Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress March 2014

Alan Duncan, Director of Data Governance, UNSW E: [email protected] Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncan

Uncontrolled when printed

Managing for effective Data Governance Delivering value while remaining sane

Alan D. Duncan March 2014



“If- “ Rudyard Kipling

“If you can keep your head when all about you Are losing theirs and blaming it in you, If you can trust yourself when all men doubt you, But make allowance for their doubting too…”



A bit about me.... •  Alan Duncan, Director of Data Governance, UNSW •  21 years Information Management & Business

Consulting –  EDS, KPMG, CPW, Acuma, Pelion, SMS –  Scottish Power, United Distillers, O2, Astra Zeneca,

Carphone Warehouse, Vodafone, Riyad Bank –  Commonwealth Bank, NSW Roads & Maritime

Services, Centrelink, OATSIH, NSW Family & Community Services, CASA, AMSA, FaHCSIA, DAFF, Navy…

•  Information-Management.com “Top 12 on Twitter” •  Best supporting Actor, 2005 Barnet Drama Festival



…and a bit about UNSW.



Agenda

•  Do we need to rethink our Data Governance strategies?

•  Is enterprise-wide Data Management really achievable?

•  What techniques and capabilities do we need to focus on?

•  What skills and personal attributes are needed for success?



PART 1. Do we need to rethink our Data Governance Strategies? Sponsored by Thomas Edison

“The value of an idea lies in the using of it.”



“I object!”

•  “I don’t know what you’re going to do with my data once you have it.”

•  “If I give you my data, you might then ask me to do some extra work to meet your additional requirements.”

•  “You may not interpret the data in the same way that I do.” •  “I’m an expert in this area, you’re not. The data is too

complex for you to understand.” •  “It’s too difficult to get the data out of the system and I’d

need help from I.T.” •  “I don’t have the budget to pay for your requirements.” •  “I’d like to help but I’m just far too busy.” •  “I know there are flaws in the data, but it’s good enough

for my needs. You might criticize me for the errors.” •  “Management may ask additional questions and hold me

to account for the work I’m doing”.

7

“I’m not interested in preserving the status quo; I want to overthrow it.”



Information as a Service: “True Facts” Identify measurable and targeted Business Outcomes

Why do we need information? For whom? What will we do differently?

Establish DG Operating Model

Who is accountable? By what processes?

Execute Activities & Tasks

How do we deliver? Who does the work?

Confirm the Information Holdings & Gaps

What do we need to provide? (Content + Context)

Implement DG/IMCC Services Catalogue:

What core capabilities do we need? “When it is obvious that the goals cannot be reached, don't adjust the goals, adjust the action steps.”



Is “Open Data” a good thing?

http://www.ted.com/talks/tim_berners_lee_the_year_open_data_went_worldwide.html

9



Outcomes of change of mindset

•  Stimulus to improve data quality •  Consistency of data definitions •  Openness and trust •  Transparency & accountability •  Opportunity value •  Proactive publication and Open

Data vs. “Need to know”

10

“Publish and be damned!”

http://www.ted.com/talks/tim_berners_lee_the_year_open_data_went_worldwide.html



Summary: Rethinking Data Governance

11

•  Control, structure, discipline & compliance? OR Advocacy service & information broker?

•  Intimate understanding of business goals & processes

•  Engagement, diagnosis & facilitation •  Understand & articulate the meaning of

data, in context •  Coach, mentor and advocate •  Highly visible point-of-access •  Self-service Information Portal •  Conduit, communicate & co-ordinate •  Leadership & direction •  “Info as a Product”

“The art of government is to make two-thirds of a nation pay all it possibly can for the benefit of the other third.”



Data Collection in a State Transport Infrastructure Authority, sponsored by Alfred North Whitehead

PART 1: Case Study

“The art of progress is to preserve order amid change and to preserve change amid order.”



