to become more agile and look to ICT as a platform for in-

novation and e ffici en cy. Acco rdin g to G art ner,

“enterprises and their CIO’s need to Flip from old to new

in terms of information and technology leadership, value

leadership and people leadership”. Fundamentally, CIOs

will need to become more business savvy.

Research has shown that only 40% of today’s CIOs under-

stand the emerging third platform technologies, the ur-

gency to adopt and their future impact. This is quite worry-

Open Data & The Private Sector: Business Models &

Opportunities Dr. Maurice McNaughton

Open Data, has become a global phenomenon, propelled

by growing consensus and an irrefutable logic in the no-

tion that data created by public funds should be publicly

accessible and freely reusable, thus creating opportuni-

ties for increased economic efficiency and civic equity.

Over 40 countries at different stages of development are

pursuing Open Data policy actions and/or initiatives to

influence social, political and economic outcomes. Alt-

hough most often associated with Government policy and

initiatives, the spotlight of the Open Data narrative is be-

ginning to focus on the private sector to determine what

roles and opportunities exist for businesses to become

more actively involved in the open data value chain? The

presentation discusses the economic value potential of

open data and various business model options and oppor-

tunities for the private sector in Jamaica and the Caribbe-

an to play an active role in the emerging open data eco-

system.

25

B ig data refers to a situation

where there exists extremely

large data sets that have

grown beyond our ability to

use traditional data processing tools to

manage and analyze them. Sagiroglu

(2013) refers to big data as huge data

sets with varied and complex struc-

tures with difficulties storing, analyzing

and visualizing as well as identifying

patterns and hidden correlations. Big

data however is not just about size but

encompasses the amount of sources

from which data is available, the vari-

ous formats and the fact that most of

the data is user generated [1]. For in-

stance was revealed that the digital uni-

verse grew by 62 percent or almost

800,000 petabytes with social media

networks like twitter facilitating over

29 billion tweets [2]. YouTube made

Data Privacy & Security in the Era of Big Data

more than 70 million videos availa-

ble while Facebook has more than 1

billion active users who spend over

700 million minutes per month on

their site. It was further stated that

there were one trillion unique URLS

in Google’s index and two trillion

google searches done every day.

These statistics provide an idea of

the volumes and amount of different

sources from which data surround-

ing an individual is being generated

and the extent of the information

that can be gathered once proper

data aggregation is conducted.

Big data, in recent years, have been

characterized by high volume, ve-

locity and variety of data (referred to

as “3V”) challenges. Nonetheless,

the reduction in price of hardware

components required for processing

Abstract—since the mid-1990’s the amount of data being collected by private organizations and govern-ments has been growing at an exponential rate. In addition to the amount of data, the types of data and its sources have also increased significantly. This situation has led to the term “big data” being coined and

concerns being raised surrounding privacy and security of these data. This is so as there is a lack of effec-tive tools and approaches for securely managing large-scale data and distributed data sets. Also, the fact that these data sets contain high volumes of sensitive and personal information, persons with malicious

intentions are increasingly motivated to find new ways of gaining access so that they can exploit the infor-mation. Consequently, the aim of this paper is to analyze the effects of big data and the resulting privacy

and security concerns. The validity of these concerns will also be examined and some of the proposed solu-tions identified.

Keywords- big data; security; privacy; cloud computing;

big data and growing usage of cloud

computing concepts for storing

large volumes of data, has contribut-

ed to an increase in the number of

private businesses being able to col-

lect and analyze big data. This grow-

ing interest in collecting large vol-

umes of data, which in many situa-

tions is considered personal data,

has motivated businesses to sell this

information to third parties for a

usually high fee and unauthorized

persons to try accessing these data

by breaching security. Unfortunate-

l y , p ro g ra mm i ng mo de l s l i k e

MapReduce which are designed to

handle big data efficiently do not

protect intermediate data efficiently

26

grams [4]. These programs saw the NSA being given ac-

cess to limitless amounts of data surrounding individuals

in the interest of national and global security. It is there-

fore abundantly clear that big data collection and pro-

cessing is a growing trend for governments and private

businesses. This trend however has for several years now

raised concerns about security and the privacy of person-

al information, encouraging one to analyze the situation

as one of the contemporary issues facing the area of In-

formation Technology. This issue being properly ad-

dressed is also just as important for big data itself as if

data is not authentic, new data mined may not be con-

vincing; while if privacy is not properly addressed then

individuals may be reluctant to share information.

RELATED WORK

Non-relational data stores like NoSQL databases are be-

coming synonymous with big data processing. However,

these databases are still evolving with respect to security

and are still not considered mature. Furthermore, each

NoSQL database was built to tackle different challenges

in the analytics world hence security was never part of

and do not allow operations on ciphertexts. Also, there is

an increase in the amount of data being leaked, whether

accidentally or intentionally, thus creating a need for the

continual reassessment of current approaches to manag-

ing data leakage. Therefore, in addition to the 3V challeng-

es, managing the emerging security and privacy challeng-

es must be a priority in the evolution of big data technolo-

gies.

MOTIVATION

The evolution of technology and web 2.0 concepts have

been a major contributing factor to the ever increasing

avenues for data generation and collection. Internet based

social media like Facebook and Twitter have been rapidly

growing, serving as examples of the ever increasing

amounts of data surrounding individuals that are being

generated. Online retailers like Amazon.com who facilitate

millions of transactions per day keep large databases with

customer purchasing information to analyze customer

tastes and buying habits [3]. Also, the United States Gov-

ernment’s National Security Administration (NSA) carried

out surveillance operations through its programs such as

Wind, Prism and a wide variety of other data intensive pro-

the model at any point of its design stage [14]. [14] further

states that data and transaction logs are stored in multi-

tiered storage media. Manually moving data between tiers

allows the IT manager to directly control what data is

moved and when. The challenge is that the 3Vs that are

synonymous with big data have created the need for the

use of auto-tiering. The resulting problem however is that

auto-tiering solutions do not keep track of where the data

is stored thereby creating new challenges as it relates to

secure data storage. These two points highlighted by [14]

reinforce the argument of big data technologies being

inherently insecure.

Healthcare Privacy Concerns

Today our lives are continuously being digitized and ar-

chived at unprecedented scales. This includes GPS infor-

mation, cell phone calls, text messages, credit card pur-

chases, e-mails, online social network conversations and

even our electronic medical records are being aggregated

and shared with third parties both public and private [5].

[5] states that it is possible to use non-DNA-based barcode

which is specific enough to uncover an individual’s iden-

tity in a collection of hundreds of millions of individual

genotypic profiles acquired in a completely different con-

text. In addition, DNA barcodes could be generated from

public data sets and screened against DNA databases that

are held by the government agencies to identify an indi-

vidual involved in an unsolved crime. While there are

obvious benefits that can be derived, these situations are

seen as proof of the fact that protection of privacy in the

era of big data is increasingly becoming more limited. For

example, it is estimated that when big data is exploited in

the healthcare context, it could save the industry up to

US$450 billion. Nevertheless, the sensitive data collected

27

social media rests with the individuals themselves who

are sharing the information.

Security has gradually increased like privacy concerns

when looking at social media. Predators, hackers and

state actors search social media websites trying to find

information or people to target for exploitation [10]. Secu-

rity is even more directly compromised with the trend of

modern devices being capable of embedding geo-data

into the created content. Individuals, some unknowingly,

upload pictures with these location services enabled al-

lowing Facebook Places or Google maps to know their

location at the time each photo was taken. This empow-

ers the companies to gather information such as where

an individual has been throughout the course of the year.

This creates issues not just surrounding privacy or data

security but can have serious implications for an individ-

ual’s personal safety. Predators wishing to physically

harm the individual can use this information to identify

their possible location.

Cloud Environments for Big Data

Big data and cloud computing are two of the fastest-

raises privacy issues that must be addressed [6].

Privacy & security Issues in Public Social Media

In the context of social media there is an increase in the

awareness of the value and risk of personal data being up-

loaded to the web [7]. Particularly, social media is de-

scribed as being an online technology tool that allows peo-

ple to communicate easily, using the internet to share and

discuss information [8]. According to [7] there has been a

lot of work done in the small data area to protect privacy,

i.e. how users control access to what they themselves post.

