Making Enterprise Data Quality a Reality - · PDF fileMaking Enterprise Data Quality a Reality ... mistakes • NHS England pays each GP £136 for each patient on ... linked to strategic

Making Enterprise Data Quality a Reality…Room for Improvement?

Nigel TurnerPrincipal Consultant EMEA, Global Data Strategy Ltd.

Enterprise Data & Business Intelligence Conference EuropeThursday 10 November 2016

3

Scene Setting & Introductions

Global Data Strategy, Ltd. 2016

What we will NOT be doing today (1)

TALKING CONSTANTLY



THE CORPORATE SALES PITCH



THEORETICAL & ACADEMIC APPROACHES


What we WILL be doing today (1)

PRACTICAL APPROACHES & TOOLS



HANDS ON – A CHANCE TO PRACTISE



HAVE SOME FUN


• What differentiates enterprise Data Quality / Data Governance from traditional project based DQ / DG approaches

• How to take the first steps in enterprise DQ / DG

• Applying a DQ / DG Framework

• Making the case for investment in DQ and DG

• How to deliver the benefits – people, process & technology

• Real life case studies

• Practice case study – getting enterprise DQ / DG off the ground in a hotel chain

• Key lessons learned and maxims for success

Tutorial Objectives


Self Introductions • Your role• Company and country• Main expectation from today

ACTIVITY


Nigel Turner - role & credentials

• 35 years experience in IT & Business Strategy; 26 years in Data Management

• Initiated and coordinated BT’s enterprise wide information quality improvement programme

• Subsequently ran a 200 strong Information Management & CRM practice serving BT’s global business customers

• Since leaving BT in 2010 co-authored Institute of Direct Marketing online qualification in Data Management

• Also VP of Strategic IM at Trillium Software, Principal Business Consultant at IPL & Principal IM Consultant at FromHereOn

• Now Principal IM Consultant EMEA at Global Data Strategy

FavouriteHobby


Some organisations I have worked with on enterprise DQ / DG…

14

Context & Drivers for Enterprise Data QualityWhy bother?


DATA

ARCHITECTURE

MANAGEMENT

DATA

DEVELOPMENT

DATABASE

OPERATIONS

MANAGEMENT

DATA SECURITY

MANAGEMENT

REFERENCE &

MASTER DATA

MANAGEMENT

DATA QUALITY

MANAGEMENT

META DATA

MANAGEMENT

DOCUMENT & CONTENT

MANAGEMENT

DATA

WAREHOUSE

& BUSINESS

INTELLIGENCE

MANAGEMENT

DATA

GOVERNANCE

› Enterprise Data Modelling

› Value Chain Analysis

› Related Data Architecture

› External Codes

› Internal Codes

› Customer Data

› Product Data

› Dimension Management

› Acquisition

› Recovery

› Tuning

› Retention

› Purging

› Standards

› Classifications

› Administration

› Authentication

› Auditing

› Analysis

› Data modelling

› Database Design

› Implementation

› Strategy

› Organisation & Roles

› Policies & Standards

› Issues

› Valuation

› Architecture

› Implementation

› Training & Support

› Monitoring & Tuning

› Acquisition & Storage

› Backup & Recovery

› Content

Management

› Retrieval

› Retention

› Architecture

› Integration

› Control

› Delivery

› Specification

› Analysis

› Measurement

› Improvement

The DAMA DMBOK Wheel


Data governance – alternate definitions (1)

© Copyright 2014, Trillium Software, Inc. All rights reserved.

“Data Governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.” (DAMA International)

“Data Governance is a quality control discipline for adding new rigor and discipline to the process of managing, using, improving and protecting organizational information.” (IBM Data Governance Council)


Data governance – alternate definitions (2)

© Copyright 2014, Trillium Software, Inc. All rights reserved.

“Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” (Data Governance Institute)

“Data Governance is the formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset.” (MDM Institute)


Data Quality & Data Governance – the interdependency

Retail

What is Data Quality?Data that is demonstrably fit for business purposes

Data Governance

Provides the means to deliver

Data Quality

Drives the need for

What is Data Governance?A continuous process of managing and improving data for the benefit of all stakeholders


• Why is good Data Quality important:

• To companies and organisations?

• To individuals?

ACTIVITY


How it can go wrong…


A recent data failure (1) - Retail

21

HP ZBook 17 G2 Mobile Workstation


A recent data failure (2) - Energy

22

Metric: Billing Unit – Cubic Meters of Gas Imperial: Billing Unit – Cubic Feet of Gas

• Several UK gas suppliers confused the two meter types • Outcome:

• Some customers overcharged by 130% per annum for 15 years• OFGEM (UK Regulator) ordered repayment of overcharges


A recent data failure (3) – Government

SPOT THE DIFFERENCE BETWEEN THESE TWO UK COMPANIES……

• UK Government Companies House confused the two • Published that Taylor & Sons Ltd. had been shut down• In fact, Taylor & Son Ltd. had ceased trading

• Outcome: • Companies House had to pay £8.8 million as it ‘irreparably destroyed’ the successful business


A recent data failure (4) - Health

24

ECRI Institute survey 2103 – 2015

• Found 7,613 cases of patient identification errors• Led to many misdiagnoses & incorrect treatments• 9% of affected patients harmed • 2 patients died as a result• Legal action taken in many cases

National Health Service England Report 2016

• Analysis showed 5% of GP patients records in error• Faults included obsolete records, duplicate patient

records, and inaccurate records • Led to several cases of misdiagnosis & treatment

mistakes• NHS England pays each GP £136 for each patient on

list • Large overpayment to GPs from NHS in time of

financial strain


The industry impact of poor DQ – the evidence

In UK in 2013 0.18% of online orders could not be delivered because of poor address data –that’s 1.4 million orders

On average, organizations waste 15-18% of budgets dealing with datainaccuracies

The US economy loses

$3.1 trillion a year because poor data quality

56% of UK Marketing organisations say managing DQ is a ‘significant challenge’


‘Big’ Data – Volumes

90% OF ALL DATA HAS BEENCREATED IN THE LAST 2 YEARS

AVERAGE BUSINESS DATA VOLUMES DOUBLE EVERY 1.2 YEARS

2.5 QUINTILLIONGRAINS OF SANDON EARTH

7.5 QUINTILLIONBYTES OF NEW DATA CREATED EVERY DAY


The New Landscape of the Digital Business

27


The Current State of Data Quality in Digital Business

28

Analytics Data Quality Correcting poor data quality is a Data

Scientist’s least favorite task, consuming on average 80% of their

working daySource: Forbes 2016

Big Data LakesLack of effective Data Governance and the absence of shared data definitions

and metadata cited as main impediments to the success of Data

Lakes Source: Radiant Advisors 2015

Self-Service Analytics Data Scientists & BI professionals

spend 50% - 90% of their time cleaning & reformatting data before

using itSource: DataCenter Journal 2015

Digitization 71% of interviewees expect

digitization to grow their business. But 70% say the biggest barrier is

finding the right data; 62% cite inconsistent data

Source: Stibo Systems 2015

Sub-optimization of Data Assets

Only 0.5% of all data is currently analyzed although 33% of data is seen to have

potential analytics value

Source: Dataversity 2015


Balance Innovation with Foundation

29

For Digital Transformation Success, it’s Important to Balance Digital Innovation with Foundational Technology.