Data collection for road transport

•  Monitoring & management of the road network

•  Optimise traffic throughput •  Plan for infrastructure

investment, maintenance •  Incident management •  Plus strategic shift from “asset

engineering” to “customer-centric” culture



Problem: current data can’t meet modern needs •  Continuing growth in traffic •  Some “in-road” sensors over 30 years old

–  Poor data quality –  Classification by “Big, Medium, Small” –  No telemetry: up to 3-month lag

•  Sparse distribution of existing sensors –  OK coverage in major urban areas –  Few (if any) in rural areas

•  Devices do “Count” only –  Speed not measured

•  Temporary “spot” surveys leave gaps in the record (or duplicate data!) •  Over 1000 new sensors would be required

–  New in-road devices approx $50K each to install (as part of road build/upgrade) –  Piezoelectric “tube” devices easily damaged, poorly installed –  Radar devices inaccurate in the wet



Solution: augment with GPS vehicle tracking data

•  8000 fleet vehicles with “always on” GPS



Benefits

•  Travel time benchmarking •  Flow management •  Congestion “pinch point” analysis •  Long-term traffic forecasting •  Road safety speed zoning •  Incident early-warning predictive

alerts



Customer service: real-time information updates



PART 2. Is enterprise-wide Data Management & Governance really achievable? Sponsored by Confucius

“When it is obvious that the goals cannot be reached, don’t adjust the goals, adjust the action steps.”



Information Management Strategy Drivers

Informa-on Management

Strategy

Informa-on & Data needs

Organisa-onal Strategic Direc-on

DG&IM Best Prac-ces



Information Use Cases •  Based on our current understanding of business needs, the following classes of

Information Use Case are identified •  Detailed Requirements Analysis should be conducted on a project-by-project basis

to explore any detailed Use Cases within each class •  Not all detailed Use Cases need to be defined ahead of time •  Solutions should be flexible to accommodate new and changing Use Cases

Structured data

repor-ng

Strategic Intelligence and Data Mining

Publish content to a community

Execu-ve briefings

Educa-on, Training, Learning

Search for content previously created

Records Management, Compliance &

Audit

GIPA & Privacy

Responses

Ability to publish Filtering/screening/valida7on of what gets published

Feedback loop, measure of usefulness & con7nuous improvement Shared understanding (IT & Business)



To centralise or not to centralise?



Identifying Owner & Stewards

Typically, there are significantly more unconscious Owners and Stewards

All key stakeholders in the Assets driven by an

informal structure

Business pain is felt but has no means of consistent resolution

Conscious Owners and Stewards

Responsibilities blurred and lack of understanding of the relationship

and how it should work Owners are accountable for driving up

the level of consciousness



Summary: Information Management for the whole Lifecycle

Plan Construct,

Create, Acquire

Commission, Organise,

Store Access Use Assess Maintain Retire

• Rigorously evaluate the decision at the earliest stages of a proposal before investing in new or replacement assets.

• Manage the procurement whether it be a construction, purchase, lease or service

• Minimise the cost and risk of ownership with effective maintenance strategies and procedures.

• Manage operational costs.

• Evaluate the level of investment in assets to identify functional or physical obsolescence, financial viability, re-use opportunities and areas of unacceptable risk.

• Consult with stakeholders and plan for disposal of assets.

• Examine all options to achieve service delivery objectives and meet business requirements.

Information Owner

Chief Steward (CDO) & IMCC (cross-‐func:onal, cross domain)

Business Process

Business Process

Business Process

Business Process

Business Process

Data Stewards

An Enterprise approach to Information & Data Management requires formal organisational

processes and controls that define the rules, roles and responsibilities for information

ownership, stewardship and associated service capabilities.

Objective is to achieve explicit assurance for an agreed level of information quality

(broadest definition) and links to business value, based on the explicit capture,

formalisation and application of business rules.



Devising the information strategy for a major retail bank, sponsored by the Vice-President of Retail Banking

PART 2: Case Study

“We must allow him to draw his sword….”



Information Strategy for Saudi Retail Bank - Scope

•  “Define Bank Data Management Best Practice - Production of a definition of Data Management Best Practice appropriate for the needs of the Bank.”

•  “Education of Bank resources – education in definition of the Information Environment, Information Architecture and how Data Management fits within this.”