However, when looking at big data the issues are focused

almost entirely on what the controlling companies do with

the information [7]. While [9] refers to statistics that list

Facebook as the most popular social media and states that

though there are increased privacy, security and trust con-

cerns, research on Facebook found that individuals will-

ingly shared content despite these concerns [9]. The re-

search also found that people seem to be more open in

online social networks and are more willing to share infor-

mation about themselves than in the real world. This infor-

mation provides some insight and encourages one to think

that part of the solution to privacy and security concerns in

moving technologies identified in Gartner Inc.’s 2012

Hype Cycle for emerging technologies [11]. The constantly

increasing volume and variety of data to be processed and

the need for effective management and storage of this

data have helped to fuel the trend of moving to cloud

based services [12]. The main idea behind cloud compu-

ting is to leverage virtualization technology to maximize

the usage of computing resources. This includes basic

ingredients such as storage, CPUs and network band-

width. However, [12] notes that big data storage and pro-

cessing affects security and privacy, since there is great

use of third-party services and infrastructures that are

used to house important data or perform critical opera-

tions. This causes some unease to potential users of cloud

services because cloud service providers (CSP) have full

control of the stored data. Additionally, attackers may be

able to access data stored in the cloud if there is not suffi-

cient secure mechanisms provided by CSPs [13]. Unlike

traditional security management big data security seeks

to try and find a way to process huge data sets without

exposing information of users. The challenge with big

data security is further compounded by the fact that cur-

rent technologies privacy protection methods are based

on static data sets, while big data is constantly changing,

thus making it difficult to implement effective privacy in

this complex situation. Additionally, regulatory and legal

frameworks surrounding big data privacy need to be

strengthened [12].

PROPOSED SOLUTIONS

Metadata based storage

The security of data is not just important during transfer

of the data but also during its storage. One proposed solu-

tion to ensure the security of data at this point is to en-

force a metadata based segregation approach and solu-

tions to access the segregated data. This concept is based

28

odology, reinforcing its effectiveness as a solution to se-

curing data in a cloud environment. Figure 2, explains

this fragmentation.

on the fact that any data is valuable as long as the frag-

ments of the information are related to each other. There-

fore, data should be segregated into public and sensitive

data segments (SDS). The SDS should be further fragment-

ed into smaller units until each fragment does not have

any value individually. When the fragmentation is done

then the mapping data needed to re-assemble the infor-

mation should be generated in parallel.

This solution is very useful to enforce the security of

information as it deviates from the traditional concept of

storing all related information at the same location. For

example, a membership table in a database does not have

any significance without the customer table relation and

therefore would be stored in different locations. However,

the credit card table on its own could be useful infor-

mation for an intruder so its security level would be desig-

nated as critical and so it would be greatly fragmented. The

fragmented data would then be segregated and stored in

different locations. The data would be re-assembled during

execution and transmission through the mapping data but

still remained stored fragmented and segregated. [15] pro-

vides a good description and documentation of this meth-

Figure 2. From: Subashini and Kavitha (2011)

Fully Homomorphic Encryption

Traditional encryption solutions can

protect data from being divulged,

but cannot be used to conduct com-

putations on encrypted data. There-

fore, if we use the MapReduce model

to count the number of times a word

appears in multiple documents, be-

fore inputing the Map function we

must do some operations on the

source data to get some valuable in-

formation. If the data is encrypted

then we must decrypt it before we

can do any computations on it. This

creates privacy concerns for inter-

mediate files as this is equivalent to

doing computations on plaintext.

Homomorphic encryption (HE) is

one solution to privacy and security

concerns raised when using big data

processing technologies. This type of

encryption allows us to perform op-

erations on encrypted data without

knowing the private key or decrypt-

ing the data [17]. There are two types

of homomorphic encryption solu-

tions available namely, partially and

fully homomorphic encryption

(FHE). Partially homomorphic en-

cryption (PHE) systems are defined

over a group and therefore support

just a single operation on the en-

crypted data [17]. These types of sys-

tems are usually divided based on

the operations they support on addi-

tive or multiplicative HE systems.

Therefore, the limitation of PHE sys-

tems is that it only allows homomor-

phic computation of only one opera-

tion (either addition or multiplica-

tion) on plaintexts. A major step to-

wards FHE was seen with the Boneh-

Goh-Nissim cryptosystem [21]. This

system was considered as somewhat

homomorphic but still not fully ho-

momorphic because it supports eval-

uation of unlimited amounts of addi-

tion but at most one multiplication. I

propose using a FHE system since it

is defined over a ring and therefore

supports various operations. The

main process of FHE systems sche-

mas are like Figure 1.

29

In 2009, the first fully homomorphic encryption system

was proposed by Craig Gentry in his PhD thesis [18]. The

challenge with the FHE system introduced by Gentry was

that the ciphertexts contain random noise with addition

operations doubling the noise and multiplication squares it

off. The noise becomes greater with successive homomor-

pfic operations resulting in only ciphertexts that remain

below a certain threshold being able to be decrypted. This

problem was tackled by Gentry using a bootstrapping

method. This method allows a decrypt function to reduce

the noise without decrypting data to plain text. This re-

freshed ciphertext can be used for subsequent homomor-

phic operations. In order for this system to work then the

decrypt function must remain below the threshold. This

limitation was later addressed by [19] using a framework

where noise only grows linearly with the multiplicative lev-

el instead of exponentially, therefore eliminating the need

for the bootstraping technique. Figure 1. From: Chen and Huang (2013)

from the environment protected by DLP technology. The

effect of this however is mitigated by the fact that physi-

cal security of the equipment is much easier to enforce.

International Data Privacy Laws

Proper laws must play an integral role in addressing

many of the privacy and security concerns that have aris-

en since the emergence of big data. Many laws exist cur-

rently, some of which were passed prior the big data era

and therefore need to be revisited and revised. Such revi-

sions will help in governing data collection points that

have recently come to the forefront such as social media.

In 2013 Australia announced that the Privacy and SPAM

Act will be revised in order to properly address contem-

porary concerns of individuals or provide clarification to

businesses regarding the steps that should be followed to

manage competing interests [20]. While I believe that

this example should be followed by countries worldwide,

this in itself will not resolve the issue without the devel-

opment and strengthening as well as the full support of

international data privacy laws. This is necessary as mul-

FHE has evolved significantly since the introduction of the

first design with many of its limitations being addressed,

however there is still room for more efficient homomor-

phic encryption systems in the future. This is so as the FHE

systems present today generate public keys that are too

large for efficient computations and are still too slow to be

considered practical in many big data real-world applica-

tions. Nevertheless, FHE is possibly one of the most prom-

ising solutions for addressing many of the privacy and se-

curity concerns related to big data technologies, especially

when performing computations on data in the cloud.

Data Leakage Prevention Technologies

A new approach to the prevention of data leakage has been

taken on since the mid-2000s which is effective in prevent-

ing leakage of sensitive data by insiders with malicious

intent. Data Leakage Prevention (DLP) technology involves

the inspection of packets, restricting the transmissions

within and leaving the network by using policies based on

classification and file location. Importantly, even individu-

als who are authorized to access the network will have

their attempts failed when trying to export from the net-

work or copy information to external storage devices [22].

The limitation to this technology however is that it does

not provide security against the removal of computers

30

violated, is to help them realize that they can play one of

the most important roles in safeguarding their privacy.

FUTURE WORK

Future efforts will be focused on finding a FHE system

that can overcome its current limitations and is more

efficient in addressing big data privacy and security con-

cerns. This is because I believe that FHE is both one of

the most promising and exciting security systems that

can be developed to prevent the big data movement from

being slowed by concerns surrounding security and pri-

vacy.

CONCLUSION

Big data is seen as one of the most important concepts in

information technology to emerge in the past few years.

It has many positive implications for a wide cross-section

of areas to include business intelligence and crime

fighting. Unfortunately, there are also strong concerns

regarding privacy and limitations in security. Neverthe-

less, based on current trends it is obvious that big data

will continue play a vital role in the development of the

technological landscape. Consequently, there must be

sustained efforts to address the concerns outlined in this

paper, using the proposed solutions as the main strate-

gies, in order to ensure that the rate of diffusion of big

data concepts continues to move and evolve at an expo-

nential rate.

tinational companies will often will abide by domestic laws

but are not compelled to recognize data privacy laws in

foreign countries.