Digital Innovation• Big Data • IoT• Artificial Intelligence

Foundational Technology• Master Data Management • Data Quality• Architecture & Design


Poor data quality & governance: impact on companies & organisations

ECONOMIC: REVENUES, COSTS, PROFITS

LAW & REGULATION

BRAND, REPUTATION & CUSTOMER LOYALTY


Poor data governance: impact on individuals

ANNOYANCE REPUTATIONAL DAMAGE

DEATH TELL THEIR FRIENDS

32

An Enterprise DQ / DG Strategy

What’s stopping me?


What problems or opportunities are changing or driving Enterprise Data Quality & Data Governance in your organisation / industry / geography?

ACTIVITY


19% of data respondents have no Data Governance initiative

20% of organizations have Data Governance programs that are over 5 years old

40% of respondents have Data Governance programs that are 3-5 years old

21% have Data Governance programs that are 1-2 years old

SOURCE: DELOITTE CONSULTING SURVEY AUGUST 2013

BUT

26% have ‘well established’ Data Governance programmes

63% are still trying to establish a formal Data Governance organization or

gain business backing for an informal team

Data Governance – the reality


The paradox – bridging the gap


Why might enterprise Data Quality & Data Governance initiatives fail?

ACTIVITY


Enterprise DQ & DG – the dangers


Traps for the unwary – why Enterprise DQ & DG can fail

Lack of business leadership and commitment Failure to link DQ / DG to organizational goals and

benefits Failure to focus on the data that really matters Giving people data responsibility but not equipping

them to succeed Placing too much emphasis on data monitoring and not

data improvement Thinking new technology alone will solve the problems Forgetting DQ / DG must embrace all who use data

across an organization Not delivering business value early and regularly


Why it can be hard – systemic failure

• Human error

• No data accountability

• Poor training

• Internal politics

• Denial

• Data capture & U/D failures

• Multiple data silos

• Interface errors

• Poor process design

• Process failures

• Flawed goal setting

• No agreed data standards


Why it can be hard - the horizontal data flow

Sales Operations Despatch Finance

CUSTOMER DATA

PRODUCT DATA

FINANCE DATA

EMPLOYEE DATA


The traditional approach to DQ improvement

• Part of business and / or IT application area (e.g. CRM, Marketing, Finance) identifies DQ problem

• Starts an initiative to address the problem

• Puts together a team to address

• Team analyses causes and potential strategies to address

• Selects methods & tools appropriate to the task

• Delivers the project

• May create localised data governance structures to maintain data quality

• May move on to another DQ problem in same area…but unlikely to seek out and improve DQ elsewhere in the organisation


Implications of this approach

• DQ improvement & DG may become stovepiped

• Each initiative derives own solutions, methods & tools

• Makes expansion of DQ and data governance more difficult

• Impedes ability to tackle enterprise wide DQ – requires horizontal E2E approaches

• Reuse of solutions difficult

• Knowledge, skills and experience not shared across the wider enterprise

• DQ remains a niche activity, specific to particular business areas or supporting applications

• Cost of delivery of DQ solutions sub-optimised


• Layers of interconnected complexity

• All problems cannot be solved so need to focus on highest value projects, linked to strategic organisational goals

• Large number of stakeholders

• Need for Board & Senior Executive support & involvement

• Need for cross-organisational team working o Between business & IT

o Across business units

• Data quality change requires business & cultural transformation

• Need to involve everyone, so communication is key

What’s different about enterprise wide DQ and data governance?

Global Data Strategy, Ltd. 2016 44

“Know from where you came. If you know from where you came, there are absolutely no limitations to where you can go.”

James Baldwinauthor & poet 1924 - 1987

The importance of origins and destinations


CURRENT MATURITY / READINESS FOR

ENTERPRISE DQ / DG AS IS

ENTERPRISE DQ / DG IS BUSINESS AS

USUAL TO BE

Enterprise DQ & DG – plotting your journey


Your own organisation – how mature are you?

Where should you start?


• Complete a Data Quality maturity questionnaire for your own organisation

• We will use the results to assess the DQ maturity of all organisations represented

• Feel free to add your organisation’s name, or keep anonymous if you prefer

ACTIVITY


The DQ Maturity model

Level 1:AWARE

Level 2:REACTIVE

Level 3:PROACTIVE

Level 4:MANAGED

Level 5:OPTIMISED

CULTURAL MATURITY

Source: META Group


Stage 1:

AWAREStage 2:

REACTIVE

Stage 3:

PROACTIVEStage 4:

MANAGEDStage 5:

OPTIMISED

• Recognise some DQ problems, but manage on an ad hoc,

mainly manual basis

• Focus on specific data cleanses; no overall approach

• Departmental approaches to data cleanse emerge

• DQ across the enterprise generally poor, but often

unquantified or unrecognised

• The value of good DQ is recognised across enterprise

• DQ improvement focused downstream, not at source

• DQ software starts to be used

• DQ improvement recognised as a corporate issue

• Large scale DQ programmes / projects underway

• DQ policies and best of breed tools in use

• Information seen as a key enterprise asset

• DQ issues managed at source

• DQ becomes business as usual

The DQ Maturity Model: Characteristics of Development


Level 1:AWARE

(35%)

Level 2:REACTIVE

(45%)

Level 3:PROACTIVE

(15%)

Level 4:MANAGED

(5%)

Level 5:OPTIMISED

(<1%)

CULTURAL MATURITY

Source: META Group

INDUSTRY PROFILE

The DQ Maturity Model – Industry Outcomes


Level 1:AWARE

(x%)

Level 2:REACTIVE

(xx%)

Level 3:PROACTIVE

(xx%)

Level 4:MANAGED

(x%)

Level 5:OPTIMISED

(<x%)

CULTURAL MATURITY

Source: META Group

THIS GROUP’S

PROFILE

The DQ Maturity Model – this group


• What do these results suggest for enterprise DQ / DG initiatives?• How could awareness of your organisation’s DQ / DQ maturity help you in your efforts?

