data-ed online webinar: monetizing data management

Copyright 2013 by Data Blueprint

Show Me The Money: Monetizing Data ManagementFailure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data management practices as the root causes of many of their business problems, they become more willing to make the required investments in our profession. This presentation uses specific examples to illustrate the costs of poor data management and how it impacts business objectives. Join us and learn how you can better align your data management projects with business objectives to justify funding and gain management approval. http://www.datablueprint.com/monetizing-data-management-survey/

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PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA

MONETIZINGDATA MANAGEMENT

Unlocking the Value in Your Organization’s

Most Important Asset.

Basic Data Management Practices– Data Program Management– Organizational Data Integration– Data Stewardship– Data Development– Data Support Operations

Advanced Data

Practices• MDM• Mining• Big Data• Analytics• Warehousing• SOA

Data Data

Data

Information

Fact Meaning

Request

[Built on definitions from Dan Appleton 1983]

Intelligence

Strategic Use

Wisdom & knowledge are often used synonymously

Data

Data

Data Data

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Organizational Data

Organizational Data Managers

Technologies

Process

People

Less Data ROT ->

Common Organizational Data (and corresponding data needs requirements)

New Organizational Capabilities

Systems Development

Activities

Create

Evolve

Future State

(Version +1)

Data evolution is separate from, external to, and precedes system development life cycle activities!

Data/Information

Network/Infrastructure

Systems/Applications

Goals/Objectives

StrategyPayroll Application

(3rd GL)Payroll Data(database)

R& D Applications(researcher supported, no documentation)

R & DData(raw) Mfg. Data

(home growndatabase)

Mfg. Applications(contractor supported)

FinanceData

(indexed)

Finance Application(3rd GL, batch

system, no source)

Marketing Application(4rd GL, query facilities, no reporting, very large)

Marketing Data(external database)

Personnel App.(20 years old,

un-normalized data)

Personnel Data(database)



Data/Information

Goals/Objectives

StrategyDistrict-L (as an example) Leave Tracking Time AccountingEmployees 73 50Number of documents 1000 2040Timesheet/employee 13.70 40.8Time spent 0.08 0.25Hourly Cost $6.92 $6.92Additive Rate $11.23 $11.23Semi-monthly cost per timekeeper $12.31 $114.56

Total semi-monthly timekeeper cost $898.49 $5,727.89

Annual cost $21,563.83 $137,469.4034

1. Manual transfer of digital data2. Manual file movement/duplication3. Manual data manipulation4. Disparate synonym reconciliation 5. Tribal knowledge requirements 6. Non-sustainable technology

Data Mapping

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Mental illness

Deployments

Work History

Soldier Legal Issues

Abuse

Suicide Analysis

FAPDMSS G1 DMDC CID

Data objects complete?

All sources identified?

Best source for each object?

How reconcile differences between sources?

MDR


Executive Editor at DATAVERSITY.net

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Shannon Kempe


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Show Me The Money

Monetizing Data Management

Presented by Peter Aiken, Ph.D.





Copyright 2013 by Data Blueprint 5

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• 30+ years of experience in data management

• Multiple international awards & recognition

• Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS, VCU (vcu.edu) • (Past) President, DAMA Int. (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data

management practices in 20 countries • Multi-year immersions with

organizations as diverse as the US DoD, Nokia, Deutsche Bank, Wells Fargo, Walmart, and the Commonwealth of Virginia

Peter Aiken, Ph.D.

The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset

Peter Aiken andMichael Gorman






1. Data Management Overview

2. Book Motivations & Survey Results

3. Leveraging Data

4. Monetary ROI (6 cases)

5. Non-Monetary ROI (2 cases)

6. Legal Considerations

7. Take Aways and Q&APETER AIKEN WITH JUANITA BILLINGS

FOREWORD BY JOHN BOTTEGA




Outline

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Tweeting now: #dataed




3. Leveraging Data









Outline

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Data Program Coordination

Feedback

DataDevelopment


StandardData

Data Management is an Integrated System of Five Practice AreasOrganizational Strategies

Goals

BusinessData

Business Value

Application Models & Designs

Implementation

Direction

Guidance

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OrganizationalData Integration

DataStewardship

Data SupportOperations

Data Asset Use

Integrated Models

Leverage data in organizational activities

Data management processes and infrastructure

Combining multiple assets to produce extra value

Organizational-entity subject area data

integration

Provide reliable data access

Achieve sharing of data within a business area


Five Integrated DM Practices

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Manage data coherently.