Public Awareness

The fact that a large portion of the data which individuals

are concerned about being exposed or misused is generat-

ed by the users themselves, then it is reasonable to share

the view that they can play a vital role in limiting the

amount of data that can be exploited. This means that us-

ers must resist the urge to share more information across

these networks that collect or store information, especially

where it is considered confidential, than is absolutely nec-

essary. If one takes a look at criminal activity in all aspects

it can be noticed that as security mechanisms improve to

prevent unauthorized access, predators also evolve in their

efforts to find more sophisticated ways of gaining unau-

thorized access. Therefore, the surest way to assist individ-

uals in maintaining privacy of personal information is to

educate them about the types of companies that collect

and share information with third parties, devices that are

intentionally or unintentionally designed to share person-

al information with these companies, the possibility of in-

formation stored in cloud systems being accessed by un-

authorized persons or any other system using big data con-

cepts that can cause vulnerabilities in relation to security

and privacy. This strategy of making the public aware as

best as possibly of how and when their data privacy can be

References

1. S. Sagiroglu, “Big Data: A Review,” IEEE Collaboration technologies and systems (CTS) international conference, 2013,

pp.42 – 47

2. T. Craig and M. Ludloff, Privacy and Big Data, 2011, pp.2–4.

3. (2013) The Teleread Website. Available:

4. http://www.teleread.com/ebooks/amazon-mines-data-to-find-customer-tastes/

5. (2013) Alleanzaerinternet Website. Availabe:

6. http://www.alleanzaperinternet.it/wpcontent/uploads/2013/06/guardian.pdf

7. E. Schadt, The Changing Privacy Landscape in the Era of Big Data, 2012

8. R. Lu, H. Zhu, X. Liu, J. Liu and J. Shao, “Toward efficient and privacy preserving computing in big data era,” IEEE Network.

July/August 2014, pp.46-50 .

9. M. Smith, C.Szongott, B. Henne and G. von Voigt, “Big data privacy issues in public social media,” 6th IEEE International

Conference on Digital Ecosystems Technologies (DEST), 2012.

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10. B&C, “What’s this stuff called “social media,” Benefits & Compensation Digest, Vol. 47, no. 3, p.10, March 2010.

11. N. Almadhoun, P. Dominic and L. Woon, “Percieved security, privacy and trust concerns within social networking sites,” IEEE

International Conference on Control System, computing and Engineering, 2011.

12. F. Ansari, M. Akhlaq and A. Rauf, “Social networks and web security: implications on open source intelligence,” 2nd IEEE

National Conference on Information Assurance, 2013.

13. (2012) Gartner.com. Available:

14. http://www.gartner.com/newsroom/id/2124315

15. C. Ji, Y. Li, W. Qui, U. Awada and K. Li, “Big data processing in cloud computing environments,” IEEE international Sympo-

sium on Pervasive

16. X Huang and X. Du, “Achieving big data privacy via hybrid cloud”, IEEE INFOCOM Workshop on Security and Privacy in

Big Data, 2014

17. Electronic Publication: “Top Ten Big Data Security and Privacy Challenges,” Cloud Security Alliance, 2012

18. Subashini and Kavitha, “A metadata based storage model for securing data in cloud environment,” International Conference

on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2011

19. X. Chen and Q. Huang, “The data protection of MapReduce using homomorphic encryption,” IEEE, 2103

20. D. Hrekstak and S. Picek, “Homomorphic encryption in the cloud,” MIPRO, Opatija, Croatia, 2014

21. C. Gentry, “A fully homomorphic encryption scheme,” Ph.D. dissertation , Stanford, CA, USA, 2009

22. Z. Brakerski, and V. Vaikuntanathan, “Efficient fully homomorphic encryption from (standard) 1we,” in FOCS, 2011, pp. 97 –

106.

23. R. Saadati and A. Christie, “ Big data big issues? Is Australian law keeping up,” DLA PIPER, 2014

24. D. Boneh, E-J. Goh and K. Nissim, “Evaluating 2-dnf formulas on cipherexts,” In Proceedings of Second International Confer-

ence on Theory of Cryptography, ser. TCC’05. Berlin, Heidelberg: Springer-Verlag, 2005, pp.325-341.

25. N. Weinberg, “Data leakage prevention technologies,” Hot Technology for 2008. NETWORKWORLD. Available:

26. http://www.net-workworld.com/research/2008/011408-8-techs-data-leakage.html.

33

F orms of Government have

been around for as long as

mankind has recorded its his-

tory. The issue of determining

the core principles and proper func-

tions of government has been actively

debated all over the world for many

years. In our local context government

has been engaged in managing, among

other areas, Crime, Healthcare, Educa-

tion and the Economy. Many citizens

will agree that these areas are of the

utmost with regard to their importance.

Government’s even though not profit

centric can learn valuable lesson from

the successfully businesses of the

world. Successful businesses have lev-

eraged technology to increase profit

and competiveness. In like manner

Government can leverage technology

to increase efficiency and provide valu-

Processing & Leveraging Big Data in Government

able service to its citizens. Govern-

ment has over time amassed large

volumes of information that serve as

unused and unmanaged reposito-

ries of data. The advance of technol-

ogy over recent years has seen in-

formation technology becoming

increasingly pervasive. Government

will need to be proactive in how it

responds to technology and will also

have to find ways of ensuring that it

fully leverages technology to meet

the needs of its citizens and the

country as a whole. The storehouse

of information that the government

has at its disposal, with the aid of

technology, should be thoroughly

exploited, taking into consideration

the rights and privacy of its citizens.

In the past government data would

be limited to a relational database or

Abstract—Government is constantly using data to improve governance and the life of its citizens. Technological ad-vances have allowed under-resourced countries to access improvement to help to manage and deliver government

services. A technology that has rightly received much attention is Big Data. The term big data has become so common, it challenges clear definition. A standard view is that big data is difficult data: difficult to store in traditional data-

bases, difficult to process on standard servers, and difficult to analyze with typical applications. Even "smaller" data can manifest a complexity that requires a new approach. There is a real need to identify tools and techniques to man-age Big data efficiently and extract from it real value. The government houses large volumes of data and is poised to harness it for the benefit of the country. In order to leverage the benefits of this data, governments must overcome the hurdles associated with Big Data. These hurdles in the form of Volume, Variety, Velocity and Veracity are not insur-

mountable, but will require purposeful attention if we are to succeed.

Keywords-Government, Privacy, Big Data

a spreadsheet. In this form the type

data that can be stored is limited and

the structure of the data is rigid, not

allowing for unstructured data. The

proliferation social media and other

forms of digital data has brought

into focus the need capture, analyse

and interpret these types of data.

The Term Big Data was born out of

this need. Big Data speaks to data

that is voluminous, varies in type

and retrieved at a fast rate. All of

these characteristics match the data

that government now possesses.

MOTIVATION

Jamaica is a country that has many

resource constraints. This is espe-

34

rather becoming multi-form, multi-source, and multi-

scale [3]. The sheer number of bytes that is generated

daily is mind-boggling! According to an article published

by IBM, every day, we create 2.5 quintillion bytes of data.

It is interesting to note that 90% of the data in the world

today has been created in the last two years alone. This

data comes from everywhere e.g. sensors used to gather

climate information, posts to social media sites, digital

cially true as it relates to our finances. The cost of technol-

ogy in the past could easily justify the lack of its utilization

in our country. Today, we are bombarded with technology

that is not only comparatively cheaper but in many cases

more efficient. We have the opportunity, as a country and

by extension the Government, to grasp the benefits that

these new technologies provide. That being said, this paper

seeks to answer the questions of how the government can,

effectively collect and manage Big Data and use Big Data to

operate more effectively whilst ensuring that the privacy

and rights of its citizens are adequately protected.

THE WORLD OF BIG DATA

It is useful when looking Big Data to truly understand what

it is and how it is characterized. This section seeks to more

intimately expose Big Data to help us to understand its

many nuances. Technology is all around us and is being

used even by babies. Smart devices coupled with the popu-

larity of social networking continue to generate colossal

amounts of data, both in its structured and unstructured

forms. The figure above gives a simplified view of the dif-

ferent types of data that is available today, whether it is

text, audio, video, or other data forms [2]. We hear the

term Data that sometimes may seem abstract, but we inter-

act with it without even considering. It is said that data is

pictures and videos, purchase transaction records, smart

devices and cell phone GPS signals to name a few. [5].

Experts agree that the world is in a digital explosion era

[4,6]. This explosion is referred to as big data. “Big data”

refers to an aggregation of datasets whose volume is be-

yond typical database software’s ability to capture, store,

manage, and analyze [7].

BIG CHARACTERISTICS

Big Data has been, over previous years, characterized by

the three ‘V’s of big data, Volume, Velocity and Variety.

Owing to its proliferation, a fourth ‘V’ has been added, Ve-

racity. The section explains these four characteristics.

Volume

Volume refers to the enormous amount of data and the

form of data. It used to be mainly employees created data.

Now that data is generated by machines, networks and

human interaction on systems like social media the vol-

ume of data to be analyzed is massive and continues to

grow [8].