ACTIVITY

53

How to Make Enterprise DQ & DG a Reality

Trying it out for yourselves (1)


Case Study


Legal Rider

• This case study is based upon a purely fictional hotel business

• Any resemblances to real hotel chains are unintentional and purely coincidental


• Hotel, Casino and Nightclub chain

• Based in USA but worldwide expansion

• 20,500 employees

• Turnover of $1.2 billion per annum

• Recently appointed new CEO

The Case Study


• Fictitious but realistic

• Safe environment

• No wrong answers!

The Case Study


• Organise into team(s)

• You are a team created by the Gwesty CIO to propose better ways of managing data across the Group

• Ultimately he will want you to present your proposals to the Gwesty Executive Committee

• Read the case study • Apply the Data Maturity questionnaire to Gwesty

• Agree 3-4 key points and considerations resulting from the Maturity Assessment

ACTIVITY


Level 1:AWARE

(35%)

Level 2:REACTIVE

(45%)

Level 3:PROACTIVE

(15%)

Level 4:MANAGED

(5%)

Level 5:OPTIMISED

(<1%)

CULTURAL MATURITY

Source: META Group

INDUSTRY PROFILE

The DQ Maturity Model – Industry Outcomes


Implications of DQ / DG Maturity Assessment

• Quick (but imperfect) test of how ready your business is for enterprise wide DG & DQ

• Be aware that:

• In larger organisations different segments of the organisation are probably at differing stages of data quality maturity

• It is a gross simplification, so in determining your specific approach be aware of the unique cultural context of your organisation

• As a rule it suggests:

• Low maturity (Unaware / Aware / Reactive) organisations are very unlikely to readily grasp the need for and support enterprise approaches such as Enterprise Data Governance, Master Data Management etc. More groundwork needed!

• Enterprise approaches will get most traction in Proactive / Managed organisations

• Optimised have probably already achieved enterprise data quality… if they exist!

• You can make it happen… BUT• You need a thorough & systematic approach• You need to apply a proven enterprise DQ / DG Framework


Break (10:30 – 10:45)

62

The components of an Enterprise DQ / DG

Strategy:Making it happen


What do YOU think are the key components of successful enterprise

DQ / DG?

ACTIVITY


Data Governance barriers: one approach

OPTION 1 ADDRESS BARRIERS

REACTIVELY


Data Governance barriers: a better approach

OPTION 2 ANTICIPATE BARRIERS

PROACTIVELY


Applying a structured Data Governance Framework

DG VISION & STRATEGY

BUSINESS GOALS& OBJECTIVES

TOOLS & TECHNOLOGY

ORGANISATION&

PEOPLE

PROCESSES&

WORKFLOWS

DATA MANAGEMENT &

MEASURES

CULTURE &

COMMUNICATIONS

KNOWN / SUSPECTED DATA CHALLENGES


Implementing enterprise DG – applying the Framework

Maturity Assessment

Current Status

Vision & Strategy

Org. & People

DM & Measures

Processes & W/flows

Culture & Comms

Tools & Tech.

Activity Roadmap

Overall Strategy

Business Justification

DQ

Visio

n

Bu

sine

ss D

rivers

Desired State


Enterprise DQ / DG Framework Components – 1

The rationale for Enterprise DQ / DG and its alignment with thestrategic and operational goals of the organisation

Formal organisational roles & responsibilities for data and thematurity of the organisation to support successful DQ / DG

Assesses how business processes preserve or degrade data and what hidden costs of failure are embedded as business as usual processes. Also specifies data definition & improvement processes & workflows


Enterprise DQ / DG Framework Components – 2

Evaluates the state of existing data management, both business & IT, and how data is monitored across the organisation, including the presence of key performance measures for data

Assesses how effectively Data Governance aims, processes andstructures are promoted & embedded via messaging andeducation across the organisation

Specifies the platforms and tools needed to support enterprise DQ / DG and how these are / should be deployed within a defined & coherent data architecture

70


Strategy:Vision & Strategy


Vision & Strategy

“You have to work hard to get your thinking clean to

make it simple. But it's worth it in the end because once you get there, you can

move mountains.”

Steve Jobs 1955 - 2011


DQ / DG Framework:Vision & Strategy – key questions

• Is there a clear understanding of the strategic goals of your organisation and the need for enterprise DQ / DG?

• How does your organisation rely on data – now and in the future?

• What impact are data problems currently having on your organisation?

• Do you have a DQ and / or DG policy?

• What are the overall expected benefits of better DQ / DG?


Vision & Strategy: Suggested main deliverables

73

BUSINESS CASE – What are the predicted benefits and costs?

STRATEGY – How are we going to do it?

MOTIVATION – Why does the organisation need to do this?

2 / 10 / 30 MINUTE PRESENTATIONS – How will we

communicate this?


The Motivation Model

• There is benefit in formally documenting the motivations for the project• Commonly-agreed upon guidelines for project tasks & deliverables

• Reminder of “why we’re doing this” - neutral arbitrator for disagreements

• Components of the Motivation Model include:• Corporate Mission: describes the aims, values and overall plan of an organisation

• e.g. To be provide the most comprehensive, customer-driven online shopping experience in the market

• Corporate Vision: describes the desired future state

• e.g. To transform the way consumers purchase goods through social-media-driven connections.• External Drivers: What market forces are driving this initiative?

• e.g. Cultural shift to online retail• Internal Drivers: What internal pressures or initiatives are key for this project?

• e.g. Disparate systems require need for an integrated view of customer

• Project Goals: high level statement of what the plan will achieve• e.g. To improve customer satisfaction with over 90% satisfaction rating in 2 years

• Project Objectives: outcome of projects improving capabilities, process, assets, etc. • e.g. To link consumer purchase history with social media activity

74

Common Set of Goals & Guidelines


Sample Business Motivation Model

75

Corporate Mission Corporate Vision

Goals & Objectives

To provide a full service online retail experience for art supplies and craft products.

To be the respected source of art products worldwide, creating an online community of art enthusiasts.