Share data across boundaries.

Assign responsibilities for data.Engineer data delivery systems.

Maintain data availability.


DataDevelopment


DataStewardship


Maslow's Hierarchiy of Needs


You can accomplish Advanced Data Practices without becoming proficient in the Basic Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present

greaterrisk


Data Management Practices Hierarchy

Basic Data Management Practices

Advanced Data

Practices • MDM • Mining • Big Data • Analytics • Warehousin

g • SOA

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Data Program Management

Data Stewardship Data Development

Data Support Operations

Organizational Data Integration


Feedback

DataDevelopment


StandardData

Five Integrated DM Practice AreasOrganizational Strategies

Goals

BusinessData

Business Value

Application Models & Designs

Implementation

Direction

Guidance

12


DataStewardship


Data Asset Use

Integrated Models

Leverage data in organizational activities

Data management processes and infrastructure

Combining multiple assets to produce extra value

Organizational-entity subject area data

integration

Provide reliable data access

Achieve sharing of data within a business area


Five Integrated DM Practice Areas

13

Manage data coherently.

Share data across boundaries.

Assign responsibilities for data.Engineer data delivery systems.

Maintain data availability.


Organizational Data Integration

Data Stewardship Data Development

Data Support Operations


Hierarchy of Data Management Practices (after Maslow)

Basic Data Management Practices – Data Program Management – Organizational Data Integration – Data Stewardship – Data Development – Data Support Operations

http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png

Advanced Data

Practices • MDM • Mining • Big Data • Analytics • Warehousin

g

• 5 Data management practices areas / data management basics ...

• ... are necessary but insufficient prerequisites to organizational data leveraging applications that is self actualizing data or advanced data practices




3. Leveraging Data









Outline

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3. Leveraging Data









Outline

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2013 Monetizing Data Management Survey Results

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http://www.datablueprint.com/monetizing-data-management-survey/


• Soon to be released: white paper & survey results

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2013 Monetizing Data Management Survey Results

http://www.datablueprint.com/monetizing-data-management-survey/


Amazon Reviews

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One Star Reviews

• "My reason for purchasing this book was to learn about how organizations are finding ways to monitize their data assets. By that I mean finding ways to generate income using their data assets or the insights derived from those assets."

• "This book title 'Monetizing data management', the reason I purchased this book is to know how to earn the money from organizational data. however this book didn't talk anything about making money through data management."

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Five Star Reviews

• "A book you can read from cover to cover on an airplane trip or during lunch over a period of days. I'm very big on stories, and the book contains many stories from the authors' experiences on how to valuate data management. It helped me brainstorm on a presentation I was working on to explain the value of our enterprise information management initiative."

• "A concise summary of how to put a value on data management in your organization. I would not categorize this book as a "how to" guide - more of a brainstorming book to help someone come up with a value for their hard data management work. Great stories and tangible results!"

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Motivation ...• Amazon rank: 1,257,801 • Task: helping our community better articulate the

importance of what we do • Until we can meaningfully communicate in monetary or

other terms equally important to the C-suite, we will continue to struggle to articulate the value of its role

• Today’s business executives – Smart, talented and experienced experts – Executive decision-makers being far removed and

insufficiently data knowledgeable – Too many decisions about data have been poor

• Four Parts – Unique perspective to the practice of leveraging data – 11 cases where leveraging data has produced positive

financial results – Five instance non-monetary outcomes of critical important

to the C-suite – Interaction of data management practices and both IT

projects and legal responsibilities

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3. Leveraging Data









Outline

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3. Leveraging Data









Outline

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Data Data

Data

Information

Fact Meaning

Request

Strategic Information Use: Prerequisites

[Built on definitions from Dan Appleton 1983]

Intelligence

Strategic Use

1. Each FACT combines with one or more MEANINGS. 2. Each specific FACT and MEANING combination is referred to as a DATUM. 3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST 4. INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING. 5. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES. 6. DATA/INFORMATION must formally arranged into an ARCHITECTURE.