IM is business critical for Retail Banking

•  Effectiveness –  e.g. Marketing the right products to the right customers every time,

outperforming the competition •  Efficiency

–  e.g. lower effort in servicing accounts due to error reduction •  Cost

–  e.g. IT savings in application development and maintenance as a direct result of unambiguous information definitions

•  Flexibility –  e.g. Rapid and controlled ability to adapt without disruption

•  Risk –  e.g. Better lending decisions, more easily established Compliance,

Trust & Reputation



Why wasn’t the Bank already doing this?

•  Financial benefits are large but... –  Hard to quantify (Indirect, Distributed) –  Hard to realise (Contingent) –  Hard to track (Causality)

•  In contrast, the costs are significant and exactly quantified

•  => Conventional investment appraisal is hard •  => Many organisations fail to invest, and lose

competitive advantage



Strategy Development plan – 2 phases Milestone Activity Date Description

DMD Services Solution Phase 1A started in June 17th, 2006 Assessment & Discovery

Discovery Sessions From June 18th to 26th, 2006 Over 20 interviews with key stakeholders to evaluate Bank against four major areas

of the i-Environment model. IVM Theory,Class Training June 27th, 2006

Given to DM Staff

P Senior Management Feedback Workshop for DM Solution From July 10th to 12th, 2006 Business, Technical & Architecture

Informatica Extraction Tools Training Course From July 16th to 19th, 2006 Given to DM Staff

P Detailed Plan for Phase 1B July 13th, 2006 Delivered Project Schedule P DMD Assessment Report July 30th, 2006 Delivered Document

DMD Services Solution Phase 1B started in July 15th, 2006 Detailed Solution Design

Batch Integration Inventory - Technical Analysis Sessions From July 15th to Aug. 16th, 2006 Over 25 interviews with Bank systems technical consultants, reviewing the current

batch integration architecture. P DMD Staff Profiles August 12th, 2006

Delivered Document P Batch Integration Inventory - Findings &

Recommendations August 19th, 2006 Delivered Document

P Riyad Bank Executive Management DMD Best Practice Design Presentation September 6th, 2006

This Presentation P DMD Standard Policies for Data Ownership, Data Quality,

Data Access & Data Definition Processes September 9th, 2006

Upcoming Milestone

P Implementation Plan for Phase II September 9th, 2006 Upcoming Milestone



Strategic issues – two problems, not one

•  Data problem –  Accurate systematic capture, integration, distribution, storage of

granular data items

•  Information problem –  The common and pervasive definition, understanding, agreement of

business rules enabling consistent interpretation of data

•  These issues are linked •  Both need to be addressed by the Bank •  The Information problem in particular is a business issue, not I.T. issue



Key Recommendations

•  RECOMMENDATION 1: Confirm the IM Vision –  Share, debate & refine the vision at Exec Level –  Syndicate vision across the bank management & staff

•  RECOMMENDATION 2: Expand “Data Management Dept” & Position as “Information Management Dept” –  Agree IMD Charter –  Confirm IMD organisational structure –  Locate IMD within IM Governance and Bank Organisation –  Confirm organisational relationships with customers & other departments

•  RECOMMENDATION 3: IM Transformation Programme –  Set targets & formulate according to best practice –  Plan Programme –  Establish Programme & Project Governance –  Release resources to the programme



Bank IM Vision

Best Prac7ce Informa7on Management

Info at the heart of the business

Single trusted View of informa-on

Self-‐service approach for Business Info Access

An IM Func-on that challenges the Business

Build the future while suppor-ng the present

Perf. Mgnt. suppor-ng Business Strategy

Common Informa-on Model

Business data analysis, not data collec-on



Bank IM Charter •  VISION: To be the information core competency centre and champion

within the Bank, promoting the effective use of high quality information and data to maximise business value.