Artful Art Supplies ArtfulArt

C

External Drivers

Digital Self-ServiceIncreasing

Regulation Pressures

Online Community & Social Media

Customer Demand for Instant Provision

Internal Drivers

Revenue Growth

Targeted Marketing360 View of

Customer

Brand Reputation Community Building

Revenue Growth

C

Accountability• Create a Data Governance

Framework• Define clear roles &

responsibilities for both business & IT staff

• Publish a corporate information policy

• Document data standards• Train all staff in data

accountability

C

Quality• Define measures & KPIs for

key data items• Report & monitor on data

quality improvements• Develop repeatable

processes for data quality improvement

• Implement data quality checks as BAU business activities

C

Culture• Ensure that all roles

understand their contribution to data quality

• Promote business benefits of better data quality

• Engage in innovative ways to leverage data for strategic advantage

• Create data-centric communities of interest

• Corporate-level Mission & Vision• May already be created or may

need to create as part of project

• Project-level, Data-Centric Drivers• External Drivers are what you’re

facing in the industry• Internal Drivers reflect internal

corporate initiatives

• Project-level, Data-Centric Goals & Objectives

• Clear direction for the project• Use marketing-style headings

where possible

76

How to Make Enterprise DQ & DG a Reality:

Trying it out for yourselves(2)


• Produce short Corporate Mission & Corporate Vision statements for Gwesty

• List External and Internal drivers for better DQ and DG across the Gwesty organisation

ACTIVITY


Enterprise DQ / DG business cases – making the case

78

LOOKING AHEAD

LOOKING BACK LOOKING

BOTHWAYS


Business Cases: seven steps to success

STEP 1

STEP 2

STEP 3

STEP 5

STEP 6

STEP 7

STEP 4

Identify the problems

Identify stakeholders

Gather evidence

Quantify the costs of failure, risks & potential

benefits

Draft the case

Socialise the case

Finalise and present

POTENTIAL ITERATIONS


Step 1 - Identify the problems

• Start with acknowledged issues

• Think about your company or department’s key strategic goals • How does data quality impact on these?

• Talk to key people across the problem area• All grades and types – producers & consumers of data

• Uncover and analyse / revisit DQ problem(s): • Business problems & impact• Potential or actual solutions• Benefits – financial & other


Step 2 – Identify Stakeholders

• Develop a stakeholder map

• Use this to:

• Identify a potential senior executive champion

• Identify other key stakeholders to be involved

• Start to gain cross-organizational support

• Engage stakeholders who represent all affected areas – both managers and front line people

• Tap into existing organisations and structures in the business and try to use them, e.g. process improvement forums, programme boards etc.


Step 3 - Gather Evidence

• Stakeholder Workshops• Various techniques - Systems thinking, Rapid etc. • Must have a clear purpose, and attendees empowered to make

decisions

• Interviews• Use a pre-prepared list of questions to ensure structured capture and

analysis • Always try to interview in pairs and ideally face to face

• Issue / opportunity logs• These logs may already exist• Can be added to or revisited

• Data profiling & analysis • Useful to do before workshop or interview sessions• Will drive the key question – “So what?”


Step 4 - Quantify the costs of failure, risks & potential benefits

• Successful business cases are always backed up with relevant & provable facts

• Potential costs & benefits generally fall into 4 categories:• Economic / Transformational • Customer Experience / Satisfaction / Loyalty• Legal & Regulatory Compliance• Brand Awareness & Reputation

• Focus on current costs of failure and not on the ‘value’ of good data

• Some potentially useful tools and techniques include:

Force FieldAnalysis

Fishbone Diagrams

Lean DQ Approaches

Root CauseAnalysis

BenefitsAnalysis

Net Present Value (NPV)


Example – Actual summary and benefit analysis (partial)

84

DATA DOMAIN

BENEFIT TYPE DESCRIPTION EXPECTED REVENUE INCREASE / COST SAVING

Year 1 Year 2 Year 3

CUSTOMER COST REDUCTION

BONUS ABUSE REDUCTION

£125,000 £125,000 £125,000

COST REDUCTION

EMAIL M/K COST REDN

£10,000 £10,000 £10,000

COST REDUCTION

REDUCTION IN 3RD PARTY ADDRESS CLEANSE

£50,000 £50,000 £50,000

SALES RISK AVOIDANCE

AUTOMATEDREGULATORYREPORTS

£20,000 £20,000 £20,000

REVENUEINCREASE

CROSS-SELLINGOPPS

50,000 50,000 50,000

TOTAL £255,000 £255,000 £255,000


Step 5 - Draft the Case

• Ensure you comply with your company standards for business cases / case studies

• Where possible obtain copies of other successful cases and emulate their style

• Use business language and avoid technical jargon

• If DQ improvements have already been made produce internal case studies and include

• Use your stakeholders to review the draft business case / case study


Step 6 – Socialise the Case

• Data quality improvement is a collaborative process so socialisation of data quality business cases is critical

• The best cases for Data Quality improvement are usually better driven “bottom up”• Secure support from those who will implement

improvements BEFORE approaching senior managers and seeking their support

• Use these supporters to help socialise and sell the case and break down potential barriers and blockers


Step 7 – Finalise and present the case

“If you cannot SELL your business case in seven PowerPoint slides and in under 20 minutes or less you don’t have a case”

(CEO of Global Manufacturing Company)

• Ensure that final cases are:• Short, simple, visual and impactful • Capable of delivery across the organization• Focused on business benefits and not technical features

• Before presenting:• Practise and memorise your key points • Think about potential objections

• When presenting: • Show personal passion, confidence & commitment• If you don’t believe in it, they won’t!


Making the business case – further tips

88

Include Market

/ Competitorperspective

Get expert help

Include‘Do nothing’

option

Seek Incremental

funding


Creating a DG / DQ Strategy (<5 pages)

89

• Business Drivers• Strategic goals of organisation / business unit• Importance of fit for purpose data • The current situation:

• Problems and business impact

• Unrealised opportunities

• Competitive threat

• Objectives• Overall aims of Enterprise DQ / DG• High level programme plan & early deliverables • Anticipated benefits

• Approaches • Policies, models and methodologies • Communications strategies


Lunch (12:15 – 13:15)

91


Strategy:Organisation & People


DQ / DG Framework:Organisation & People – key questions

• Who are the key data stakeholders within and outside your organisation?

• Who are the primary data producers, consumers & modifiers?

• Are individuals formally accountable for data ownership?

• Are employees trained in good data management practices?

• Are there any channels through which data shortcomings can be highlighted and investigated?


The 5 Basic Models of Data Governance

Process Centric

Contingent / Blended

SystemsCentric

Data Centric

OrganisationCentric


Definitions of the 5 Basic Models

Model Description

Process Centric Process owner(s) become(s) the data owner for all data created, amended & deleted by the business process for which he / she is responsible

Systems Centric System owner(s) become(s) the data owner for all data created, amended & deleted by the IT system for which he / she is responsible

Data Centric Business appointed FT or PT roles accountable for improvement of key data domains wherever created or used across an organisation, e.g. Data Stewardship

Organisation Centric Business appointed FT or PT roles accountable for improvement of key data domains on the basis of departmental boundaries (e.g. Marketing, Finance) or geographical locations (e.g. Region, Country, Territory)

Contingent / Blended There is no single best model for data governance, either when initiating data improvement activities, or as Business As Usual. The best model is dependent on the type of data and the circumstances of each initiative, at each stage of maturity

94


What are the pros and cons of each model of Data Governance?