Wisdom & knowledge are often used synonymously

Data

Data

Data Data

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Leverage is an Engineering Concept

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• Using proper engineering techniques, a human can lift a bulk that is weighs much more than the human


Data Leverage is an Engineering Concept

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Organizational Data

Organizational Data Managers

Technologies

Process

People

• Note: Reducing ROT increases data leverage

Less Data ROT ->


Why Is Data Management Important? • Too much data leads directly to wasted productivity

– Eighty percent (80%) of organizational data is redundant, obsolete or trivial (ROT)

• Underutilized data leads directly to poorly leveraged organizational resources – Manpower – costs associated with labor resources and

market share – Money – costs associated

with management of financial resources

– Methods – costs associated with operational processes and product delivery

– Machines – costs associated with hardware, software applications and data to enhance production capability

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Incorrect Educational Focus• Building new systems

– 80% of IT costs are spent rebuilding and evolving existing systems and only 20% of costs are spent building and acquiring new systems

– Putting fresh graduates on new projects makes this proposition more ridiculous

– Only the most experienced professionals should be allowed to participate in new systems development.

• Who is responsible for managing data assets? – Business thinks IT is taking care of it - it is called IT after all? – IT thinks if you can sign on to the system their job is complete

• System development practices – Data evolution is separate from, external to and must precede

system development life cycle activities! – Data is not a project - it has no distinct beginning and end

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Evolving Data is Different than Creating New Systems

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Common Organizational Data (and corresponding data needs requirements)

New Organizational Capabilities

Systems Development

Activities

Create

Evolve

Future State

(Version +1)

Data evolution is separate from, external to, and precedes system development life cycle activities!


Application-Centric Development

Original articulation from Doug Bagley @ Walmart

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Data/Information



Goals/Objectives

Strategy• In support of strategy, organizations develop specific goals/objectives

• The goals/objectives drive the development of specific systems/applications

• Development of systems/applications leads to network/infrastructure requirements

• Data/information are typically considered after the systems/applications and network/infrastructure have been articulated

• Problems with this approach: – Ensures data is formed to the applications and not

around the organizational-wide information requirements

– Process are narrowly formed around applications

– Very little data reuse is possible


Payroll Application(3rd GL)Payroll Data

(database)

R& D Applications(researcher supported, no documentation)

R & D Data (raw) Mfg. Data

(home grown database)

Mfg. Applications(contractor supported)

Finance

Data (indexed)

Finance Application(3rd GL, batch

system, no source)

Marketing Application(4rd GL, query facilities, no reporting, very large)

Marketing Data

(external database)

Personnel App.(20 years old,

un-normalized data)

Personnel Data

(database)

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Typical System Evolution

Einstein Quote


"The significant problems we face cannot be solved at the same level of thinking we were at when we created them."- Albert Einstein


Data-Centric Development

Original articulation from Doug Bagley @ Walmart

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Data/Information

Goals/Objectives

Strategy• In support of strategy, the organization develops specific goals/objectives

• The goals/objectives drive the development of specific data/information assets with an eye to organization-wide usage

• Network/infrastructure components are developed to support organization-wide use of data

• Development of systems/applications is derived from the data/network architecture

• Advantages of this approach: – Data/information assets are developed from an

organization-wide perspective – Systems support organizational data needs and

compliment organizational process flows – Maximum data/information reuse


Polling Question #1 • Who or what

department(s) makes the decision on investing in data management initiatives?

A) IT B) Supported business area C) IT and the supported

business area together D) Office of Chief Data

Officer or Enterprise Data Office/Equivalent

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3. Leveraging Data









Outline

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3. Leveraging Data









Outline

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Monitization: Time & Leave Tracking

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At Least 300 employees are spending 15 minutes/week

tracking leave/time


Capture Cost of Labor/Category

District-L (as an example) Leave Tracking Time AccountingEmployees 73 50Number of documents 1000 2040Timesheet/employee 13.7 40.8Time spent 0.08 0.25Hourly Cost $6.92 $6.92Additive Rate $11.23 $11.23Semi-monthly cost per timekeeper $12.31 $114.56

Total semi-monthly timekeeper cost $898.49 $5,727.89

Annual cost $21,563.83 $137,469.40


Compute Labor Costs

• Range $192,000 - $159,000/month

• $100,000 Salem

• $159,000 Lynchburg

• $100,000 Richmond

• $100,000 Suffolk

• $150,000 Fredericksburg

• $100,000 Staunton

• $100,000 NOVA

• $800,000/month or $9,600,000/annually

• Awareness of the cost of things considered overhead


Annual Organizational Totals


International Chemical Company Engine Testing

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• $1billion (+) chemical company

• Develops/manufactures additives enhancing the performance of oils and fuels ...