•  DUTIES: Within the context of the Bank’s overall business Vision, Strategy and Operations, the IMD is responsible and accountable for: –  Definition and assurance of Information Management Policy –  Enterprise steward - looking after Enterprise Information Model & Metadata

repository –  Business Intelligence centre of excellence –  Information Management Services



Implement Branch/CSR Data Capture Improvements

Review Branch/CSR Data Capture

Ini-al cycles of Data Cleansing & DQ Improvement

IMD Communica-ons

Ongoing coaching & skills transfer

Appoint/recruit ini-al IMD resources

Specific IM Skills Training Formalise IM key docs

Implementation Plan

Month 1 Month 3 Month 4 Month 5 Month 6 Month 7 Month 2

Implement IMD Capability

Mobilise IM Governance

Bank procedural changes WRT IMD Impacts

IMD Quick Wins

IM Training (IT Bas)

Implement Data Masking

Data Classifica-on Project

IMD Setup & Mobilise

Design Data Masking



“Data quality is like a public toilet. We all want to use it, but nobody wants to clean it.”

Vice President of Retail Banking



PART 3. What techniques and capabilities do we need to focus on? Sponsored by Carl Sagan

“I try not to think with my gut. If I‘m serious about understanding the world, thinking with anything besides my brain, as tempting as that might be, is likely to get me into trouble.”



Information Asset Management

Owners

AssetManagement

Tools

Governance

Admin

Experts

User Community

InformationAsset

Steward

OwnersOwners

The “Information Asset Community”

Directives

WMSAIRS

Example High Level Data Systems & Flows

Version 1.0

General

Mandatory

Core

Corporate Support

Automatic One-way Relationship

Automatic Two-way Relationship

Manual One-way Relationship

Manual Two-way Relationship

External

External System

AIRSInterchange

AUSSAR

AME

Cyber Exams

ATSL

CLIC

SDR

Publishing System

GMEL

TRIM AD

FRLIWeb Control

Mgmt System (WCMS)

FMIS

STI

ESIR

Comweb

ATO Business

Portal

Inventory Mgmt

System (IMS)

EPK

ComBIZ Online

ProMaster

FCAT

CalumoCBMS(DoFD)

COMCARE

Thomas Logistics

SM7

HRMS

HRFlex

DTAR/OTAR

APEX

AOD Audit

AOD Case Mgmt

SystemTimelog

eRooms

Symbion Health System

API Upload System

ChangePoint Testing System

MRS

ASSP

AWS

AFD

ASIR

ADMS

TrackerATOG Job Register

AOC Surveys

Industry PaymentsCompensation Payments

Financial Actuals

Financial Actuals

Employee Expenses / Adjustment

Journals

Salary Payments

Cash Payments

Payroll (Salary)

Cash Payments / Organisation Info

Human Resources Finance

Surveillance/Audit/Reporting/Tracking

Workflow and Online Collaboration/Service Delivery

Service Delivery

Service Delivery

Service Delivery, HR & Finance, Agreements, Permissions, Aerodromes, Participants, Aircraft

Medical Examinations

Medical Exams

Surveillance /Audits/ Reporting/ Tracking

Alcohol and Other Drugs

Surveys / Surveillance

Events/Occurrences, Aircraft, Aerodromes

Surveys/ Certifications

Examinations

Work Orders

Surveillance /AuditsAerodromes

Aircraft

Events/Occurrences, Aircraft, Aerodromes

Defects/Events/Occurrences, Aircraft, Aerodromes

Exemptions Database

Alternative Means of

Compliance (AMOC)

Exemptions

AMOC / Exemptions

Human Resources – Flex

Time

Human Resources - Travel

Physical Inventory

Audit Data

workflow / service delivery

workflow / service delivery

Contacts – Ind, Org’s Contacts – Ind, Org’s

ExaminationsMedical

Examinations

Search and Rescue

Surveys

Human Resources – Time

Aircraft Equipment Finances

MMELBaseline/Minimal Equipment

Medical

PAWS

Retain

Details of Operators

Incidents

Applications / Permissions

Trending

Workflow

MAAT

Permissions / Change of Status

Permissions / Change of Status

Service Log

Alternative Means of Compliance (AMOC)

Dangerous Goods

Dangerous Goods

Content

Inventory

FTTO

FTNS

Individual Flight Data

Organisational Flight Data

Human Resources – Time

Aircraft

Individuals/ARNS

Payments

HR - TimeCash Receipts

Reconcile Invoice against

Flown Hours

Surveys / Surveillance

Enterprise Data Warehouse

CASA Internet

Airports

Landings/ Take Offs

Data

Mandatory

Core

Corporate Support

External

Business Process

Surveillance/Audit/

Reporting/Tracking

Bank Data File

PAYG payments, Salary

payments, and Superannuation

payments.

