reflections on alignment with togaf 5 th november 2008 james dawson - consulting enterprise...

Reflections on alignment with TOGAF

5th November 2008

James Dawson - Consulting Enterprise Architect

“Unlocking the Information Assets”

Monitoring, explaining, predicting and optimising all aspects of the business

AOEGA AGM and Case Studies

Purpose of Today’s Talk

Presentation on UNSW Data Warehouse Project Covers strategy, business, technical and many other

aspects The thoughts and thinking on this have evolved over

several years Reflection on where elements of TOGAF can be

found and how the ADM process and methodology might have shaped and improved the outcomes

First, lets start with the presentation as is…………..

3

Overview

This presentation will examine the history and development of the UNSW data warehouse.

The presentation is less about the technical aspects and more about the business aspects of selling an enterprise solution to a broad and varied set of stakeholders, battling the NIH syndrome and what to do when the idea is actually sold.

Agenda

About the UNSW

Background on how UNSW implemented a SUN/SAS DW/BI solution Selling the idea and Gaining sponsorship Managing different stakeholders from different aspects of the business Juggling long term strategy versus immediate outcomes Different ways to tackle Project Reporting

Planning for the future Managing and developing the project as a product Developing processes and governance structures Development processes to manage non-IT developers (& IT too )

5

UNSW facts in brief

Member Go8 and Universities 21

Students (2006) 37,836

International Students (2006) 7,411

Staff (2006) 6,209

Degrees/Diplomas awarded (2005) 9,832

Total awarded 204,167

Alumni 182,401

Faculties 9

Schools 75

Centres 69

Institutes 6

Principal teaching hospitals 4

Residential colleges 8

University College (ADFA) 1

Undergraduate programs available 603

Postgraduate programs available 327

Kensington site: area (ha) 38

Permanent buildings 85

Field stations 5

Items in University Library 2.7m

Number of reporting solutions - unknown

6

UNSW facts in brief

But perhaps best know for its But perhaps best know for its

solar challenge car and football solar challenge car and football

playing robots.playing robots.

Definitions (we can skip these?)

What is a data warehouse?

A database geared towards the business intelligence requirements of an organisation. The data warehouse integrates data from the various operational systems and is typically loaded from these systems at regular intervals. Data warehouses contain historical information that enables analysis of business performance over time. (Webster)

A data warehouse is the main repository of an organization's historical data, its corporate memory. It contains the raw material for management's decision support system. The critical factor leading to the use of a data warehouse is that a data analyst can perform complex queries and analysis, such as data mining, on the information without slowing down the operational systems. (Wikipedia)

Some data modelling definitions Relational databases are optimised for online transaction

processing (OLTP). OLTP systems. For day-to-day operational needs and the database performance is tuned for many fast small transactions. can retrieve a small number of records quickly, but it can be slow if you

need to retrieve a large number of records and summarise data on the fly

data may not be consistent across the various OLTP systems access to the data can be complicated data organised around specific processes (such as order entry)

A dimensional database is designed and tuned to support the analysis of business trends and projections. This type of informational processing is known as online analytical processing (OLAP) or decision-support processing. data is “de-normalised” optimised for data retrieval and analysis new data loaded into the database is usually updated in batch and from

multiple sources a dimensional database tends to be subject oriented and aims to

answer questions such as, "What courses are most popular?" "At what time of year do courses sell best?" "In what regions are prospects and recruits weakest?"

International

Extr

act

, tr

ansf

orm

and load

Managem

ent

Report

sM

anagem

ent

Report

s

Busi

ness

Inte

lligence

Report

ing C

apabili

ty

“Operational” “Strategic”

“Base” Data(Raw, cleansed, exceptions)

Summary data

Data from systems“read only” access

ServiceManagement

Faculty(eg Medicine)

Research

Student

Others

HR

Finance

Data arranged in the DW differently. Arranged in subject areas and designed for

customisable and DIY reporting

(Examples to illustrate)virtual

History at UNSW

12

PeopleSoft@UNSW

UNSW was one of the first universities in Australia to deploy enterprise resource planning ERP software. One of the original business case items for PeopleSoft was it’s promised reporting capability.