• ... to enhance engine/machine performance – Helps fuels burn cleaner – Engines run smoother – Machines last longer

• Tens of thousands of tests annually – Test costs range up to

$250,000!


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1.Manual transfer of digital data 2.Manual file movement/duplication 3.Manual data manipulation 4.Disparate synonym reconciliation 5.Tribal knowledge requirements 6.Non-sustainable technology


Data Integration Solution• Integrated the existing systems to

easily search on and find similar or identical tests

• Results: – Reduced expenses – Improved competitive edge

and customer service – Time savings and improve operational

capabilities

• According to our client’s internal business case development, they expect to realize a $25 million gain each year thanks to this data integration

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Vocabulary is Important-Tank, Tanks, Tankers, Tanked

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How one inventory item proliferates data throughout the chain

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555 Subassemblies & subcomponents

17,659 Repair parts or Consumables

System 1:18,214 Total items

75 Attributes/ item1,366,050 Total attributes

System 2 47 Total items

15+ Attributes/item720 Total attributes

System 3 16,594 Total items 73 Attributes/item

1,211,362 Total attributes

System 4 8,535 Total items

16 Attributes/item136,560 Total attributes

System 515,959 Total items22 Attributes/item351,098 Total attributes

Total for the five systems show above:59,350 Items

179 Unique attributes3,065,790 values

• National Stock Number (NSN) Discrepancies – If NSNs in LUAF, GABF, and RTLS are

not present in the MHIF, these records cannot be updated in SASSY

– Additional overhead is created to correct data before performing the real maintenance of records

• Serial Number Duplication – If multiple items are assigned the same

serial number in RTLS, the traceability of those items is severely impacted

– Approximately $531 million of SAC 3 items have duplicated serial numbers

• On-Hand Quantity Discrepancies – If the LUAF O/H QTY and number of items serialized in RTLS conflict, there can

be no clear answer as to how many items a unit actually has on-hand – Approximately $5 billion of equipment does not tie out between the LUAF &

RTLS


Business Implications


Improving Data Quality during System Migration

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• Challenge – Millions of NSN/SKUs

maintained in a catalog – Key and other data stored in

clear text/comment fields – Original suggestion was manual

approach to text extraction – Left the data structuring problem unsolved

• Solution – Proprietary, improvable text extraction process – Converted non-tabular data into tabular data – Saved a minimum of $5 million

– Literally person centuries of work

Unmatched Items

Ignorable Items

Items Matched

Week # (% Total) (% Total) (% Total)1 31.47% 1.34% N/A2 21.22% 6.97% N/A3 20.66% 7.49% N/A4 32.48% 11.99% 55.53%… … … …14 9.02% 22.62% 68.36%15 9.06% 22.62% 68.33%16 9.53% 22.62% 67.85%17 9.5% 22.62% 67.88%18 7.46% 22.62% 69.92%


Determining Diminishing Returns

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Time needed to review all NSNs once over the life of the project:NSNs 2,000,000Average time to review & cleanse (in minutes) 5Total Time (in minutes) 10,000,000

Time available per resource over a one year period of time:Work weeks in a year 48Work days in a week 5Work hours in a day 7.5Work minutes in a day 450Total Work minutes/year 108,000

Person years required to cleanse each NSN once prior to migration:Minutes needed 10,000,000Minutes available person/year 108,000Total Person-Years 92.6

Resource Cost to cleanse NSN's prior to migration:Avg Salary for SME year (not including overhead) $60,000.00Projected Years Required to Cleanse/Total DLA Person Year Saved 93Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million


Quantitative Benefits


Seven Sisters (from British Telecom)

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Thanks to Dave Evans

http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/


Polling Question #2 • Is it hard to obtain

funding for your data management projects?

