global data strategy, ltd. 2016 · pdf file• also vp of strategic im at trillium...

Global Data Strategy, Ltd. 2016

Making Enterprise Data Quality a Reality…Room for Improvement?

Nigel TurnerPrincipal Consultant EMEA, Global Data Strategy Ltd.

Data Governance & MDM Conference EuropeMonday 16 May 2016

3

Scene Setting & Introductions


What we will NOT be doing this afternoon (1)

TALKING CONSTANTLY



THE CORPORATE SALES PITCH



THEORETICAL & ACADEMIC APPROACHES


What we will be doing this afternoon (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 – key do’s and don’ts

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

• Key lessons learned and maxims for success

Tutorial Objectives


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…

13

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 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


A recent data failure (1)

• Major global bank

• Supports community projects in Hong Kong

• Referred its customers to a Community Projects link on its website

• Unfortunately link was out of date and sent customers to a pornography site

• Many customers questioned the suitability of the projects sponsored…


A recent data failure (2)

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


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’


DQ & DG – and it’s going to get worse…

3 DATA VIRTUALIZATION1 BIG DATA 2 CLOUD COMPUTING

TECHNICAL DRIVERS

5 LAW & REGULATION4 CUSTOMER EXPECTATIONS

BUSINESS DRIVERS

6 IMPROVED EFFICIENCY

20

An Enterprise DQ / DG Strategy

What’s stopping me?


What problems or opportunities are changing or driving 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


QUESTION FOR DISCUSSION:

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


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


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


• 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 31

“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

• After the break 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


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 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 systematic approach so…• You need a proven enterprise DQ / DG Framework

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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


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?


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

51

Common Set of Goals & Guidelines


Sample Business Motivation Model

52

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


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 4 Basic Organisational Models of Data Governance


What are the pros and cons of each model?

ACTIVITY


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

• Has key 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?


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?


“LEAN” thinking can help…

..identify the “hidden factories” in your organisation


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?


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


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%


Making it happen: Creating the roadmap – some 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


Enterprise DQ / DG: putting it into practice

Outcome: Need for EDQ & DG proven; £650m benefits delivered

Outcome: Failure costs proven; DG policy & guidelines implemented

Outcome: Benefits of DG & DQ communicated; programme expanded

Outcome: DG programme revitalised;new roadmap enacted with focus on key data elements

70

How to Make Enterprise DQ & DG a RealityTrying it out for yourselves


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


• Fictitious but realistic

• Safe environment

• No wrong answers!

• Why this exercise?

The Case Study


• 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


• You are a newly formed team of data specialists in the Gwesty Group HQ function

• You have been asked by your CIO (who is constantly being criticised about data, and blamed for data problems by other Board members) to look at how data might be improved across the group

• As the final task your CIO has also asked you to deliver a 5 minute presentation on the need to address Data Quality & Data Governance across the organisation at the next Board meeting

ACTIVITY


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

77


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


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


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

• Would you now approach anything in a different way?

• Any further questions or comments?

ACTIVITY


And the winner is…


• 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 Data Governance 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


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.

85

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

86

http://www.globaldatastrategy.com/


And finally…

• Please complete your feedback form

• Leave your business cards for a chance to win the book

• I am here until Wednesday lunchtime so feel free to follow up with me

• Lightning talks at 17:25 this afternoon

• Enjoy the Summit and enjoy London!

global data strategy, ltd. 2016 · pdf file• also vp of strategic im at trillium...

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