Velocity

Velocity refers to the rate at which the data are collected

and analyzed. Big Data Velocity deals with the pace at

which data flows in from sources like business processes,

machines, networks and human interaction with things

like social media sites, mobile devices, etc. The flow of

data is massive and continuous. This real-time data can

help researchers and businesses and Governments make

valuable decisions relating to their particular strategic

objectives. Inderpal Bhandari, a well-respected mind the

field of Big Data, suggests that sampling data can help

deal with issues like volume and velocity [8].

Figure 1. Sources of Big Data

35

Data Integration

Data integration seeks to answer mainly two questions.

Firstly, how will data be collected and organised? Second-

ly, how will Privacy, Confidentiality, and Ethical concerns

be addressed?

The integration of data will require an integration frame-

work agile enough to handle big data regardless of wheth-

er it originates in the enterprise or across the Internet.

This big data integration framework must be able to work

across different architectures and data technologies,

while at the same time move data seamlessly between

relational and non-relational structures. Data integration

must be adaptable allowing for multiple streams of data,

and it must be able to harvest data from transactional sys-

tems and business applications in enterprise data ware-

houses.

The privacy and confidentiality of data has become in-

creasing important as individuals interact with a growing

number of systems. The data that is being collected from

these individuals continues to grow. Government often

seen as the largest repository of citizen data is forced,

especially with the advent of Big Data, to address the is-

sue relating to privacy of citizen information. In the con-

text of Big Data, the Government would have an interest

in knowing citizen who live a particular lifestyle are like-

ly to have a particular disease. The government also has

Variety

Variety refers to the increasing types of data that can be

collected. Variety speaks to the many sources and types of

data both structured and unstructured. Traditionally we

stored data from sources like spreadsheets and databases.

Now data is manifested in the form of emails, photos, vide-

os, monitoring devices, PDFs, audio, etc. This variety of

unstructured data creates problems for storage, mining

and analyzing data [8]. These problems, though outside the

scope of this paper, are addressed by the now very popular

NoSQL database.

Veracity

Data veracity speaks to how validity of Data. It ask the ques-

tion, is the data correct and accurate for the intended use?

In the context of making correct decision valid Data is

most important [8]. Government’s use of Big Data is ex-

pected to lean heavily on this characteristic as it will use

data to inform policy; therefore affecting all citizens.

There is no doubt that our world today is driven by many

processes. Whether preparing for an exam or moving from

point A to Point B, we consciously or unconsciously plan

how we will succeed at a particular task. When we consid-

er leveraging the power of Big Data, a feasible process is

integral to our success. Outlined in this section are the

steps that should be taken by Government to capitalize on

this powerful phenomenon called Big Data.

the responsibility, based on the discovery above, to protect the privacy of the individuals, ensuring that John Doe’s data

when linked does implicate John Doe.

Similar to our physical lives, we leave behind trails of information about ourselves in our digital communications and inter-

actions. In Figure 2 above it is easy to see that the data collected can be used to sketch a good profile of an individual. It is

said that in every interaction, we leave behind a huge trail of data that includes bits and pieces and pointers to our real be-

haviors [15]. It is accurately recognized that the collection of information from digital transactions and interactions is some-

thing that cannot be stopped. We inadvertently leave a digital trail behind in the digital world and this trail is many time

substantial. An issue arises when these digital trail are analyzed and compromises an individual’s anonymity of the World

Wide Web (WEB).

It is has been said that the conveniences of access to information and services through the varied devices, also creates new

ways of watching us more closely than ever before. It is also pointed out that as companies customarily collect billions of

details about nearly every connected individual, the world will reach a state where people will lose control of their privacy

and identities momentarily [16].

36

Government is always a step behind and like many other

entities working with Big Data they may not even be sure

of what you are looking for. The Government has vol-

umes of data that they, in context, will want to get valua-

ble insight from. In Big Data analysis it common for pat-

terns to emerge from that data before there is an under-

standing of why the said patterns are there.

If we look loosely at the functions of Government, pro-

tecting, empowering and serving its citizens, we should

have a clear idea of what they should be interested in. For

instance, are you interested in predicting penetration of

none communicable diseases to improve wellness? Do

you want to analyze the driving patterns of Drivers to re-

duce road casualties? The nature of the high-level prob-

lem will dictate the analytics will employed. In analytics,

an entity may consider a range of possible kinds, which

are briefly outlined in the table 1 .

In order to ensure that privacy is protected when data is

linked a system has to be put in place. One such system is

the Secure Decoupled Linkage (SDLink). This system allow

analyst to link and analyse data without seeing the identifi-

cation details. SDLink is a computerized third-party linkage

system that offers safe and high-quality data integration by

using a hybrid human-machine system based on three fun-

damental privacy principles [17]. The first principle decou-

ples the identifying data from the sensitive data via encryp-

tion. The second principle adds fake data to the dataset, this

process is called chaffing. The third principle changes the

dataset label this process is called universe manipulation.

In universe manipulation SDLink prevents the attribute

inference that can occur in group disclosure. In explaining

the process, if an analyst knows someone on the HIV regis-

try at the Ministry of Health (group disclosure), that indi-

vidual must have HIV (attribute disclosure), but this disclo-

sure can be prevented it is established that the list has fake

data i.e. people who do not have HIV are also on the list or

the analyst may not know if the list is a HIV registry. It is

important to note that only the information that is essential

for record linkage is revealed during the linkage process.

This paper concedes that more in-depth research is needed

to fully explore this relatively new area, and this will in turn

produce improved protocol to allow for identity protection.

DATA ANALYSIS

The first question that one needs to ask oneself before em-

barking on big data analysis is, what problem are you try-

ing to solve? It is well documented that many times the

Figure 2. Digital Behaviour & Privacy

Table 1: Type of Analysis

37

been forced to unfairly choose be-

tween data size and latency (time

taken to process) and Hadoop and

other MapReduce tools offer not

enough comfort [20]. There are,

however, new tools are being devel-

oped that will eliminate the need to

choose between size and speed of

queries. Google has published sever-

al papers on new big data technolo-

gies it is in the process of imple-

menting. One of th ese, called

“Dremel,” can analyze trillions of

lines of data in a matter of seconds.

Wh ile MapRe duce es s entially

breaks a problem apart, distribute it

and combine the results, Dremel

enables data scientists and analyst

There is a real challenge with re-

gards to analysis as many of the

technologies that now exist are not

fully capable of meeting the require-

ments many analysts. There is the

reasonable expectation that business

intelligence (BI) programs will fill

the data gap, but this has not been

realized. Tools that were introduced

early in the Big Data revolution like,

Hadoop and other MapReduce based

tools have also not lived up to the

kind of functionality that analysts

need. MapReduce, fundamentally, is

designed for organized data pro-

cessing, or workflows. This becomes

a challenge for analysts looking for a

quick response. These analysts have

alike to query and analyze datasets in

near real time. It is stated that

Dremel can “run a query on a

petabyte of data in about three sec-

onds” [20]. This will allow analysts to

conduct ad hoc, slice and dice in-

quiry more common to business in-

telligence in just a matter seconds.

Big data expert Tim Gasper has noted

“Drill, Dremel’s open source coun-

terpart, and Dremel put power in the

hands of business analysts, and not

just data engineers” [20]. These tools

will enable government analyst to

truly make practical sense of the data

available.

LEVERAGING BIG DATA IN THE

ROLE OF GOVERNMENT

How can Government use Big data

to be more effective

The areas of Crime, Healthcare, Ed-

ucation and the Economy have been

on the mind of many Jamaicans over

the years. Technolgy has enabled

many countries including Jamaica

to access the same resources availa-

ble to developed counties of this

world. Big data is one such technolo-

gy that we should exploit as a nation.

Accoring to Jamaica’s Vision 2030,

technolgy is strategically placed as

an enabler to all industries. It is

therefore befitting that this section

outlines the afore meantioned areas

of concern and how Big Data can be

used to help us to reach our goal

according to our vision.

Crime

In Jamaica one does not have to go

very far to see the effects of crime

and criminality. The categorization,

treatment and perception of crime

is many time blurred and highly

subjective. The average citizen of-

ten does not see criminal acts like

tax evasion and “white-collar crime”

as serious, and it can be debated

whether or not the Government has

the same view.

Crime prediction and prevention

challenges today are becoming in-

creasingly complex and require a

new approach to the management of

information. The growing variety,

velocity and volumes of data will

increasingly require agencies to

anticipate and pre-empt emerging

trends in criminal activity if they are

to maintain, let alone increase ac-

ceptable levels of response. To more

effectively predict and prevent

crime, government organizations

must be capable of managing nu-

merous data types and use ad-

vanced, real-time analytics designed

to transform data into insights.