External Superannuation

Companies

Cash Payments

Superannuation Contributions

Suppliers

Remittance Advice

300+ Access Databases

Contacts

Airspace

Organisational

Human Resources

Aircraft

Permissions Info Asset Register (inventory) System Interfaces map

“Science is organized knowledge. Wisdom is organized life.”



Analytics & Business Intelligence

37

“The alchemists in their search for gold discovered many other things of greater value.”

•  “Traditional” BI (reporting & ad-hoc analysis) •  Data Mining •  Statistical modelling •  Data visualisation •  Textual analytics

•  What questions do we want to answer? •  What questions can we answer with the data

we’ve got? •  What other data would we need? •  What does the data tell us we should be

asking?



“Big Data” is a fact of life •  Three, four, five, six “Vees”?! •  A lot of data (Tb/day) •  Streaming data (monitoring, flow-of-control and

alerting analytics) •  Inference from semi-structured data (Twitter,

Facebook) •  Synthesise insight from millions of pages of text •  Programmatic analysis for specific scenarios (hard in

SQL) •  A disruptive catalyst to put information at the top of

the organisational agenda •  Not just about the data! Business scenarios are key •  Beware the Vendors!



All of the data, all of the time

•  Granular, forensic history •  Modern data management & analytics solutions can make “all

of the data, all of the time” a reality •  The bigger challenge is that the business community is not

analytically skilled enough to navigate the data and draw meaning from it…



Get on the Cloud

40

… but security, privacy considerations are heightened.

In principle, it’s just another place to store data….



Compliance & statutory considerations

•  Freedom of Information Act 1982 (Cth) •  Freedom of Information Amendment (Reform) Act 2010

(Cth) •  Privacy Act 1988 (Cth) •  Privacy Amendment (Private Sector) Act 2000 •  Privacy Amendment Act 2012 (Cth) •  Privacy Amendments (Privacy Alerts) Bill 2013 (Cth) •  State Records Act 1998 (NSW) •  Government Information (Public Access) Act 2009 (NSW) •  Privacy & Personal Information Protection Act 1998

(NSW) •  Health Records & Information Privacy Act 2002 (NSW) •  NSW Government Guide To Labelling Sensitive

Information 2011 (NSW Financial & Services)

41

But is “compliance” a motivator?

“All I want is compliance with my wishes, after reasonable discussion.”



Collaboration Culture

42

•  A general willingness to share information

•  Co-operative, communicative & collegiate OR control, coercion & criticism?

•  The “whose data is this?” cue •  Call-to-action? •  Accountability & measurement?

“Respond intelligently even to unintelligent treatment.”



Data Models & Metadata Management

Metadata Repository

Master Data Repositories UNSW Core Systems

Information Asset

Register

Physical Instantiations

Physical Layer

Logical Layer (Transition)

AnalyticalDB Models

Cubes

Conceptual Layer (Business)

Physical MessagesFormats

DWHDB

HRDB

Student Admin etc...

OperationalDB Models

Reference models

Data Subject AreasData Entities

Data Attributes

Information Concepts

Business Content Business Rules Data

Business Data Element

Domain Values

Endorsed Standards for Content

Business Constraints

Business Measures

Master data models

Classification Entity

Hierarchies

Mappings

Business Rules Definitions

Business Constraints

Business Measures

Core SystemsMDM

Metadata M

anagement Process

Information M

odel Managem

ent Process

Information Alliances: Data O

wnership & Stewardship Process

MDM Processes

Related Data Governance Processes

Application Logical Data

ModelsLogical

MessageSchemas

MDM Data Model

Systems Data Models

SOA/EP MessagesG/L

Application Logical Data

ModelsLogical

MessageSchemas

AnalyticalDB Models

Cubes

Physical MessagesFormats

OperationalDB Models

Business Glossary

Conceptual Model: Groupings & Relationships

Data Elements, Definitions, Aliases, and Security

Data Domains

Enterprise Information Model

“Do not quench your inspiration and your imagination; do not become the slave of your model.”