A typical business case item for the myriad of ad hoc reporting solutions around the University since then……….

“is the lack of effective strategic reporting”, difficult access and inability to cross-link reports from PeopleSoft” :o)

Unlock the Information Assets 2003

• Extensive business analysis, ‘what if’ and judgmental content.

• Highly consolidated current and historical data.

Decision Support

• Extensive statistical and modelling content used for assessing trends and exceptions.

• Detailed and consolidated current, and usually requiring a large span of historical data.

Planning & Analysis

• Provides routine analysis & reporting.

• Consolidated current and historical data.

Monitoring & Control

Operational Support

• Provides day to day business operations and operations reporting.

• Highly detailed current data.

• Interactive operation

• Used for on-line processing with real time updates.

• Highly detailed current data.

• Time critical, highly interactive.

Transaction Processing

University Information Need Analysis, 2002


Enrolment Data Repository

1st Phase Data Warehouse

Executive Information System

Originally Planned Deployment Timeline 2003 2004 2005

Key Elements:

Improve data quality

Enforce “system of record” principles

Develop data warehouse infrastructure

Empower user community by deploying intuitive tools to analyse data

Triggered project “University Business Intelligence and Knowledge (UBIK)

Several earlier attempts at improving reporting and access to information (UBIK example)

Needs analysis conducted early 2005 with progressively inclusive interviews

A consistent set of problems emerged Problems linked to a set of key themes (and root causes) to

address Developed Model to address these themes and emerging

requirements


Typical Issues Access to information and getting effective reports or

information for decision support is a general issue Some complain that reports they need are non existent, not

known, hard to get, not always sufficiently accurate or up-to-date.

Many cite data being captured is not available or if it is they are unaware of what is there and how to get at it.

Part of this problem came from not assigning responsibility and ownership to data as there was no general policy on data management at that time.

There was no widely known general service that people could use to identify data, report problems and provide reports or even obtain lists of what is available.

Business information was and still is stored in multiple data stores with little linkages between them

Lots of stakeholders involved, each having their own requirements (and ways of stating them).

Selling the idea

Gaining common understanding and sponsorship

Seeking and gaining sponsorship

Operational decision making Are we currently on budget this month? Did this student enrol? Has the student paid their fee? Is a supervisor assigned to this student researcher?

Strategic decision making Does our new course structure attract more overseas students? What cities and countries need to be targeted more effectively? Was our last advertising campaign successful? How does our research performance compare to other Universities? Are we gaining more research grants now then five years ago?

Something in-between How many expressions of interest did we get from our last advertising campaign? Based on our current rate of enquiry, can we expect an increase or decrease in

International enrolments in four months time? What can we do to address this situation and generate more interest in time for VISA application and processing?

Is our DEST report accurate? Why don’t these reports match?

Selling the idea (1) – putting it in context

Recruitment Admissions Enrolment

ProcurementAccounts Receivable/Payable

GL

Recruiting Induction Maintenance

Assessment

IncomePayments

Pre-award Ethical clearance Post AwardProject

Management

Da

ta D

oma

ins

Student

HR

Finance

Research

Facilities

Da

ta S

tore

s (r

ep

osito

ries

)

SecurityServices

Building Works

SafetyServicesHazard

Reporting

Fire and Hazard Prevention


ProcurementAccounts Receivable/Payable

GL


Assessment

IncomePayments

Pre-award Ethical clearance Post AwardProject

Management

Da

ta D

oma

ins

Student

HR

Finance

Research

Facilities

Da

ta S

tore

s (r

ep

osito

ries

)