A) Yes, because it is hard to show value

B) Yes, because we have not aligned with the business objectives

C) Yes, because no precedent has been set

D) No, because we can clearly demonstrate value

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3. Leveraging Data









Outline

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3. Leveraging Data









Outline

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In one of the more horrifying incidents I've read about, U.S. soldiers and allies were killed in December 2001 because of a stunningly poor design of a GPS receiver, plus "human error." http://www.washingtonpost.com/wp-dyn/articles/A8853-2002Mar23.html A U.S. Special Forces air controller was calling in GPS positioning from some sort of battery-powered device. He "had used the GPS receiver to calculate the latitude and longitude of the Taliban position in minutes and seconds for an airstrike by a Navy F/A-18." According to the *Post* story, the bomber crew "required" a "secondcalculation in 'degree decimals'" -- why the crew did not have equipment to perform the minutes-seconds conversion themselves is not explained. The air controller had recorded the correct value in the GPS receiver when the battery died. Upon replacing the battery, he called in the degree-decimal position the unit was showing -- without realizing that the unit is set up to reset to its *own* position when the battery is replaced. The 2,000-pound bomb landed on his position, killing three Special Forces soldiers and injuring 20 others. If the information in this story is accurate, the RISKS involve replacing memory settings with an apparently-valid default value instead of blinking 0 or some other obviously-wrong display; not having a backup battery to hold values in memory during battery replacement; not equipping users to translate one coordinate system to another; and using a device with such flaws in a combat situation


Friendly Fire deaths traced to Dead Battery

55

Suicide Mitigation


Suicide MitigationData Mapping

12

Mental illness

Deployments

Work History

Soldier Legal Issues

Abuse

Suicide Analysis

FAPDMSS G1 DMDC CID

Data objects complete?

All sources identified?

Best source for each object?

How reconcile differences between sources?

MDR



Senior Army Official• A very heavy dose of

management support

• Any questions as to future data ownership, "they should make an appointment to speak directly with me!"

• Empower the team

– The conversation turned from "can this be done?" to "how are we going to accomplish this?"

– Mistakes along the way would be tolerated

– Implement a workable solution in prototype form

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Communication Patterns

59

Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide and Saving Lives - The Final Report of the Department of Defense Task Force on the Prevention of Suicide by Members of the Armed Forces - August 2010


Polling Question #3 • What percentage of

your data projects are successful?

A) All B) 25% C) 75% D) none

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3. Leveraging Data









Outline

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3. Leveraging Data









Outline

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Plaintiff (Company X)

Defendant(Company Y)

AprilRequests a recommendation from ERP Vendor

Responds indicating "Preferred Specialist" status

JulyContracts Defendant to implement ERP and convert legacy data

Begins implementation

January Realizes a key milestone has been missed

Stammers an explanation of "bad" data

JulySlows then stops Defendant invoice payments

Removes project team

Files arbitration request as governed by contract with Defendant


Messy Sequencing Towards Arbitration

63


Points of Contention• Who owned the

risks? • Who was the project

manager? • Was the data of poor

quality? • Did the contractor

(Company Y) exercise due diligence?

• Was their methodology adequate?

• Were required standards of care followed and were the work products of required quality?

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Expert ReportsOurs provided evidence that : 1. Company Y's conversion code introduced

errors into the data 2. Some data that Company Y converted was of

measurably lower quality than the quality of the data before the conversion

3. Company Y caused harm by not performing an analysis of the Company X's legacy systems and that that the required analysis was not a part of any project plan used by Company Y

4. Company Y caused harm by withholding specific information relating to the perception of the on-site consultants' views on potential project success

Expert Report

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FBI & Canadian Social Security Gender Codes

1. Male 2. Female 3. Formerly male now female 4. Formerly female now male 5. Uncertain 6. Won't tell 7. Doesn't know 8. Male soon to be female 9. Female soon to be male

If column 1 in source = "m" • then set value of target data to "male"

• else set value of target data to "female"

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The defendant knew to prevent duplicate SSNs