Classification schemes and taxonomies

•  Taxonomy: method for classification of things •  Classification Scheme: grouping of kinds of

things, based on their characteristics •  Information Model: representation of concepts,

relationships and semantics

•  For an Enterprise approach, each should relate to the other in an ordered manner



Information Disconnect

Careless data entry & lack of validation

Teams use different IT systems

?

Organisations change rapidly

Teams have different ways of reporting data

Month Region

Multiple codes exist for the same thing

IC_STR

Data is in different Formats

Overlapping subsets in different places

Multiple, inconsistent master data

Data Quality

“Get your facts first, then you can distort them as you please.”



Accurate vs Inaccurate Data

Right Representation

Wrong Representation

Right Value Wrong Value

Valid Values Invalid Values

Missing Values

Accurate data Inaccurate data

“Valid” does not equal “Correct”



Accuracy

Completeness Consistency

Integrity

Validity

Origins

Compliance

Storage Retention

Transmission Distribution Ownership

Use

Security Performance

Uniqueness

Accessibility

Flexibility

Timeliness Inherent

Pragmatic Data Quality Dimensions

•  DQ Dimensions are the characteristics against which we measure quality.

•  May be categorised into two types: –  Inherent Quality –  Pragmatic Quality



Ongoing&BAU&opera.ons&for&Data&Governance&&&Enterprise&Informa.on&Management

Review&and&refine&Opera.ng&Model,&Processes,&Standards

KPT1305IData&Governance&

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Pilot-Info-Alliance-7-Student-LifecycleORG007

DG&Founda.ons DG&Enablement DG&Embedding Complete&Rollout

KPT1304&I&Consistency&of&Data&Processing&Methods

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Data-Owner-Role

PEOP001

Data-Interfaces-F/work-(cf-NextGen)

PROC003

Confirm-DG-Framework

STRAT007

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Data&Governance&Culture

Phase&2& Phase&3& Phase&4&

UNSW&Data&Governance&Strategic&Roadmap&2013/14

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Systems

Data&Governance&

People&&&Skills

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DG&StrategyEndorse-DG-Charter-(Vision-&-Principles)

STRAT002

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STRAT005

DG-"Cheat7Sheet"

CUL002

DG-Org-ModelORG003

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ORG005

Pilot-Info-Alliance-7-Staff/HRORG006

Iden)fy-Data-Owners-&-Stewards

PEOP003

Core-DG-PolicyPOL001

DG-Standards-Framework

POL002

ToRs-for-Info-Alliances

ORG002

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SYS004

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STRAT015

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Strategic Planning & Benchmarking

“One day Alice came to a fork in the road and saw a Cheshire cat in a tree. Which road do I take? she asked. Where do you want to go? was his response. I don't know, Alice answered. Then, said the cat, it doesn't matter.”



Summary: Data Governance increases understanding, utility & value of information

Information System

Data Quality Management(Profiling, root-case analysis, issues tracking & resolution)

Data Modelling(Consistent, inter-operable data structures & semantic meaning)

Information Requirements & Business Analysis

(Identification & traceability of business definitions & rules)

Information Asset Register (Catalogue of data holdings)

Information SystemInformation System

Information System(s)

Data Set



Applying the Telemanagement Forum solution models, Sponsored by Mae West

“I’m no model lady. A model’s just an imitation of the real thing.”