SecurityServices

Building Works

SafetyServicesHazard

Reporting

Fire and Hazard Prevention

Selling the Idea (2) – process context

Selling the idea (3) - root cause analysis


ProcurementAccounts

Receivable/PayableGL


Assessment

IncomePayments

Pre-awardEthical

clearancePost Award

Project Management

STUDENT

HR

FINANCE

RESEARCH

Ca

mp

us

Co

mm

un

ities

Pe

op

leso

ft

Inte

rfa

ces

Operational Reporting Areas

Data capture opportunities Systems and interfaces

Support and Functions

•Tools

•Canned

•Lists

•Extracts

•Service Current extracts

External sources

Additional Need

other other other otherOther

(eg Raisers Edge, Facilities)

Operational Reporting AreasOperational Reporting Areas


Operational Reporting Areas Operational Reporting Areas


ProcurementAccounts



Assessment

IncomePayments

Pre-awardEthical

clearancePost Award

Project Management

STUDENT

HR

FINANCE

RESEARCH

Ca

mp

us

Co

mm

un

ities

Pe

op

leso

ft

Inte

rfa

ces




•Tools

•Canned

•Lists

•Extracts


External sources

Additional Need






Real versus intangible value?

The assessment of value of information needs to be made using different criteria to standard accounting metrics such as ROI and NPV.

Management Focus

Knowledge Capital

Value

Added Value

AutomationLow

High

Low High

Tangible, direct benefits

Intangible, indirect benefits

The IT Value ParadoxSource: Butler Group

Cost reduction - mainly through labour displacement. Dominant use of IT for last 30 years

Value Creation - barely understood because measurement is so difficult. Examples include new channels to market, enhanced services to trading partners, increased collaboration…

Knowledge Capital - there is a strong correlation between the information and knowledge orientation of an organisation and its market valuation

IT Value Domains

“The difficulty in dealing with the value created through investments in knowledge capital has caused organisations to shy-away from these types of investment. However, with the benefits of business process automation largely harvested by many organisations, the challenge will be to create new value propositions around IT investments which enhance knowledge Capital.”

“The degree to which benefits are tangible and also provide value are inversely related.” (Butler Group)

The IT value paradox:

Recall this slide…..


ProcurementAccounts



Assessment

IncomePayments

Pre-awardEthical

clearancePost Award

Project Management

STUDENT

HR

FINANCE

RESEARCH

Ca

mp

us

Co

mm

un

ities

Pe

op

leso

ft

Inte

rfa

ces




•Tools

•Canned

•Lists

•Extracts


External sources

Additional Need







ProcurementAccounts



Assessment

IncomePayments

Pre-awardEthical

clearancePost Award

Project Management

STUDENT

HR

FINANCE

RESEARCH

Ca

mp

us

Co

mm

un

ities

Pe

op

leso

ft

Inte

rfa

ces




•Tools

•Canned

•Lists

•Extracts


External sources

Additional Need






Now Simplify to illustrate Target Environment

EIS

Executive Dashboard

Inte

rfac

es

Operational Reporting Support

Localised Support and Functions

•Canned

•Lists

•Extracts

•Service

•VALIDATION

External sources

En

ha

nce

d D

ata

Ca

ptu

re

Ex

tra

ct

Tra

ns

form

an

d L

oa

d (

ET

L)

Dat

a W

areh

ouse

Current extracts

Reporting Area


•Tools

•Canned

•Lists

•Extracts

•Service•

VALIDATION

Data

Mart

Data

Mart

Data

Mart

Data

Mart

DIY

•DIY

•web access

•download

Future extracts/reports

validation

OTHER

EIS

Executive Dashboard

RESEARCHRESEARCH

Inte

rfac

es

Operational Reporting Support

Localised Support and Functions

•Canned

•Lists

•Extracts

•Service

•VALIDATION

External sources

En

ha

nce

d D

ata

Ca

ptu

re

Ex

tra

ct

Tra

ns

form

an

d L

oa

d (

ET

L)