Crime solving challenges today are

also becoming increasingly complex

and require a new approach to the

management of information. It is

well known that security forces and

their supporting agencies have

amassed large volumes of data over

the years. This volume of data fits

well into the blueprint of Big Data.

38

The problem, however, is that data is disaggregated and unlinked for the most part. The crime fighting effort would be

greatly aided by safely consolidation this data to allow for pre-empting and quick resolution of criminal activity.

In taking a closer look at actual developments relating to big data and Crime, I invite you to look at the real life cases be-

low:

Case Question 1: How can I predict where crime is going to happen?

Target Result: Recognize crime trends as they are happen-ing

Entity: Memphis Police Department

Implementation: Predictive model uses numerous variables to predict locations of criminal activity

Effect:

30% reduction in serious crime overall

15% reduction in violent crime

Gives precinct commanders ability to change tactics and redirect patrol resources to catch more criminals in the act

Case Question 2: How do I connect data about related crimes and criminals across jurisdictions?

Target Result: Share local, state, and national law enforce-ment information

Entity: US Federal Bureau of Investigation

Implementation: Federally-hosted N-DEx national repository of criminal justice records

Effect:

18,000 law enforcement agency participants

Each agency controls sharing based on legal, jurisdictional, & privacy requirements

N-DEx proactively notifies specific users if certain relation-ships are discovered

Healthcare

As the saying goes, “health is wealth”. Countries are in-

creasingly realizing that a healthy nation is a prosperous

and productive nation. These countries are therefore in-

vesting in their healthcare systems []. Many countries

have adopted the mandated adoption of electronic health

records (EHRs) that allows healthcare professionals to get

centralized access to patient records. Generally, in the

past, the healthcare industry has been cautious to about

big data, but is rapidly changing. The Government stands

to gain not only money saved from more efficient use of

information, but also new research and treatments. The

possibilities show huge potential for Jamaica considering

our always improving research relating to medicines de-

rived from our vast portfolio of medicinal herds.

The advent of data from wireless, wearable devices is a

perfect example of how Big Data will change healthcare.

These devices allow medical practitioners collect vast

amounts of data, such as Blood pressure and heart rate,

which can be further analysed in full to pick up trends and

areas of for improvement. Social media data is an un-

tapped source of health data. This data can be analysed to

see what people post which in turn can help to fight insur-

ance fraud and improve customer service. Having ob-

served the impact of big data and analytics on other mar-

kets the government should be encouraged to know that

healthcare analytics in other countries are already show-

ing impressive results [11].

When harnessing data resources in healthcare it is im-

portant to note that data is growing and moving faster

than healthcare organizations can consume it; 80% of

medical data is unstructured and is clinically relevant.

This data resides in multiple places like individual elec-

tronic medical records, lab and imaging systems, physi-

cian notes, medical correspondence, claims, Customer

Relationship Management (CRM) systems and finance.

Getting access to this valuable data and factoring it into

clinical and advanced analytics is critical to improving

care and outcomes, incentivizing the right behavior and

driving efficiencies.

Healthcare organizations are leveraging big data technol-

ogy to capture all of the information about a patient to get

a more complete view for insight into care coordination

39

The 2013 World Innovation Summit for Education

(WISE) held in Qatar with representatives from over 100

countries, including Jamaica, started with a unanimous

resolve, a plea for radical educational change. Most of the

leading educators, policy makers, and governments

claimed that the way learning happens today will not ade-

quately prepare young people for the world of tomorrow.

At this conference Big Data was explored as a possible

light in the darken world of education. Mr. John Fallon

the CEO of Pearson PLC presented at the conference the

idea that “big data, and the disruptions it can lead to,

have led to one of the most creative periods in history in

terms of innovation.” He further went on to say that it

was “Time to harness that in education” [10].

The questions at the beginning of this section are an-

swerable if Government puts in place the necessary ar-

chitecture to harness the existing data and capitalize on

the now cheaper technological solutions that are readily,

sometimes freely, available.

Economy

It is public information that our Economy has been ailing

for some time. The variables involved in determining the

state of an economy are as varied as the variety charac-

teristic of Big Data. The before mentioned, Crime,

Healthcare and Education have a direct impact on any

economy. There are, however, some key areas that our

country must pay attention to. These areas are outlined

below.

Jamaica loses a great deal of money through fraud, waste

and abuse of unpaid taxes. There is constant pressure to

collect more revenue, reduce operational costs and im-

prove collections. The goal is to detect and prevent fraud

and abuse. An implemented Big Data platform will help to

more accurately determine fraudulent and suspicious

activity, for example denied refunds by detecting new

deception tactics, uncovering multiple identities and

identifying suspicious behavior. The government will

benefit by minimizing the tax gap, proactively detecting

and deterring fraud and abuse, reducing analysis time,

improving efficiency and saving taxpayer dollars.

In considering social programme fraud, waste and errors

government human services bodies face constant pres-

sure to increase efficiency, reduce operational costs per

and outcomes-based reimbursement models, population

health management, and patient engagement and out-

reach. Successfully harnessing big data unleashes the po-

tential to achieve the three critical objectives for

healthcare transformation, i.e. build sustainable

healthcare systems, and collaborate to improve care and

outcomes, Increase access to healthcare [12]. In taking a

closer look at actual developments relating to Big data and

Health, I invite you to look at the real life case below:

Education

This section is started by asking the following questions:

What sequence of topics is most effective for a specific

student?

Which student actions are associated with better

learning and higher grades?

Which action indicates satisfaction and engagement?

What features of an online learning environment lead

to better learning?

When are students ready to move on the next topic?

When is student at risk for not completing a course?

What grade is student likely to receive without inter-

vention?

Should student be referred to counselor for help?

What are the Benefits of Big Data to the E-Learning

Industry?

Case Question 2: How do I Reduce Fraudulent Claims?

Target Result: Reduce Fraudulent Claims

Entity: U.S. State Health

Effect:

Identified fraud among millions of claims, by finding ob-scure connections among doctors, pharmacists, lab and medical supply companies

Identified more than US $200m in questionable claims resulting in 22 criminal convictions and US $49m in recov-ered funds

40

CONCLUSION

Big data in the government of Jamaica is like a volcano on

the verge of erupting. It has the potential to inform data

driven decision making at many levels of government. To

realize this potential, the necessary infrastructure is re-

quired to give data practitioners the ability to develop the

necessary tools for integrating, analyzing and interpret-

ing data. The fears associated with big data, owing mainly

to the fact that it is relative new, should be as an oppor-

tunity to improve an already beneficial technology. Suc-

cessfully exploiting the value in big data will require ex-

perimentation and exploration. Government will need to

take big data more seriously and put in place data strate-

gies to create new waves of productivity growth. The is-

sue of trust in how information is used, shared, archived,

and managed is critical in this complex and highly fluid

environment [18].

Our Government will need to pay more attention to

addressing policies that are related to privacy and securi-

ty needs in today’s digital world. Big Data is here to stay;

the real question is what will we do with it?

ACKNOWLEDGEMENT

Thanks are due to our lecturer Mr. Kedrian James and by

extension the University of Technology, Jamaica, for

providing an incubator and platform ideas. I have been

challenged and motivated by this topic and now see more

clearly that we have a contribution to make in helping

this country to improve through technology.

case, ensure eligible citizens receive benefits and maintain

program integrity. In Jamaica the Programme of Advance-

ment Through Health and Education (PATH) is one such

programme that can benefit from a Big Data revamp. The

goal of this approach is to detect and prevent fraud and

abuse before payments are made the beneficiaries. Big

data analytics can help present, a much clearer citizen-

centric picture, uncover invisible connections and provide

real-time information-sharing for the analyst, social work-

er or government executive. The benefits to the govern-

ment are, reduced social benefit overpayments, proactive-

ly detected & deterred fraud and abuse, reduced analysis

time and improved efficiency, improved program integrity

and preservation of limited budgets for eligible citizens [9].

In taking a closer look at actual developments relating to

Big data and the Economy, I invite you to look at this real

life case :

Case Question 2: How do I predict who is likely to pay their taxes?