PART 3: Case Study



B2B Telco Provider (UK): the problem

•  Poor lead-times for provisioning new orders •  Orders fulfilled incorrectly •  High levels of customer credits Caused by: •  Multiple business systems •  Limited levels of integration •  Silo operations



“Order to Bill” solution approach

•  Unified business processes •  Enterprise Service Bus for integration •  Data warehouse & BI (visibility & monitoring)

•  Needed an Enterprise Information Model – quickly! –  IBM model was expensive! – Company were already members of TM-Forum…



TMForum: communications industry trade association

www.tmforum.org



TMForum standard models



TMForum SID (Standard Information Definition)



Mapping SID to key process groups



Mapping SOA APIs using the cannonical model

Enterprise Service Bus (ESB)

Order Management System

Business Process Framework

Supplier Management System

Service Ac-va-on Fault Management Ra-ng/Billing

Network opera-ons & monitoring

EDW



Data schema (example)



Monitoring outputs



Outcomes

•  Average order times down from 3 weeks to 4 days

•  Significant reduction in cancelled orders •  Approx 75% reduction in customer credits

•  Project was originally to take 6 months; it took over a year…



PART 4. What skills and personal attributes does a Data Governance Manager need? Sponsored by Mark Twain

“To succeed in life, you need two things: ignorance and confidence.”



Forethought

•  Think about both current and future demand –  Cf. Google philosophy to “keep everything” –  Every click, every font change…

•  Imagination, innovation, entrepreneurialism •  Don’t be inhibited by the current scope of existing data

“Forethought we may have, undoubtedly, but not foresight.”

Source new data; Collec-on & Integra-on; Prepara-on &

Quality. Demand-‐oriented

Inbound requests for specific requirements

Data Factory (“push”)

Product-based delivery (“pull”)

Need both “push” and “pull” modes for evidence-

based decision-making



Expectations Management •  Finding data that makes an impact •  Having data for the problem at hand •  Trusting the data to guide your

decision •  Justifying pre-determined answers •  Setting inappropriate goals •  Not having the right data tools •  Not thinking about value

“Two things are infinite. The universe and human stupidity. …and I’m not so sure about the universe.”



“That propaganda that is good which leads to success, and that is bad which fails to achieve the desired result. It’s not propaganda’s task to be intelligent, it’s task is to lead to success.”

Communication

•  Listening skills – e.g. active listening

•  Facilitation •  Consulting & advisory •  Coaching & mentoring



“Whosoever desires constant success must change his conduct with the times.”

Change management



Summary: Personal attributes

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Data Owner

Data Steward



PART 5. Conclusions & Final Thoughts Sponsored by Terry Pratchett

“It’s still magic even if you know how it’s done.”



Conclusions: Information Excellence EIM Framework: Enterprise Information Management Framework describes each aspect of an organisations information management state, provides a baseline of maturity against best practice and a framework of business transformation to your aspirational information management state. Provides linkage and balance between business,/IT, and human/technical aspects of EIM.