Dat

a W

areh

ouse

Current extracts

Reporting Area


•Tools

•Canned

•Lists

•Extracts

•Service•

VALIDATION

Data

Mart

Data

Mart

Data

Mart

Data

Mart

DIY

•DIY

•web access

•download

Future extracts/reports

validation

OTHER

STUDENT

HR

FINANCE

STUDENT

HR

FINANCE

Funding and Business Case

Seed funding for proof of concept Use of Butler’s “value paradox” Risk and compliance Specific savings on targeted solutions

Student churn Savings through elimination of paper based

systems Others

Strategic funding – “reporting tax”

Implementation Decisions

Reviewing tools and vendors

All have their good and bad points - reviewed according to needs/gaps of current deployments – how progressed is the UNSW in 3 major areas? Data generation (ETL, data sourcing) Data management (DB, DM tools) Information access (presentation, analysis, data mining)

Balance between choosing individual “Best of breed” versus ease of integration through smaller numbers of vendor solutions (BOB versus solution suites) Warehouse generation tools - used in the design, cleansing,

transformation, loading, and administration of the data warehouse. Warehouse management tools - database management software

(DBMS) used to manage data in the data warehouse. Warehouse information access tools - used to enable end users to

access and analyse information stored in the data warehouse. Although important, the tool set is not the primary

consideration

Reviewing skill sets

Strong SUN/Solaris experience Less strong Windows Server (at that time - situation now

has changed significantly) Fragmented skills in VB, Pivot Tables, Business Objects,

Cognos and MSBI around the traps More ubiquitous skills in SPSS and SAS through

teaching (SAS), research (SAS & SPSS) and institutional reporting area (SAS)

After review and reports (Gartner, IDC, etc) we chose SAS We are also be able to leverage good students, post grads and

existing skill sets. Training still required

Sent both users and developers of the system on relevant training

Data integration

WebBrowser

MicrosoftOffice

HTTP / Java Servlet / W

ebdavScaleable Intelligent Server Analytical & Business Intelligence

ETL Studio Management Console

Enterprise Guide

Analytics Enterprise Guide

PeopleSoft

PeopleSoft

Access

ODBC / OLE DB

Oracle

Metadata Repository

Integration TechnologiesXMLSAS/SecureSAS 4GL SAS/STAT

LEVEL 3DataMarts

LEVEL 2DataWarehouse

LEVEL 1Staging/ ODS

Enterprise Data Warehouse (EDW) WebServer

Portal

Web Report Studio

IntrNet

Stored Processes

AppDevStudio

Information Maps

ApplicationServer

Stored ProcessesServer

Workspace Server

OLAP Server

MetadataServer

SPD Server

SAS Foundation SAS/ETS

Information Map Studio

WebBrowser

WebBrowser

ETL

Data Connectivity

DataMigration

DataQuality

DataSynchronization

DataFederation

SAS Enterprise Suite

Data generation environmen

t

Data management environment

Analytics, data mining, information

access

clients

Architecture

Client Tier

Middle Tier

ServerTier

SAS ETL StudioSAS OLAP Cube StudioSAS Management ConsoleSAS Information Map Studio

Web Infrastructure Kit Web Infrastructure Kit

Java Servlet Container

Java Servlet Container

webDAVServer

webDAVServer

HTTP ServerHTTP Server SDKSDK

SAS Enterprise GuideSAS Add-In for Microsoft Office

SAS Web Report StudioSAS Information Delivery Portal

Workspace Server Workspace Server

Stored Process Server Stored Process Server

OLAP Server OLAP Server

Metadata Server Metadata Server

SAS®9 Foundation SAS®9 Foundation

SAS/CONNECT Server SAS/CONNECT Server

Supports programming environments

Stores and manages meta data repositories that store data on servers, libraries,

stored processes and user policies

Stores and runs canned programs

Delivers data cubes (pre standardised tabulations)

to enterprise guide or OLAP clients

Super users

End user accessDevelopers

Reviewing Infrastructure

Most vendors have Windows and UNIX solutions (some only windows)