ACTIVITY


Process Centric Data Governance

Process owner(s) become(s) the data owner for all data created, amended & deleted by the business process for which he / she is responsible

PROS

• Processes create data

• Poor data a good measure of broken

processes

• Data improvements need process

change

• Works well where processes extend

beyond org. boundaries

• Facilitates business support

• Funding sits with process owners

CONS

• Partial data views, where data is used

/ shared between / across processes

• Objectives may clash between

process & data improvement

• Can result in a process-centric view of

data which may not solve long term

problems

96


Systems Centric Data Governance

System owner(s) become(s) the data owner for all data created, amended & deleted by the IT system for which he / she is responsible

PROS

• Works well where reference / master

data sources exist or are being built

• Easier to obtain IT support for DQ

initiatives

• Facilitates introduction & integration of

embedded data toolsets

• Systems owners have ready access to

budget

CONS

• No end to end view of data problems,

particularly sources & impacts

• Solutions tend to be IT centric, rather

than business centric

97


Data Centric Data Governance

Business appointed FT or PT roles accountable for improvement of key data domains wherever created or used across an organisation, e.g. Data Stewardship

PROS

• Have potentially complete view &

governance of data across the

organisation

• Consistent data policies easier to

implement & enforce

• Experienced stewards develop highly

specialised skill sets / SME

• Works well where reference / master

data sources exist

• Simplest and clearest governance

model

CONS

• Only tends to work well in smaller,

DQ mature organisations

• How to fund?

• Are people available with the right

skills & aptitudes?

• If PT, stewardship often seen as a low

priority activity

• Less effective where a majority of

data used is owned outside the

organisation

• Danger that others feel data problems

exclusively owned by steward

98


Organisation Centric Data Governance

Business appointed FT or PT roles accountable for improvement of key data domains on the basis of departmental boundaries (e.g. Marketing, Finance) or geographical locations (e.g. Region, Country, Territory)

PROS

• Likely to have a close understanding

of the data in their area & the impact of

data problems

•Good access to senior department and

geographical executives

• Appropriate in a widely dispersed

global organisation

• Understanding of local language and

culture

CONS

• May lead to fragmented approaches

to Enterprise DQ / DG

• Data problems arise and need to be

addressed horizontally across an

organisation

• Might encourage cross-departmental

or cross-geography divisions

99


Contingent / Blended Data Governance

There is no single best model for data governance, either when initiating data improvement activities, or as Business As Usual. The best model is dependent on the type of data and the circumstances of each initiative, at each stage of maturity.

CONS

• Can create added complexity and

uncertain responsibilities

• Requires strong leadership skills to

work

100

PROS

• The particular context is unique to

each organisation so this model allows

flexible combinations of governance

models

• Reflects the fact that DG as a change

programme & as BAU demand different

approaches


An example Enterprise Data Governance organisational structure

• Executive • Policy & process• Escalation & prioritisation• Conflict resolution

• Management & Ownership • Resolution of data issues• Enforcement of policy• Coordination of resources

• Working Group • Understanding business processes• Investigating issues raised• Building business rules and

performing data analysis

Executive Group

Management Group

Working Group

SMEsBusiness Analysts

Data Analysts

Data Governance

Board

Responsible Exec

Data Steward

101

102


Strategy:Data Management & Metrics


DQ / DG Framework:Data Management & Metrics – key questions

• Has critical / important data been identified, defined and analysed?

• Have data models been built – conceptual / logical / physical?

• Has the relationship between business processes and data been mapped?

• Are data shortcomings known, measured & recorded?

• Are there are formal standards & rules specifying how data should be managed and improved?


The Importance of High-Priority Data Elements

104

You can’t improve and govern all your data!!

Launch of New Product – Marketing Campaign requires better customer information

Customer Product

Region

Vendor

Partner

Identify Key Business Drivers

Filter Data Elements Aligned with Business

Drivers

Focus Governance & DQ efforts on Key Data

Targeted Project(s) to show Short-Term

Results


Creating a Business Glossary

• Definitions are as important as the data elements themselves

• Many data-related business issues are caused by unclear or ill-defined terms

105

The Importance of Definitions

What do you mean by “customer”?

We’re calculating “total sales” differently in each region!

Sales is using a different “monthly calendar” than

Finance.

How are we defining a “household”?

What’s an “equity derivative”?

What’s a “PEG ratio”?

“API” as in “Application Programming Interface?” or “American Petroleum Institute”?

What’s the difference between an “ingredient” and a “raw material”?


Data Management & Measures: Example Data Dashboard

Source: Trillium Software

107


Strategy:Processes & Workflows


DQ / DG Framework: Processes & Workflows – key questions

• Do business process design and operations management take data needs into account?

• Are there any specific data management / improvement processes in place?

• Are there issue and workflow management processes to address data problems?

• Has there been any analysis of the efficiency and effectiveness of how data is managed within operational business processes?

• How does the business and IT interact to manage data improvement?


Enterprise DG / DQ: Typical Processes & Workflows

109

Business Strategies &

Goals

Key Data Objects & Attributes

Data Ownership & Stewardship

Data Definition Business Glossary

Management

Data Problem Capture & Reporting

Data Improvement

Continual Data

Improvement


Business processes: LEAN thinking can help…

..identify the “hidden factories” in your organisation

111


Strategy:Culture & Communications


DQ / DG Framework:Culture & Communications – key questions

• Has the importance of data been communicated across the organisation? Is there a data communications plan?

• Is the value of good data management understood and championed by senior managers?

• Do all employees and third parties receive data awareness and improvement education and training?

• Are there communication channels for communicating best practice in data management?

• Are there internal success stories that could be used to promote better data management across the organisation?


How would we feel….?

113


How NOT to communicate Data Governance….

“Data Governance will define personal & organisational accountabilities in a manner that leverages checks and balances between business and information technology teams as well as employees who create and collect information, those who are accountable for managing & organising it, those who rely on it to perform their managerial and operational functions, and those who are responsible for introducing and enacting standards and compliance with regulatory and legal requirements. Data Governance will also support proactive and reactive Change Management activities for reference and metadata values and the structure & utilisation of master and reference data and metadata.”

Client Problem:He could not get

colleagues to support

his Data Governance

initiative…

I wonder why???


So if you want to communicate effectively ….

“I have a dream that my four little children will one day live in a

nation where they will not be judged by the

colour of their skin but by the content of their

character.”

Dr. Martin Luther King

28 August 1963

“Going forward, my futuristic vision is to create a society where the capabilities of my four junior members will not be determined

and evaluated entirely on the criterion of their ethnicity but on

the alternative basis of an egalitarian objective

performance meritocracy where proficiencies, aptitudes and

competencies are assessed and appraised objectively.”