Target Result: Increase revenue with Effective cross-sell & up-sell

Implementation: Uses predictive modeling that gathers information from tax assessments, train ticketing systems, TV licenses, police records and more to predict whether a per-son will reliably pay taxes

Entity: European Tax Collection Agency

Effect:

18% reduction in collections workload

70% risk prediction accuracy

References

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3. J. Liebowitz. (2013). “Big Data and Business Analytics”. Verlag: Auerbach Publications. http://dx.doi.org/10.1201/

b14700

4. IBM. (2010). What is big data? Retrieved from http://www-01.ibm.com/software/data/bigdata/what-is-big-

data.html

5. R. Smolan and J. Erwitt (2012). “The Human Face of Big Data”. Sausalito, Calif.

41

6. J. Manyika, M. Chui, B. Brown, J, Bughin,, R. Dobbs, C.Roxburgh, & A. Byers. (2011). Big data: The next frontier for

innovation, competition, and productivity. McKinsey Global Institute. Retrieved from http://www.mckinsey.com/~/

media/McKinsey/dotcom/Insights and pubs/MGI/Research/Technology and Innovation/Big Data/

MGI_big_data_full_report.ashx

7. K. Normandeau. (2013) “Beyond Volume, Variety and Velocity is the Issue of Big Data Veracity” Retrieved from:

http://inside-bigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-big-data-veracity/

8. IBM Big Data in Action “Produce Actionable Information with Big Data Analytics for Government” Retrieved from:

http://www-01.ibm.com/software/data/bigdata/industry-government.html

9. A. Doyle “It is a Capital mistake to theorize before one has data”. Retrieved from: http://www.opencolleges.edu.au/

informed/features/big-data-big-potential-or-big-mistake/#ixzz39YtK1Xlj

10. A. Diana. “Healthcare Dives into Big Data” http://www.informationweek.com/healthcare/analytics/healthcare-dives-

into-big-data/d/d-id/1251138

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healthcare.html

12. Oracle Corp. “Integrate for insight”. http://www.oracle.com/us/technologies/big-data/big-data-strategy-guide-

1536569.pdf

13. M. Smith. “Big Data Broken without integration”. http://marksmith.ventanaresearch.com/2013/02/22/big-data-is-

broken-without-integration/

14. A. Al-Khouri1. “Privacy in the Age of Big Data: Exploring the Role of Modern Identity Management Systems” World

Journal of Social Science Vol. 1, No. 1; 2014

15. R. O'Harrow. (2006). “No Place to Hide”. New York: Free Press.

16. H.-C. Kum et al., “Privacy Preserving Interactive Record Linkage,” to appear in J. Am. Informatics Assoc., 2014, doi:

10.1136/amiajn/-2013-00216/

17. J. Gantz, and D. Reinsel. (2011). “Extracting Value from Chaos. IDC”. Retrieved from http://www.emc.com/collateral/

analyst-reports/idc-extracting-value-from-chaos-ar.pdf

18. D. Mohapatra, (2014). “ Big Data and Analytics in Government” IBM Smarter Government Summi 2014

19. T. Gasper, “Big Data Big Data Right Now: Five Trendy Open Source Technologies,” Tech Crunch, October 27, 2012,

20. http://techcrunch.com/2012/10/27/big-data-right-now-five-trendy-open-source-technologies/

43

T he successes of business-

es of late are arguably

been driven ‘data’ a term

that is considered by

some parties to be meaningless or

of little value. However it must be

noted that data is critical to the suc-

cess of business operations or for

any organization or institution that

relies on it to carry out its opera-

tions. How data is collected, manip-

ulated and monitored should be

held in high esteem by businesses,

since they are heavily reliant on

these data to make decisions which

are critical to the success of their

operations. In the past, when the

term data is mentioned within an

organization the structured data

stored in relational databases will

definitely come to mind. As this was

Maintaining the privacy of customers’ data within

the field of business

the established norm then to store

and manipulate data. However in

recent years the need to store and

u s e u n s t r u c t u r e d a n d s e m i -

structured data along with struc-

tured data stored in these relational

databases has increased. This has

led to the rise of the current buzz

term ‘big data’. Big data has un-

doubtedly brought many advantages

to the operations and success of

businesses; however it has it fair set

of challenges. It must be noted that

once proper mechanisms are put in

place this feat can be achieved.

Description of big data

Big data is defined as a huge amount

of data that is considered to have

high velocity, complex and variable

data which utilizes advanced tech-

Abstract – Big data is interestingly a growing phenomenon. Many businesses of late have gravitated to this buzz term; however there are some who are still apprehensive about such a move. There is no doubt that big data changes the way how business is conducted. To some it brings joy but equally it has brought peril to others. The privacy of cus-tomers’ data in the field of business is an area of grave concern where big data is concerned. Customers are now

forced to grapple with the effects of being judged by what data is obtained about them not only from one source, but from multiple sources. Information from the web, social media, text messages, emails, to name a few are now been

used by businesses to make choices about their customers. It must be noted though that even though privacy is a con-cern it must not be used as the reason not to use big data. An appropriate governance program can be used to effec-tively curtail the privacy issues zooming around big data at the moment. This study assessed generally how big data

invades the privacy of customers and a data governance framework was recommended to deal with the issue.

niques and technologies to capture,

store, distribute, manage and ana-

lyze information [19]. Evidently

when the term big data is men-

tioned the following words come

into play: volume, velocity and vari-

ety [3]. Volume refers to the huge

amounts of data been stored, veloci-

ty the speed at which data is created

and variety the various sources from

which the data is gleaned or ac-

quired. These sources include mes-

sage updates and images on social

networks, readings from sensors,

GPS signals from cell phones to

name a few [3]. The requirements of

big data have exceeded the capabili-

ties of structured databases. Busi-

44

ganizations are more inclined to keep data for longer pe-

riod because of what can be extracted from it [11]. Finally,

there are regulations in place which stipulate that data

should be kept for an extended period. There are cases

where data can be kept indefinitely [11]. Big data is used

by many social media sites such as Linkedin, facebook

and twitter. Big data accounts for a lot of storage space in

various organizations. Actually when unstructured data

is retained in user generated images, video or music files

and word processing, spreadsheet and presentation for-

mats it results in a data glut in the organization. This ac-

counts for approximately 60% of the organization’s big

data [10]. Big data actually grows at a fast pace [6].

McAfee and Bryonjolfsson (2012) mentioned in their

study that approximately 2.5 exabytes of data are created

since 2012 [3]. To date many companies struggle with

why big data exists what its true value is [9]. One has to

evaluate the value of their data and derive how much to

invest in storage technologies to avoid overspending [9].

nesses of late have gravitated to sources of data other than

relational databases in order to achieve faster business

processes and ascertain answers to questions which were

once beyond their reach to answer [18]. According to

McAfee and Bryonjolfsson (2012) structured databases are

not capable of storing and processing big data [3]. Big data

is growing phenomenon in recent years. A lot of scholarly

work has been done on this area and it accounts for almost

12,000 hits on Google scholar [2]. Big data is used in a vari-

ety of fields such as telecommunications, health care, en-

gineering, and finance. It is by and large used where the

need arises to store, process and analyze large volumes of

data using new technologies [2]. The recent growth in big

data can be attributed to three main factors. First and fore-

most organizations are now better able to keep data in

large volumes for an extended time due the availability of

better, faster and more cost effective storage [11]. Also, in

recent times there has been an increase in interest in the

area of data analytics and the benefits of data mining. Or-

Data quality and Privacy challenges with big data

The question is often asked if more data means better data

[2]. Of a fact more data does not automatically result in

better decisions products and services [2]. It must be not-

ed that more data can result in an increase in data gar-

bage. Maintaining acceptable data quality has proven to

be a challenge [12]. However it can also prove to be a cost-

ly venture as users will most likely lose trust in the data

even if a small fraction of it is found to be faulty [12]. Or-

ganizations are aware that once data quality is accurate

the return on investments will be worthwhile [11]. The

task to manage data which come from social networks,

mobile apps and CRM systems can be a complex one. An

increase in the volume of data poses challenges for archiv-

ing, retrieving and analyzing. Abed (2011) mentioned that

when low quality data is used in ERP systems it has ad-

verse effects on the operations. It results in failure of

transactions, processes and projects [4]. Abed (2011) also

indicated that data governance or data management are

normally given the task to maintain good data quality.

Hence it is critical that these be instituted in the relevant

businesses or organizations to ensure effective running of

operations. Another major challenge with big data is how

to find out if the data obtained is an outlier [5]. As the vol-

ume of data increases so does the ambiguity of outliers

[5]. Dinu and Iovan (2014) also mentioned that an organi-

zation is exposed to greater risks when more data is con-

sumed [18]. When automated and semi-automated deci-

sion making is required to take place in a highly integrat-

ed data supply chain, the process is heavily reliant on

high quality data [10]. It must be noted that liabilities can

be incurred for damages caused by inaccurate data [10].