UNSW has strong local capability in UNIX environments especially SUN hardware and Solaris

Belief that UNIX is currently more scalable and fits well into the current managed environment

Architecture needs to handle future growth and the rate of growth not clearly understood

Looked at Solaris 10 containers/zones to create more flexible and scalable deployment

Chosen Infrastructure

SAS Application Tier Mid Tier

Client (Administration)

Client (Ad-Hoc Analysis)

Client (External Customer)

AUTHENTICATION DOMAIN- DefaultAuth


NON-SAS SOFTWARE- Tomcat 4.1.18- Xythos Web File Server 4.0.48- Xythos Darabase- Sun Java Development Kit 1.4.2_05

MS IE 6+PortalWRV

EG4.1WRS

SMCIMS

University of NSW: Sun Solaris SAS Enterprise Intelligence Platform

Sun Fire V490 (UltraSPARC IV)64-bit Solaris 104 CPU, (Dual Core)Memory: ??Mb

~ 20 External Users

~ 20 Internal Users

SAS 9.1.3 Foundation,Enterprise BI Server,Enterprise DI Server,Enterprise Miner,Platform Suite for SAS

Peoplesoft

Adhoc Flat Files~ 5 Internal Users

SAN

SAS Metadata Tier


SAS 9.1.3 Foundation

Zone 2MidTier

Server

Zone 1Application

Server

Zone 3MetaTier

Server

Running the Project

Implementation

and Project Reporting

The big vision versus immediate outcomes (maintaining momentum)

Set up project steering group and identified a number of “quick wins”

Used to quickly showcase capability QW1 – OLAP cube of student load QW2 – Budget report (with forecasting) QW3 – HR staff report made more friendly

Delivered on time and used for senior management presentations and demonstrations to further sell the idea

Used high level plans and different progress reporting styles for flexibility

Project reporting a new way

OperationalSystems Data

Feeds Data&

Applications

Reporting&

Analytics

AccessUser uptake

Project reporting a new way

Infrastructure

Goals/Objectives

ActualProgress

FunctionalityLayers

Outcomes

Showing Scope and Progress

External

HR

Student

Finance

Research

Facilities

Others

DATAGENERATION

DATAMANAGEMENT

INFORMATIONACCESS

CHANNELSLocal

o Unified development environmento 3GLs

o Data movement and replicationo Data management facilitieso Data feeds

o Data feeds

o Relational and dimensional datao Information maps

o Unified data managemento Data cleansingo Performance monitoring and optimisations

o Data security models

o Reportso End-user query, reporting and analysis

o Reportso Data miningo Packaged data marts

o Reportso Spatial information management.

o General authenticated access via webo Developer access via GUI tools

o Specific authenticated access via webo General and broad access via web (info kiosk)o Broader usage GUI tools & Office

o Specialist information services

Data quality process

ExceptionReports &Feedback

1

2

2+

2

The Project Report Card

System Outputs

Total number of reports and reports by data domainInformation maps and report themes (capability)

Organisational Contribution

Financial implication Time saved. Revenue generated. Costs reduced.

Process

Client Satisfaction (CSI) .Ease and convenience of use. Responsiveness to new requests and

requirements Security breaches and information leaks

People

Client Satisfaction (CSI) Data quality culture. More use of decision support data to make

decisions Learning organisation

Reports Capability Financial CSI Other??