!************************************************************************ ! Procedure Name: 230-Assign-PS-Emplid ! ! Description : This procedure generates a PeopleSoft Employee ID ! (Emplid) by incrementing the last Emplid processed by 1 ! First it checks if the applicant/employee exists on ! the PeopleSoft database using the SSN. ! !************************************************************************ Begin-Procedure 230-Assign-PS-Emplid

move 'N' to $found_in_PS !DAR 01/14/04 move 'N' to $found_on_XXX !DAR 01/14/04

BEGIN-SELECT -Db'DSN=HR83PRD;UID=PS_DEV;PWD=psdevelopment' NID.EMPLID NID.NATIONAL_ID

move 'Y' to $found_in_PS !DAR 01/14/04 move &NID.EMPLID to $ps_emplid

FROM PS_PERS_NID NID !WHERE NID.NATIONAL_ID = $ps_ssn WHERE NID.AJ_APPL_ID = $applicant_id END-SELECT

if $found_in_PS = 'N' !DAR 01/14/04 do 231-Check-XXX-for-Empl !DAR 01/14/04 if $found_on_XXX = 'N' !DAR 01/14/04 add 1 to #last_emplid let $last_emplid = to_char(#last_emplid) let $last_emplid = lpad($last_emplid,6,'0') let $ps_emplid = 'AJ' || $last_emplid end-if end-if !DAR 01/14/04

End-Procedure 230-Assign-PS-Emplid

AJHR0213_CAN_UPDATE.SQR

The exclamation point prevents this line from

looking for duplicates, so no check is made for a duplicate SSN/National

ID

Legacy systems business rules allowed employees to

have more than one AJ_APPL_ID.

67


Identified & Quantified Risks

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Risk Response “Risk response development involves defining enhancement steps

for opportunities and threats.” Page 119, Duncan, W., A Guide to the Project Management Body of Knowledge, PMI, 1996

"The go-live date may need to be extended due to certain critical path deliverables not being met. This extension will require additional tasks and resources. The decision of whether or not to extend the go-live date should be made by Monday, November 3, 20XX so that resources can be allocated to the additional tasks."

Tasks HoursNew Year Conversion 120Tax and payroll balance conversion 120General Ledger conversion 80

Total 320

Resource HoursG/L Consultant 40Project Manager 40Recievables Consultant 40HRMS Technical Consultant 40Technical Lead Consultant 40HRMS Consultant 40Financials Technical Consultant 40

Total 280

Delay Weekly Resources Weeks Tasks CumulativeJanuary (5 weeks) 280 5 320 1720February (4 weeks) 280 4 1120

Total 2840

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Process Planning Area Company Y Company X LeadMethodology Demonstrated

Scope Planning √ √Scope Definition √ √Activity Definition √Activity Sequencing √Activity Duration Estimation √Schedule Development √Resource Planning √ √Cost Estimating √Cost Budgeting √Project Plan Development ?Quality Planning ? ?Communication Planning √ √Risk Identification √ √Risk Quantification √Risk Response √ ? ?Organizational Planning √ √Staff Acquisition √


Project Management Planning

71


Inadequate Standard of Care - Tasks without Predecessors

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Inadequate Standard of Care

73


Professional & Workmanlike Manner

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Defendant warrants that the services it provides hereunder will be performed in a professional and workmanlike manner in accordance with industry standards.


The Defense's "Industry Standards"• Question:

– What are the industry standards that you are referring to? • Answer:

– There is nothing written or codified, but it is the standards which are recognized by the consulting firms in our (industry).

• Question: – I understand from what you told me just a moment ago that

the industry standards that you are referring to here are not written down anywhere; is that correct?

• Answer: – That is my understanding.

• Question: – Have you made an effort to locate these industry standards

and have simply not been able to do so? • Answer:

– I would not know where to begin to look.

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3. Leveraging Data









Outline

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3. Leveraging Data









Outline

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Monetizing Data Management


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• State Agency Time & Leave Tracking – Time and leave tracking

• $1 million USD annually

• International Chemical Company – Data management: Test results – $25 million UDS annually

• ERP Implementation – Transformation of non-tabular data

• $5 million annually • Person Centuries

• British Telecom Project Rollout – £250 (small investment)

• Non-Monetary Examples – Friendly Fire – Suicide Mitigation

• Legal – ERP Implementation Legal Case

• $ 5,355,450 CAN damages/penalties






Questions?

79

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your questions to Peter now.

data-ed online webinar: monetizing data management

Business

data evolution

data management investments

data management projects

manual data manipulation4

poor data management

costs of poor data management

management approval

semimonthly cost