Provision should be made for such occurrences if the

consequences are deemed catastrophic [10]. Issues with

data quality usually come into play when businesses lo-

cated worldwide are required to collaborate with each

other and there is some form of deviation in data stand-

ards [12]. Usually these parties will be required to estab-

lish similar standards to maintain data quality [12]. Some-

times maintaining the quality of data is ignored to get

data quicker, however this practice is not encouraged [12].

In a survey conducted recently, data obtained from cus-

45

setting of premiums, rates, etc.. There are even cases

where big data is used by brokers to obtain employment

information about 190 million individuals inclusive of

salary information. The information obtained was then

sold to debt collectors, financial institutions and other

establishments [19]. Also, big data makes it possible for

marketers to effectively advertise the right product to the

right customer at the right time [19]. However this poses

a challenge where the privacy of these customers is con-

cerned [19]. Target also used big data to ascertain if cus-

tomers are pregnant and send offers to them about ma-

ternity products [19]. Whilst this might be perceived by

some individuals to be ingenious it is actually violation of

the privacy of the parties involved, considering that the

information was not directly given but derived from per-

sonal data accumulated. Of fact customers at times are

not aware where their information will end up when they

consent to stipulated privacy agreements [19]. Also when

data is combined from several sources additional inter-

pretations can be arrived at about the individual [19]. At

times data collected about the individual deemed as

harmless might reveal sensitive information such as

health, financial, status, sexual orientation, etc. when

these pieces of data are combined [19]. The extent to

which these data can be used, disseminated and retained

is pretty much not standardized, hence there is some

level of ambiguity if lawsuits can come into play about

tomers was the area that gave the most problems, primari-

ly because customer data usually involves infrequent up-

dating and updating of incorrect information [2]. Data ob-

tained from a controlled setting are normally of a better

quality [12]

Privacy is an area of grave concern where big data con-

cerned [2]. Mobile devices and cloud computing platforms

have contributed significantly to security issues with big

data [19]. The use of data from social media and sensors to

arrive at business decisions is an issue that is echoed by

many of late [5]. Personal data such as: name, address, age,

email address and telephone numbers are gleaned from a

wide variety of sources [2], [5]. Often times decisions are

not made in favour of the parties from which they are ac-

quired [5]. In addition location based data amongst other

things are used to identify and track individuals easily [2].

Regulations have been put in place to some extent to cur-

tail these issues, however these regulations vary according

to the site on which they are posted or the area in which

the parties involved resides in [5]. Big data companies

gather data on you , put them together and use it to get an

understanding of who you are, where you live, friends you

associate with, products you may be inclined to purchase,

etc. [16]. For example insurance companies can obtain da-

ta about a client driving habits and location [5]. It can be

inferred that these pieces of data can be used by the com-

pany in their decision making process as it relates to the

how these pieces of data are been utilized. Few individuals are aware of and comprehend what organizations are using big

data to do. However this figure is quickly increasing [16]. Big data analytics has to be closely monitored to prevent any

violation of customer privacy. Companies have resorted anonymize data instead of using personally identifiable infor-

mation (PII). When this is done the data is looked at in the mass and then assessed to see what they can learn about the

individual. Refusing to use PII does not guarantee the anonymity of the persons whose data are been analyzed [16]. AOL

anonymized 20 million search queries and made them available to be used by researchers. However the New York Times

was able to uncover the identities of the users by merely using other data that was publicly available [17]. The recent secu-

rity breach at Target and Neiman Marcus is still etched in the memory of individuals [16]. The security of data varies

across industries; however it is a high point in the financial services, insurance and health care [16]. The bid data tools

that are available today, coupled with availability of large public data sets make it a challenge to anonymize data [17]. An-

other challenge posed in the big data era is the de-identification of personal health information [17]. The HIPAA Privacy

Rule stipulates how data should be de-indentified and re-identified [17]. The use of statistical tools and practices, or the

removing of fields to enable revelation of an individual identity is strictly prohibited [17]. Data can be de-identified by

simply taking away key information from the data set, thus preventing individuals from identifying who the data subjects

46

to implement one [8]. This practice

is fairly new in some organizations;

hence its official definition is still

evolving at this point [10]. Data gov-

ernance is defined as the merging of

data quality management, data man-

agement systems, data security, and

data administration to control and

manage data within an organization

[10]. In a nutshell data governance

takes into consideration proper ac-

quisition and monitoring of data in

an organization. In addition data

governance practices are defined as

those policies in the organization

which provide a description of how

data ought to be managed for the

duration of its economic life cycle

a r e [ 1 9 ] . T h e p r o c e s s o f d e -

identification must be done proper-

ly. Failure to do so will facilitate easy

re-identification of data subjects [19].

If data gets into the hands of third

parties who are able to re-identify

the data subjects, which can result in

the dissemination of sensitive infor-

mation [19].

Description of data governance

Rand Secure Data, a division of Rand

Worldwide conducted a survey re-

cently which uncovered the fact that

44 percent of the companies sur-

veyed are without a formal data gov-

ernance policy, whilst another 22

percent did not express an interest

[9]. Data governance practices are

broken down into three components,

namely: structural, operational and

relational [9]. Big data grows at a fast

pace [6]. The company is able to

make better decisions whilst its data

is deemed consistent, accurate and

available [6]. Intel, Google and

Walmart presently manage several

petabytes of data [9]. For the most

part organizations tend to struggle

with the implementation of appropri-

ate governance mechanisms that will

effectively strike a balance between

the risk and the reward of big data

[9]. The value of data should be es-

tablished and used to determine how

much investment is required to be

made with a particular set of data

[9]. For example if profits to be de-

rived from a set of data are one mil-

lion dollars, it would not be worth

investing 5 million dollars in storage

technologies for that data [9]. It

should be noted that it is going to

cost organizations more money to

manage their big data [9]. This will

pose a problem if the organization

does not foresee significant increas-

es in profit margins [9]. Despite the

cost involved to have proper data

governance in place it is imperative

that organization make the neces-

sary investments required in this

area. The benefits to be derived

from good data governance in-

cludes: better quantity and quicker

decision making, quick response to

business change, improvement in

business intelligence reporting, re-

duction in costs, compliance with

established government regula-

tions, improvement in customer

satisfaction, improvement in posi-

tion on the market, to name a few

[13]. The value of a company’s infor-

mation will increase once a decent

data governance policy is put in

place [6]. In addition the company is

able to make better decisions as

long as its data is deemed con-

sistent, accurate and available [6].

MOTIVATION

Big data has sparked the interest of

many individuals particularly in the

field of business. The technologies

used in this era to manipulate data

have changed the way how business

is conducted, particularly in the

field of marketing. The ingenuity of

big data analytics have transformed

the way how meaning can be de-

rived from data. However this trans-

formation have somewhat contrib-

uted to the hunger and desire for

data scientist wanting to do more

with data that they have. Data does

not exist supernaturally, it belongs

to someone. Looking at it holistical-

ly, it would appear as if only data is

been touched. However the person

who the data belongs to is ultimately

affected by the hands of these data

analysts or even the computer algo-

rithms put in place. We are now

living in an era where what you put

in social media, text messages,

email, etc can be used to dictate the

kind of person who you are. Is this

analysis true? How much control do

you have as an individual over inter-

pretations made about you? Are you

capable of maintaining privacy in

the big data era? The results gained

might or might not be beneficial to

the person to whom it belongs. The

more power a data analyst gets the

47

O. Hamami spoke to the limitations of data analytics [17].

O. Hamami also mentioned in the study that big data

analytics has to be closely monitored to prevent violation

of customer privacy. The study also outlined how chal-

lenging the task is to maintain anonymity of the custom-

ers to whom a particular data set belongs in the big data

era [17].

D. Navetta looked at how marketers now rely on big data

to determine the correct product to market to the cus-

tomer and the time frame in which this should be done.

Evidently the customers are no longer required to speak,

the data obtained from them will be used to do this in-

stead [19]. D. Navetta also alluded to the fact that anonym-

ity of data subjects is difficult to maintain where big data

is concerned [19].

weaker the privacy of the customer gets.

RELATED WORK

J. Harper looked at how data obtained from social media;

sensors, etc are used to make decisions about individuals,

sometimes not in their favour [5]. H. Buhl, M. Roglinger, F.

Moser, and J. Heidemann, and J. Harper studies also

looked at how location based data was used by businesses

to make decisions about their clients [2], [5].

H. J. Watson looked at how data is combined from several

sources to arrive at what decisions to be made about cus-

tomers. Pieces of data such as who you are, where you live

and the friends were used to determine the products you

are more inclined to purchase [16].