Report Card Examples

Hybrid service modelData policy (DBOR, ROR)

Traceability matrix to root cause problems and associated

solutions with progress bar

PIR on business case items

User uptake and adoption.Numbers trained

Development Process Artifacts

High Level Data Acquisition Plans

ETL Flows

Data Schemas

InformationMaps

Rhubarb Rhubarb Rhubarb Rhubarb

Rhubarb Rhubarb

Reporting Requirements

Early screen shots

Achievements

ISSUES ACHIEVEMENTS

Information unavailable or unknown Self service and 40+ standard (user customisable) reports with descriptions and data dictionary export capability (also provided training to GMs etal)

Difficult to measure our real business performance

Produced VC report scoring our performance in key areas and in comparison to other universities.

Project Optimum (Research) Reports

Commencing AUQF and Phase 2 of Optimum (web based access)

Unable to track students/staff through their lifecycle

Created data feeds and reports linking admissions and enrolments

Just completed HR and Student data feeds and first reports

Now adding International data (for campaigns, conversion rates, predictive follow up) and IT service management. data for tracking SLAs

100’s of reports from different systems with errors and difficult to understand

Replaced with self service and 40+ canned reports from trusted data. Introduced data and text mining tools to glean more information from existing data. CATEI survey system saving $1M in annual expenses and being enhanced with text mining capability (automated trend analysis).

Unable to link data from different systems (eg HR, Student, Research, Finance, other) and external sources

Combining data in warehouse for any type of analysis. HR, Student, Medicine, International, Financial, ITSM data is being added. Reports have ability to span across different data domains – eg staff/student/researcher/lecturer. Live link to external UAC data provides 15 years of historical information

Unable to monitor our business performance

With strong internal/external data linkages trend and causal relationships can finally be monitored. We are now ready to provide executive dashboards for current and future defined KPIs and measures.

Early Achievements

Managing different stakeholders from different aspects of the business

Mistake number one Involving too many stakeholders during requirements

gathering and analysis phase == delays

Mistake number two Not involving enough stakeholders during requirements

gathering and analysis phase == turf battles

Major Lesson – understand your stakeholders!!!

Planning for the futureor….The BHAG

A truly integrated view of information.

Timely and relevant to inform and enable effective business decisions.

Meaningful measures of corporate performance and competitive business intelligence.

All aspects of key business lifecycle events are tracked, stored and optimised.

Guest Prospect Applicant Admitted Student Active Student Graduand

Recruitment units GradsStudent AdminAdmissions offices

Administration Com

mun

ityResearch S

tudent

Staff

UNSWsystems

Trustworthy information and reports that are contextual, complete and correct.

The Big Hairy Audacious Goal

Planning for the Future

More realistically it is managing the ongoing project as a product intended to satisfy new and emerging requirements

Planning for the future – “data maturity”(product view)

Optimisation What is the best that could happen?

INTELLIGENCE

Predictive Modelling What will happen?

(What if?)

KNOWLEDGE

Descriptive Modelling Why did it happen? INFORMATION

Integrated and cleansed data, standard and adhoc reports, OLAP

What happened? DATA

RO

I and

Bus

ines

s P

ower

Pro

gres

s

Prioritising Efforts

We can’t wait to complete each row before moving up Lose business interest Miss out on early benefits May never get past the first row!

Three step (VCP) process to define projects – value, complexity, priority Classify the Value of each initiative Classify the Complexity of each Prioritise the highest value lowest complexity

implementations first

Classify valueValueValue

Raw operational data

Cleansed, modelled, understood, integrated data

Data modelled for descriptive analysis

Data modelled for predictive analysis

Data modelled for what-if analysis and optimization

Management Cockpit

Corporate Performance Management Financial

reporting

Product Profitability

Human ResourcesResearch

Competitive Intelligence

Combine financial data with other domains

Link Admission and Enrolment

More detailed student data

Trends reporting

Balanced Scorecard

Analytical CRM

Campaign Management

Target Marketing

Customer Lifecycle Value

Budgeting and Forecasting

Enrolment Forecasting

Business Activity Monitoring

Production Planning

Activity Based Costing

Operational CRM

Data Integration MaturityData Integration Maturity

Complexity to Implement

Corporate Performance Management

Financial reporting

Product Profitability

Research

Combine financial data with other domains


Trends reporting

Balanced Scorecard

Analytical CRM

Campaign Management

Target Marketing

Activity Based Costing

Operational CRM

Production Planning

Business Activity Monitoring



Customer Lifecycle Value

Management Cockpit



Classify ComplexityB

usin

ess V

alu

e

Examples only!