AVOID (DATA) JARGONKEEP IT CLEAR & SIMPLE!


Jargon – how to avoid it

• Adopt the principles and practices of good communication:• Never use jargon & long words when simple language will suffice• Consider the audience & prepare – use appropriate language &

approaches • Don’t assume people know what you are talking about – check with

them• Listen actively and pick up & respond to verbal and non-verbal signals• Use pictures & diagrams if you are conveying a complex story• Be concise and focus on the main messages

• Validate any materials you produce (white papers, blogs, proposals, presentations etc.) with non-specialists • ‘The Karen Test’ • Aim to simplify, not complicate

• Whenever possible, use the language of business, not the language of data management • Profit, customer service, revenue, costs, productivity, brand etc.

116

117


Strategy:Tools & Technology


DQ / DG Framework Framework:Tools & Technology – key questions

• Is there a coherent data architecture in place to define and guide how data is captured, processed, stored and used?

• What primary IT systems and platforms are used to store and process key data?

• Do design gateways exist to ensure data needs are taken into account in new & modified platforms?

• What specialist data management tools are currently in use?

• What metadata (data about data) is captured and stored?


DQ / DG – potential toolset

• Issue and case management

• Workflow

• Data glossaries / Data dictionaries

• Data modelling

• Data analysis / profiling / auditing

• Data quality improvement

• Data mining & analytics

• Data reporting & dashboards

120

Enterprise DQ / DG Strategy:

Creating the Action Roadmap


CURRENT MATURITY / READINESS FOR

ENTERPRISE DQ / DG AS IS

ENTERPRISE DQ / DG IS BUSINESS AS

USUAL TO BE

Enterprise DQ & DG – creating the Roadmap


Example Maturity Assessment

Description + - RAG

Vision & StrategyStrong recognition of the need for DG No clear alignment between DG and the

goals of the organisation

Organisation & PeopleWidespread recognition that ownership of data is required

DG is not seen as business as usual therefore there is a lack of awareness

Culture & Communications

Access to shared platforms to help communicate DG messages

No communications plan or ownership of DG communications

Processes & Workflows Elements of DG methodology in place in parts of the business

No overarching and consistent approach to DG

Data Management & Metrics

Some validation of data formats Insufficient focus on verification of data

Tools & TechnologyDistributed data sources allow user flexibility and independence

Complex, disjointed and unplanned infrastructure


Example DQ / DG Framework Output:Summary Heat Map

Vision & Strategy Organisation & People

Culture & Communications

Processes &Workflows

Data Management & Metrics

Tools & Technology

Priority Level Description

1 – High Structure or strategy required to realise Data Governance capabilities are not yet in place so requires high priority action to develop them to enable the Framework to meet the requirements

2 – MediumThe foundations or part of the required structure or strategy are partly in place but require further development to enable the Framework to meet the requirements

3 – LowThe capability is already in place and only requires minor actions to enable the Framework to meet the requirements

Scoring Methodology


Comparative DQ / DG Maturity Summary

OrganisationDQ / DG Maturity Assessment

Comparable Organisations

• Organisation X has good DQ / DG awareness and pockets of good practice

• BUT data estate is highly complex and challenging

• However RAG maturity scoring is better overall than many comparable organisations

50%42%

8%

60%30%

10%


Getting it Right –the Key Features of an Effective DQ / DG Roadmap

125

ALIGNED Directly connected to Business Drivers

ACTIONABLE with clear activities & milestones

EVOLUTIONARY to meet changing business needs & new technology

UNIQUE to the specific organization


Making it happen: Creating the roadmap – summary hints & tips

Understand current & future business drivers of your

organisation

Learn the lessons & refine the DQ / DG Foundation

Build a DQ / DG Vision & associated business case and socialise – think big,

start small

Expand & adapt to other business areas… and eventually across the

enterprise

Present the case for action –increment financial & resource

demands

Assess how these depend on a good data foundation

Identify the critical data areas and focus on these (e.g. Customer,

Product, Billing etc.)

Capture current data problems and their impact (Economic / Legal & Regulatory / Brand & Reputation)

Build the DQ / DG Foundation & pilot in one area – turn a

problem into a project


Roadmap – Implementation phases

Launch Pilot Roll Out

Create clear vision for DG & DQ

Develop the Framework & Roadmap

Secure resources

Refine the Framework & Roadmap

Deliver the pilot

Measure & validate projected benefits

Refine implementation plan

Stage roll out

Measure & refine


Iterative enterprise DQ / DG

DATAGOVERNANCE

DATAQUALITY

IMPROVEMENT

IMPROVEMENTCYCLES

DG DRIVERS & DATA PROBLEMS

IMPROVED DATA

EVOLVING BAU ENTERPRISE

DATA QUALITY & GOVERNANCE

LAUNCH THE DG / DQ FOUNDATION


An enterprise approach to DG & DQ improvement


• How useful / relevant would this approach be in your organisation?• What barriers would you need to overcome to apply it?

ACTIVITY

131

Case StudiesWhat’s worked in the

real world?


Enterprise DQ / DG: putting it into practice • Every Enterprise DQ / DG Roadmap is unique


Network Inventory Improvement in BT Wholesale

Sub-programme of overall BT wide Enterprise Information Improvement Programme

BT Wholesale identified many problems with mismatches in billing and physical / logical inventory

Focused on a range of network inventory areas: PSTN Private circuits Broadband Virtual private networks etc.


Data profiling – reusable approaches

BILLING

SALES

INVENTORY 1

2

34

5

6

7

1. Matched on all 3 data sources – no action

2. In Sales & Inventory, but not billed – investigation needed (revenue leakage?)

3. In Billing & Inventory, but no Sales record – investigation needed (omission?)

4. In Sales & Billing, but no Inventory record – investigation needed (omission?)

5. In Sales only – investigation needed (cancelled sale not cleared?)

6. In Billing only – investigation needed (Customer billed in error?)

7. In Inventory only – investigation needed (obsolete inventory record?)


The Benefits

• 40 projects completed over 5 year period

• Main benefits included:• Improved customer experience • Revenue recovery• Reduced process failure costs • Lower capital expenditure (CAPEX) - existing inventory returned to use• More efficient exploitation of existing network inventory assets • Faster migration to new network inventory systems

• Total benefits to BT Wholesale £400 million (€486 million)


British Gas

• For the consumer energy sector Big Data and Smart Meters are transforming the ways of doing business and interacting with customers. • Moving away from traditional data use cases of metering & billing.

• Smart meters allow customers to be in control of their energy usage.