Information Governance

Information Security

Information Asset Mgmt

Metadata

Ownership & Stewardship

Information and IM Strategy and

Planning

Information and IM Quality Mgmt

Information Asset Classification

Intellectual Property

Reporting design AnalyticsInformation

Security Policy and Governance

Asset Management

Human Resources Security

Management

Knowledge Transfer

Data Mining

Data WarehousingBusiness Intelligence

Information IM Workforce

Management

Information and IM Risk Management

Registration

Data Modelling

Data management

Data Integration

Data Cleansing

Data Capture

Data Migration

Data De-duplication

Record Keeping

Knowledge Management

Information Asset Access and Use Management

Privacy Publishing

CopyrightPhysical and

Environmental Management

Communications and Operations Management

Information Security Incident

Management

Access Management

Information system

acquisition, development and

maintenance management

Compliance Management

Information and IM Policy, Principles and Architecture

Information and IM Governance Processes

Meta Knowledge

Search and Discovery

ExchangePricing

Licensing and Rights

Management

Assess and Accessibility

Redress Mechanisms Data Quality and Integrity

Data Conversion & Transformation

Record Management Archiving Conservation and Preservation

Record Creation and Capture

Digital Continuity

Collection Management

Retrieval and Access

Retention and Disposal

Business Continuity

Enterprise Informa7on Model IM Solu7ons and Technology IM Policies

Organisa7on and People Data Governance Informa7on Culture

IM Processes

Business Processes

DB Models

Definitions, Derivations, Decision Rules, Execution Rules

IM Governance Process

IM Stewardship Process

Technical MetaData Management

Logical Model

ETL SpecsReport

DefinitionsSemantic Specs

Data Marts

ETL Cubes

SemanticLayer

Standard R

eport Library

ETLOperational System Staging Warehouse

Conceptual Model

Logical Model

Physical Model

Capture & FormaliseRequirements

& Rules

Impact Assessment

& Implementation

MetadataLineageImpact

Etc.

MetadataCollection

Asset Alignment/Mgt

Architecture Changes

Architecture Mgt

A holistic, data-centric approach to Information Management & Data Governance, addressing both human and technical factors in both Business and IT domains



Recap: Seven Transformational Levers •  Strategy (are the vision, objectives and overall direction for the

organisation clearly articulated and well understood? Do Business Strategy, IT Strategy and Information Strategy align?)

•  Culture (is the desired behaviour exhibited throughout the organisation?) •  Organisation (are the organisational structures appropriate to executing

the Strategy?) •  People (is the workforce properly skilled and motivated?) •  Process (do all business processes align with and support the Strategy?) •  Policy (are the organisational controls appropriately defined and applied?) •  Systems (does the infrastructure of IT Systems provide the right support

for all key business processes?)



Calls to action

•  WHAT: Identify 3 key issues (data related) that need to be addressed within your business, and the IM capability areas that support these

•  WHY: Outline the business outcomes/benefits that you would derive from addressing these issues

•  HOW: Map these changes to the Seven Transformational Levers of the Enterprise IM Framework

See example worksheet



Decision Request / Action Plan - 6-step method

1.  REQUIREMENT: State one key issue (data related) from the strategic list. What is needed?

2.  PROBLEM/INHIBITOR: what is currently preventing your organisation from doing something about it?

3.  OUTCOME: What specific benefits would your organisation derive if this was addressed?

4.  SOLUTION: What new product or capability is needed to deliver the requirement stated in (1)?

5.  PLAN: Outline the step-by-step action plan & timescales that will deliver the outcome.

6.  DECISION REQUEST: In THREE bullet points, state what specific support you need from your Sponsor in order to get things started

–  “I need you to agree to… 1, 2, 3.” –  e.g. budget, resources, new policy, key communication

See example worksheet



FINAL THOUGHTS: perspectives on Information Management

Applica-ons/Systems Architecture (How do I access it?)

Data Architecture (Conceptual, Logical, Physical – What does it mean?)

Informa-on Asset Catalogue (What have we got? In what context? For whom?)

Info Management & Data Governance Roadmap (When will it be delivered?)

IM Business Case (Why do we want it? How much will it cost?)

Business Process Models (How do we do it?)

Business Services Framework (What do we do?)

Business Lifecycle (Why do we do it?)

Business IM Capability & Transforma7on (Who is accountable?)

IM Capability

Assessment (What do we want/

need?)

IM Service Capability /

IMCC

(Who delivers & supports it?)



Blog links…

1.  RETHINKING OUR STRATEGIES: http://informationaction.blogspot.com.au/2012/07/information-as-service-what-is-it-and.html

2.  IS EIM ACHIEVABLE?: http://informationaction.blogspot.com.au/2013/11/to-centralise-or-not-to-centralise-that.html

3.  CAPABILITIES: http://informationaction.blogspot.com.au/p/the-information-management-tube-map.html

4.  SKILLS & QUALITIES: http://informationaction.blogspot.com.au/2013/10/normal-0-false-false-false-en-au-ja-x_29.html

5.  MAGIC: http://informationaction.blogspot.com.au/2014/03/now-thats-magic.htm

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Intellectual curiosity Skeptical scrutiny Critical thinking

http://www.informationaction.blogspot.com.au/

@Alan_D_Duncan

http://www.linkedin.com/in/alandduncan

Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress March 2014

Data & Analytics

Transcript of Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress March 2014