Consider the styles of user

Farmers

Know what they want

Predictable Small queries Expect fast

responses Almost always

gets an answer Rarely find gold

Explorers

Have many ideas Unpredictable Large queries Do not always

get useful answers

Occasionally find gold

Miners

Generate hypotheses

Uses statistical methodologies

and models Seek unknown

patterns

Tourists

Have wide knowledge

Often use metadata

Internet capable ”Look things up”

“Building the data warehouse” Bill Inmon

BI Strategy: Development Plan

2007 2009 2009

Time

Create the “product” road map(reviewed annually)

Pro

jects

Financial reporting

Combine financial data

with other domains


Campaign Management



Trend reporting

Balanced Scorecard

Target Marketing



Depending on the situation, projects may be categorised against

data/problem domains or types of reports etc

Cust

omer

Life

cycl

e Va

lue

Management Elements

Program Steering Group Sets direction Manages Finances Review/approve all business cases

Reference Groups Represented by each major data (problem) domain Feed requirements up into PSG

Data Principles and Policy Identifies major data sources (DBORs) and owners Sets rules and approvals “Ombudsman” role to settle disputes Designated “official reports” (ROR)

Hybrid Service Model IT & Business - each support what they are good at Business provide incremental capability IT provide major capability jumps

Communications – report and celebrate wins regularly

61

Where to now?

Expanding capability for more users and data.Expand scope to lifecycle events such as cohort trackingIntegration with other reporting systems.Formalise governance structures.Continue to leverage skill sets

Original Architecture

SAS Application Tier Mid Tier

Client (Administration)

Client (Ad-Hoc Analysis)

Client (External Customer)



NON-SAS SOFTWARE- Tomcat 4.1.18- Xythos Web File Server 4.0.48- Xythos Darabase- Sun Java Development Kit 1.4.2_05

MS IE 6+PortalWRV

EG4.1WRS

SMCIMS

University of NSW: Sun Solaris SAS Enterprise Intelligence Platform

Sun Fire V490 (UltraSPARC IV)64-bit Solaris 104 CPU, (Dual Core)Memory: ??Mb

~ 20 External Users

~ 20 Internal Users

SAS 9.1.3 Foundation,Enterprise BI Server,Enterprise DI Server,Enterprise Miner,Platform Suite for SAS

Peoplesoft

Adhoc Flat Files~ 5 Internal Users

SAN

SAS Metadata Tier


SAS 9.1.3 Foundation

New Architecture Just Deployed

Immediate Improvements for 2008 Completed performance tuning exercise Bigger faster server and storage to handle the increase in

users and analytic applications SAS upgrade plus Oracle for fast static ad-hoc reporting Increased storage and own NSS copy (max 24 hours old) Additional resources to accelerate progress in reporting Structured lifecycle approach to create assignments for

students of varying skill sets and creating BI courses and qualifications – Institute of Analytics Professionals (www.iapa.org)

Projects to execute or assist Performance management Dashboards, scorecards Cohort and lifecycle tracking

Full operational handover

65

Lessons Learned

What we would do differently

Avoid over-thinking future needs – eg a small windows POC may well have produced similar “selling” outcomes to a POC built with the future in mind

Avoid overly fast tracking internal processes – only comes back to bite later (still need to make them do their part for buy in and ownership)

Communicate more about the project internally and externally (aka “best kept secret in UNSW”)

Ensure solution architectures agreed and approved with a greater number of stakeholders and there is buy-in where it matters