• Control over energy usage with connected systems

• Custom Energy Reports & Usage

• Smart Billing based on usage times

• As energy usage declines, data is becoming the true business asset for this energy company.• Monetization of non-personal data is a future consideration.

• While the Big Data Opportunity is crucial, equally important are the traditional data sources

• New Data Quality Tools in place for operational and DW data

• Data Governance Program analyzing data in relation to business processes & roles

• Business-critical data elements identified and definitions created

Business Transformation via Data


Data-Driven Business Evolution

137

Data is a key component for new business opportunities

New Business Model

• Consumer-Driven Smart Metering

• Connected Devices, IoT• Proactive service monitoring• Monetization of usage data

Traditional Business Model

• Usage-based billing• Issue-driven customer service

More Efficient Business Model

• More efficient billing• Faster customer service

response• More consumer information

re: energy efficiency, etc.

Databases Big DataData

Quality

Data

Governance

Metadata Management


Defining Key Business Data ElementsFocusing on the 120 or so data items that really matter to the business

Evaluate CommunicateInvoke Act

Identify required Data Definition(s)

Group related Definitions

Identify Stakeholders

Socialize with key stakeholders

Conduct initial impact assessment

Draft initial Data Definition

Conduct full impact assessment

Obtain & review approvals

Build profiling & monitoring rules

Update metadata locations

Notify all stakeholders

Complete Data Definition process


Chartered Institute for Personnel & Development

• As a leading professional development & certification organization, customers/members and customer service are critical to the success of the organization.

• The corporate goal was to move to more modern, online processes• Online, Community-based member services

• Centralized CRM system

• Automated processes

• In order to reach this goal, a Data Governance initiative was implemented to improve data quality and streamline IT processes• Existing systems and processes had developed in an organic, ad-hoc manner over the years resulting in:

o Duplicate member records

o Incomplete or incorrect member records

o Wasted time and money from IT resources working to rectify bad data

• Both the business and IT had to work together to deliver Data Governance and so tackle data quality in a more systematic and sustainable way

139

Data Governance & Data Quality critical to member management


Data Governance for Data Quality Improvement

• Processes were put in place for both• Data Governance

• Data Improvement

• Tools were selected to help automate manual processes• Data quality

• Data profiling

• Both business and IT stakeholders were involved in governance• Identifying key data elements

• Defining business rules & standards for data

140

Best Practices defined for Data Governance & Data Improvement

DATA GOVERNANCE

DATA IMPROVEMENT

• Data Standards• Data Capture• Data Cleansing

• People• Processes• Policies• Communication

141

How to Make Enterprise DQ & DG a Reality:

Trying it out for yourselves(3)


ACTIVITY

• As the first step in preparing this case, create a Motivation Model for the Gwesty Group

• Use the Motivation Model as the basis of a 5 minutes (maximum) presentation to the Gwesty Executive Board

• You may either present the Motivation Model you’ve completed or use it as input to other materials

• Decide who will present it

• We will all act as the CEO & Board

• Prizes for winning team…


Sample Business Motivation Model

143


Goals & Objectives

To provide a full service online retail experience for art supplies and craft products.

To be the respected source of art products worldwide, creating an online community of art enthusiasts.

Artful Art Supplies ArtfulArt

C

External Drivers

Digital Self-ServiceIncreasing

Regulation Pressures

Online Community & Social Media

Customer Demand for Instant Provision

Internal Drivers

Revenue Growth

Targeted Marketing360 View of

Customer

Brand Reputation Community Building

Revenue Growth

C

Accountability• Create a Data Governance

Framework• Define clear roles &

responsibilities for both business & IT staff

• Publish a corporate information policy

• Document data standards• Train all staff in data

accountability

C

Quality• Define measures & KPIs for

key data items• Report & monitor on data

quality improvements• Develop repeatable

processes for data quality improvement

• Implement data quality checks as BAU business activities

C

Culture• Ensure that all roles

understand their contribution to data quality

• Promote business benefits of better data quality

• Engage in innovative ways to leverage data for strategic advantage

• Create data-centric communities of interest

• Corporate-level Mission & Vision• May already be created or may

need to create as part of project

• Project-level, Data-Centric Drivers• External Drivers are what you’re

facing in the industry• Internal Drivers reflect internal

corporate initiatives

• Project-level, Data-Centric Goals & Objectives

• Clear direction for the project• Use marketing-style headings

where possible


Break (14:45 – 15:00)


Example Gwesty Motivation Model


Data Goals & Objectives

GWESTY GROUP

Internal Drivers

Customer Loyalty Scheme

Improve Revenues

Increase Room Occupancy

Better Targeted Marketing

Reduce Operational Costs

Demonstrate Value For Money

C

INNOVATION

• Agree a forward looking data strategy to align with the business strategy

• Produce a business case for data improvement

• Implement a Gwesty Data Policy

• Build a Business Glossary of business critical data items

External Drivers

Improved Web Presence

Increased Competition

PersonalisationExpectations

Shareholder Expectations

Brand and Reputation

Demand For Integrated Booking

To ensure all our hotel and casino customers experience a personal

touch and value for money

C

ACCOUNTABILITY

• Appoint a Chief Data Officer (CDO)

• Create a Data Governance framework

• Appoint Data Owners & Data Stewards

• Create Data Steering & Working Groups

C

QUALITY

• Identify business critical data items

• Set KPIs and measures for key data items

• Introduce regular reporting of key data item quality

• Run pilot project – Valet parking• Enhance Marketing data • Investigate Finance reporting• Investigate Supply Management

C

CULTURE

• Train all Gwesty staff on Data Policy and best practice in data management

• Produce a data management awareness communications plan

• Produce regular Gwesty Data newsletter etc.

To be the world’s leading hotel and casino chain by providing excellent personal customer

service at a great price


Presentation to

Gwesty Executive Board

Gwesty Hotel Group


• Increase revenues by 20%• Our data will not identify our most profitable customers so limits

marketing opportunities• Poor DQ has caused us brand damage so will discourage new bookings • Valet parking revenues are not been accurately recorded so losing

revenues > $2.5 million pa

• Cut operating costs by 15%• Returned mailings & duplicates due to poor DQ in marketing systems cost

us $420k pa (197,000 direct mailings returned in 2015)• Emergency supply orders to hotels cost $21.7m pa (20% above standard

orders), costing Gwesty $3.6m pa. Caused by poor ordering / inventory data management

• Introduce customer loyalty scheme• Our current customer data contains duplicates, inaccuracies & missing

data, e.g. 37% of customer records have no zip code• Impact is that launch of scheme will be 31% more expensive if no action

• Launch on-line hotel reservation system• No validation of customers’ email addresses at present