PROPOSED SOLUTION

Data privacy remains a critical issue when it comes to big

data. The ability for the organization to effectively deal

with this issue lies in an effective data governance pro-

gram. When organizations choose a data governance pro-

gram it is critical that the same be aligned with the needs

of that specific organization. Notably, a governance pro-

gram might work excellent in one organization and pro-

duce minimal benefits to another simply because their

requirements differ. The same principle also applies to

the type of organization in which the data governance pro-

gram is to be implemented. This paper is focusing on rec-

ommending an appropriate data governance program

within the field of business to effectively manage privacy

issues with big data.

Before a governance framework is established it is imper-

ative that the affected organization familiarize itself with

the different types of big data [20]. Each big data type has

to be treated differently to get the desired results where

data governance is concerned.

Big data falls into five main categories, namely: social me-

dia, web, machine to machine (M2M), big transaction da-

ta, biometrics and human-generated. It must be noted

that ‘big data’ is ‘data’, hence the ‘traditional disciplines’

of information governance still applies to it [20]. The

tricky thing about big data thought is that its use cases are

used to drive its analytics and these use cases normally

relates to a specific industry or function [20]. Therefore a

data governance program will have to be tailor made

around a particular use case.

The DGPC (Data Governance for Privacy, Confidentiality

and Compliance) framework is recommended to address

the privacy issue currently experienced with big data.

The framework is comprised of three core elements,

namely: people, process and technology [21].

Figure 1. The three dimensional framework for gov-

ernance of big data (20)

48

The process element of the DGPC involves going through

authority documents such as: statutes, regulations, stand-

ards and company policies. These documents will outline

clearly what requirements are to be met [22]. It must also

be noted that organization are required to identify a set of

rules that is applicable to all of the core processes. These

principles identified include data privacy and confidenti-

ality principles [21].

The technology component involves checking selected

data flows and ensure that flows-specific risks that were

previously unaddressed by the information security man-

agement systems and or the control framework broader

protective measures will be identified [22]. The organiza-

tion is required to fill out what is called a risk/gap analysis

matrix when this approach is used. This matrix is built is

around the information life cycle, four technology do-

mains and the data privacy and confidentiality principles

for the organization [22].

An organization must be fully aware of how data flows

through its systems, is accessed and processed at differ-

ent stages by several applications and people for a wide

variety of purposes [22]. Figure 4 outlines the concept

behind the information processing life cycle. An organi-

zation must be fully aware of risks that can occur at each

stage to effectively combat them if they should arise in

the future [22].

Every time data is required to be transferred using what-

ever means necessary within the organization a new in-

formation life cycle will begin. It is critical that the organ-

ization applies the same restrictions for security and pri-

vacy to the transfer of data as what was previously as-

signed to the original data set [22]. The organization

Figure 2. Core elements of the DGPC Framework (21)

People are needed to use and manage the various data

governance processes and tools [22]. They are consid-

ered to be the most significant of the three cores [21]. It is

recommended that a DGPC team be set up in the initial

stages. The members of the team should come from in-

side the organization and each member should be as-

signed clearly defined roles and responsibilities along

with the prerequisite resources required to effectively

perform their duties [22]. These team members are

called data stewards [22]. The team is formed with repre-

sentatives from all levels in the organization [21]. These

include: executive management, data governance organ-

ization and workforce and trusted business partners [21].

Figure 3. The People Pyramid for DGPC (21)

Figure 4. Information Life Cycle (22)

49

email accounts via attachments, to

name a few. These devices can be

easily lost or stolen; hence it is im-

perative that the organization put

measures in place to effectively

monitor and protect these types of

transfers when they are required to

be performed [22]. Organizations are

encouraged to thoroughly evaluate if

the effectiveness of the various tech-

nologies used to protect data confi-

dentiality, integrity and availability

[22]. The organization should ensure

that there is a secure infrastructure

in place. Such an infrastructure

should be developed such that it will

not be vulnerable to malicious at-

tacks. Identity and access control

needs to be aware of the rules and

regulations that are related to data

privacy at the recipient’s location

[22]. These include but are not lim-

ited to the policies, systems and

practices [22]. These details are re-

quired to be identified prior to the

transfer of data as if additional secu-

rity measures will have to put in

place to deal with any underlying

issue the necessary changes can be

made. The organization also needs

to track situations where reports are

run and subsets of data are extracted

from databases. It is evident that

these pieces of data can be easily

transferred to removable storage

media, handheld devices, laptops,

should also be held in high esteem.

Only authorized access should be

granted and user privileges should

be effectively monitored across the

organization. The organization

should ensure that measures be put

in place protect its information and

relevant technologies should be in-

stituted to carry out auditing and re-

porting activities [22].

The organization should put in place

data privacy and confidentiality prin-

ciples to ensure the privacy of its cus-

tomer data is maintained. There a

four principles which are used to

govern such activities. They are: ad-

here to the policies outline for the

confidential life span, reduce the

risk associated with unauthorized

access or misuse of confidential da-

ta, reduce the impact due to loss of

confidential data and document rel-

evant controls and demonstrate

their effectiveness [22]. During the

confidential life span of data it is

critical that the organization pro-

cesses the data as stipulated by the

regulations. The preservation of the

customers privacy is critical during

this period of time as any breach of

this can prove to be catastrophic for

the both the customer and the or-

ganization. Therefore the organiza-

tion needs to enforce anonymity at

all costs wherever it is possible to do

so and ensure that the data is de-

identified in a manner that makes it

difficult to be re-identified and used

for other purposes. The organiza-

tion also needs to ensure that the

relevant administrative, technical

and physical mechanisms are put in

place to reduce the unauthorized

access and use of data that is

deemed confidential [22]. It was

mentioned earlier that individuals

can easily re-identify data using var-

ious technologies. The organization

needs put in as many redundancy

measures as possible to maintain

confidentiality as exposure of such

data can prove to be costly. The or-

ganization needs to be aware that no

system is free from penetration.

Therefore measures should be put

in place to ensure that confidential

data is lost or stolen the impact on

the customer and the organization

will not be too severe. It is encour-

aged that data be encrypted that if it

gets into the hands of the unscrupu-

lous individuals then the data would

be meaningless to them. In addition

breach response plans should be put

in place to deal with any such occur-

rences. Members of staff should

also be a part of this plan and should

receive training on this matter [22].

It is critical that all members of staff

be trained because swift actions are

required to taken in eventuality of

any security breach. Transparency

of activities is critical in the whole

process of maintain data privacy.

The organization needs have in

place appropriate monitoring and

auditing tools in place to ensure that

the data privacy and confidentiality

issues outlined by the organization

are been followed. If there are any

cases of non-compliance in this re-

gard the organization needs to

should put measures in place that

will mandate the submission of an

official report about such occur-

rences [22].

50

CONCLUSION

Maintaining the privacy of customer data will never be

an old issue. It is evident that as technology advances the

more measures will have to be put in place to combat

issues in this area. It must be noted that even though

maintaining the privacy of customers’ data is a daunting

task proper data governance can be used by organiza-

tions to effectively minimize the occurrences and effects

of data privacy. Data governance cannot be implement-

ed and effectively monitored by an individual; it has to be

a team effort. It is imperative that all organizations put

data governance frameworks in place to combat the data

privacy, particularly in the area of big data.

FUTURE WORK

Further research can be conducted on ethical issues con-

cerning big data analytics. Big data analytics has undoubt-

edly introduced some interested perspectives on how data

can be effectively manipulated to arrive at interesting re-

sults. Companies are no longer required for the most part

to personally ask customers what they want as they can

use data aggregation, location base data, to name a few to

get their desired results. Companies are now at a point

where they are no longer obligated to ask, they can just

take what they want. Are such actions ethical? Can a data

governance framework be put in place to address ethical

issues surrounding big data analytics?

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03). Available: http://library.utech.edu.jm/ [August 6, 2014].

2. H. Buhl, M. Roglinger, F. Moser, and J. Heidemann. (2013). “Big Data: A Fasionable Topic with(out) Sustainable Relevance for Research and Practice,” BISE-Editorial [Online]. Available: http://library.utech.edu.jm/ [August 6, 2014].

3. A. McAfee, and E. Brynjolfsson. (2012). “Big Data: The Management Revolution,” Harvard Business Review [Online]. Available: http://library.utech.edu.jm/ [August 6, 2014].

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http://www.isaca.org/Journal/Past-Issues/2010/Volume-6/Pages/Data-Governance-for-Privacy-Confidentiality-and-Compliance.aspx

http://www.isaca.org/Journal/Past-Issues/2010/Volume-6/Pages/Data-Governance-for-Privacy-Confidentiality-and-Compliance.aspx

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