Allocate more resourcing to project management aspects

What we would do the sameUse SUN with SAS for production environment Build in-house skillsUse Hybrid Service Model

Sounds like a success story and in some ways it is BUT in some ways it isn’t Some stakeholders now trying to refocus it to operational and

reduce the vision even though others are won over Some stakeholders still don’t know about the project! Internal and external stakeholders still differing on requirements Constant re aligning and selling even now Resources stretched given enterprise wide view – “n x clients” IT received blame for business generated mistakes

Project longevity relying on a small number of key influential stakeholders rather than perceived as an Enterprise Imperative

Is there any TOGAF in here? Yes!

Phase P – Planning and Principles

Business Goals •Strategic•Operational•Regulatory

Drivers •Information available, monitored, track lifecycles, quality, integration

Overarching Principles•Information should be trustworthy•Information should be boundary less•Information should be easily available

Principles•User driven report generation and customisation•Centralised Dictionary environment•Centralised Business Performance measurement & Monitoring•Centralised tracking of Student / Staff data•Few trusted reports than many erroneous ones•Centralised Data Warehouse


Phase A – Architecture Vision

ScopeLocal and External reporting – DW / BI Architecture – Stage 1HR, Student, Finance – DW / BI Architecture – Stage 2Research, Facilities, Others – DW /chitecture – Stage 3 Architecture VisionUser Self Service for reportsExecutive Dash BoardData Warehouse / Business Intelligence

Phase B – Business ArchitectureBusiness Scenario Workshops – Stake Holder Management, Buy In and Prioritisation of Concerns Business PrinciplesUser driven report generation and customisationCentralised Business Performance measurement & MonitoringCentralised tracking of Student / Staff data Business ArchitectureOrg Chart with new Business Processes if any


Phase C – Information ArchitectureInformation Principles•Centralised Dictionary environment•Fewer trusted reports versus many erroneous reports•Centralised Data Warehouse

Target Information ArchitectureSlide number 22 : Target Environment

Phase D – Technology ArchitectureSlide no. 30 – Chosen Infrastructure

Phase E – Opportunities & SolutionsSolutions Architectures - SASSlide no. 28 – Architecture

Phase F – Migration PlanningSlide no. 35 – Showing Scope & Progress

Phase G - Implementation & GovernanceGovernance Structure to be set up

Phase I – Change ManagementChange management processes to be set up

1. Technical Definition

2. Data Modelling

3. Data Architecture

4. History

5. Issues

6. Selling the idea – whose idea?

7. Real versus Intangible value problem

8. Business Case Mechanisms

9. Tools and Vendors

10. Skill sets

11. Software technology

12. Infrastructure Project Management

13. Development Processes

14. Screen shots of the system

15. Achievements

16. Stakeholder Issues

17. Technical Definition

18. Data Modelling

19. Data Architecture

CONCLUSION – All good stuff but not always the best order and not always the right time = missed stakeholder engagement opportunities

TOGAF is in here – so what is the problem?

20. Finally - THE VISION!21. Strategy

22. Types of Users

23. Future work and techo and architecture stuff

24. Lessons Learned

The value TOGAF would have provided this project………

Focused on the vision earlier and kept this focus Tracked and aligned (changing) business requirements

continually throughout the process (centre piece of ADM) Able to use underlying principles to assist and ensure

people behaviour Share and common vision and SOW through ADM with

continual customer alignment would have reduced these problems

Helped keep the varying kinds of clients and needs by providing the common and agreed principles rules and guidelines by which the architecture and solution continuum is developed and sustained. 72

Conclusion

TOGAF helps you bring out the good work at the right time, working with the business with joint objectives and agreed deliverables

It provides the frameworks and support materials but works equally well with those you develop in house

Higher chance of success!

QUESTIONS?

reflections on alignment with togaf 5 th november 2008 james dawson - consulting enterprise...

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