• Increase stock price by 10%• Our financial data is not trusted


• Quick wins • Project to capture valet parking stubs and feed into ledgers

• Cleanse of marketing database using name & address enrichment tools• Reconcile customer data with revenues to identify most profitable

customers & enable better targeting for Loyalty SchemeTOTAL COST OF PROJECTS - $115k

DELIVERY BY 31 MARCH 2017

• Medium term projects • Create data governance structure to maintain early gains

• Communications campaign to all employees to stress importance of improving data quality as part of business as usual activities

• Further enhancement and enrichment of customer database and marketing lists, including addition / validation of customer email addresses, using specialist DQ tools

• Feasibility study into reducing emergency orders

• In depth analysis of current financial reporting processesTOTAL COST OF PROJECTS - $1.2m (subject to detailed business cases)

DELIVERY BY 31 DECEMBER 2018


• QUICK WIN PROJECTS TOTAL EXPECTED BENEFITS – $3m by 31March 2017

• MEDIUM TERM PROJECTS TOTAL EXPECTED BENEFITS – $15m+ by 31 December 2018

Subject to detailed production of business cases

• What we need from you:• Funding for initial Quick Wins projects - $115k

• Public statement of endorsement from you for the Quick Wins projects

• Facilitate access to all individual Gwesty Board members to gain their personal commitment

• Slot at Board meeting in July 2013 to present progress update and to make case for further action

150

Moving towards Enterprise DQ & DG:

Some additional useful techniques


...in order to plot individuals and their style of behaviour

OPEN(relationship orientated)

INDIRECT

(slow paced

and low key)

SELF-CONTAINED

(task orientated)

DIRECT

(fast paced :

confront

situations

head on)

ANALYTICALDRIVER

AMIABLE EXPRESSIVE

Behavioural Styles


...using observable behavioural characteristics...

AMIABLE EXPRESSIVE DRIVER ANALYTICAL

Indirect Behaviours

Soft handshake

Intermittent eye contact

Low quantity of verbal

communication

Questions for clarification

Tentative statements

Limited gestures to

support conversation

Low voice volume

Slow voice speed

Hesitant Communication

Seldom interrupts others

Open Behaviours

Animated facial

expressions

Considerable hand and

body movement

Flexible Time perspective

Story telling & anecdotes

Little emphasis on facts

and figures

Shows personal feelings

Contact orientated

Direct Behaviours

Firm handshake

Eye contact steady

High quantity of verbal

communication

Emphatic Statements

Points emphasised with

gestures and challenging

tone

High voice volume

Fast voice speech

Frequently interrupts

others

Self Contained

Behaviours

Little facial expression

Controlled and limited

hand and body movement

Disciplined sense of time

Conversation focussed on

issues and tasks

Emphasis on facts and

details

Little sharing of personal

feelings

Behavioural Styles


Data Improvement Project Selection Grid

HIGH BENEFITS /

LOW DIFFICULTY

PRIORITY

1

LOW BENEFITS /

HIGH DIFFICULTY

PRIORITY

4

HIGH BENEFITS /

HIGH DIFFICULTY

PRIORITY

2

LOW BENEFITS/

LOW DIFFICULTY

PRIORITY

3

LEVEL OF DIFFICULTY

BE

NE

FIT

S


Force Field Analysis (Candidate Project 1)

Return on

investment

FORCES FOR CHANGE

(1= weak ; 5 = strong)

FORCES AGAINST CHANGE

(1 = weak; 5 = strong)

Regulatory

benefits

Quick wins

possible

Customer

experience

+3

+3

+5

+1

+12

Technical

Complexity

Lack of

bus. support

Risks

Process

changes

- 3

- 4

- 1

- 2

-10


Return on

investment

FORCES FOR CHANGE

(1= weak ; 5 = strong)

FORCES AGAINST CHANGE

(1 = weak; 5 = strong)

Regulatory

Benefits

Quick wins

possible

Customer

Experience

+2

+5

+4

+3

+14

Technical

Complexity

Lack of

bus. support

Risks

Process

changes

- 1

- 1

- 3

- 4

- 9

Force Field Analysis (Candidate Project 2)


ACTIVITY

• How does this case study relate to your own experience of data quality / governance within your own organisation?

• Would you now approach anything in a different way based on this case study?


And the winner is…

158

Making Enterprise Data Quality a Reality

Summary


• What differentiates enterprise Data Quality / Data Governance from traditional project based DQ / DG approaches

• How to take the first steps in enterprise DQ / DG

• Applying a DQ / DG Framework

• Making the case for investment in DQ and DG

• How to deliver the benefits – people, process & technology

• Real life case studies

• Practice case study – getting enterprise DQ / DG off the ground in a hotel chain

• Key lessons learned and maxims for success

Recap: Tutorial Objectives


• Spend a couple of minutes thinking about your objectives / expectations for the day

• Assess if these expectations have been met

• Consider one key point you will take back to your organisation

ACTIVITY


• Assess your current Data Governance readiness - use the Data Governance framework to help

• Perform data audits on suspected problem areas to prove the need for action

• Identify the barriers preventing sustainable DQ & DG – use the DG Framework

• Articulate and communicate why DQ and DG are a necessity in your organization – develop a Motivation Model etc.

• Create a high level enterprise roadmap – priorities & focus

• Pilot your roadmap in a problem area identified above

• Engage with others who are already on the journey or who have helped others on the journey

What you can do when you get back to your organisation

Call to action: starting your enterprise DQ / DG journey


Data Governance –it’s not about IF you do it but about HOW you do it

162


And remember…

“It is not in the stars to hold our

destiny but in ourselves”

William Shakespeare(Julius Caesar)


About Global Data Strategy, Ltd.

• Global Data Strategy is an international information management consulting company that specializes in the alignment of business drivers with data-centric technology.

• Our passion is data, and helping organizations enrich their business opportunities through data and information.

• Our core values centre around providing solutions that are:• Business-Driven: We put the needs of your business first, before we look at any technological solution.• Clear & Relevant: We provide clear explanations using real-world examples, not technical jargon.• Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s

size, corporate culture, and geography. • High Quality & Technically Precise: We pride ourselves in excellence of execution, and we attract high-

quality professionals with years of technical expertise in the industry.

164

Data-Driven Business Transformation

Business StrategyAligned With

Data Strategy


Contact Info

• Email: [email protected]

• Twitter: @NigelTurner8

• Website: www.globaldatastrategy.com

• Linkedin: uk.linkedin.com/in/nigelturnerdataman

165

http://www.globaldatastrategy.com/


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• Please complete your feedback form

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• Good luck with your data quality and data governance improvements

Documents

Making Enterprise Data Quality a Reality - · PDF fileMaking Enterprise Data Quality a Reality ... mistakes • NHS England pays each GP £136 for each patient on ... linked to strategic