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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
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What we will NOT be doing today (1)
TALKING CONSTANTLY
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What we will NOT be doing today (2)
THE CORPORATE SALES PITCH
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What we will NOT be doing today (3)
THEORETICAL & ACADEMIC APPROACHES
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What we WILL be doing today (1)
PRACTICAL APPROACHES & TOOLS
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What we WILL be doing today (2)
HANDS ON – A CHANCE TO PRACTISE
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What we WILL be doing today (3)
HAVE SOME FUN
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• 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
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Self Introductions • Your role• Company and country• Main expectation from today
ACTIVITY
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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
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Some organisations I have worked with on enterprise DQ / DG…
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Context & Drivers for Enterprise Data QualityWhy bother?
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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
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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)
Global Data Strategy, Ltd. 2016
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)
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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
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• Why is good Data Quality important:
• To companies and organisations?
• To individuals?
ACTIVITY
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How it can go wrong…
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A recent data failure (1) - Retail
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HP ZBook 17 G2 Mobile Workstation
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A recent data failure (2) - Energy
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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
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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
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A recent data failure (4) - Health
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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
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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’
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‘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
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The New Landscape of the Digital Business
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The Current State of Data Quality in Digital Business
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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
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Balance Innovation with Foundation
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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
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Poor data quality & governance: impact on companies & organisations
ECONOMIC: REVENUES, COSTS, PROFITS
LAW & REGULATION
BRAND, REPUTATION & CUSTOMER LOYALTY
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Poor data governance: impact on individuals
ANNOYANCE REPUTATIONAL DAMAGE
DEATH TELL THEIR FRIENDS
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An Enterprise DQ / DG Strategy
What’s stopping me?
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What problems or opportunities are changing or driving Enterprise Data Quality & Data Governance in your organisation / industry / geography?
ACTIVITY
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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
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The paradox – bridging the gap
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Why might enterprise Data Quality & Data Governance initiatives fail?
ACTIVITY
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Enterprise DQ & DG – the dangers
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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
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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
Global Data Strategy, Ltd. 2016
Why it can be hard - the horizontal data flow
Sales Operations Despatch Finance
CUSTOMER DATA
PRODUCT DATA
FINANCE DATA
EMPLOYEE DATA
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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
Global Data Strategy, Ltd. 2016
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
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• 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?
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“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
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CURRENT MATURITY / READINESS FOR
ENTERPRISE DQ / DG AS IS
ENTERPRISE DQ / DG IS BUSINESS AS
USUAL TO BE
Enterprise DQ & DG – plotting your journey
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Your own organisation – how mature are you?
Where should you start?
Global Data Strategy, Ltd. 2016
• 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
Global Data Strategy, Ltd. 2016
The DQ Maturity model
Level 1:AWARE
Level 2:REACTIVE
Level 3:PROACTIVE
Level 4:MANAGED
Level 5:OPTIMISED
CULTURAL MATURITY
Source: META Group
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
• 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
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How to Make Enterprise DQ & DG a Reality
Trying it out for yourselves (1)
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Case Study
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Legal Rider
• This case study is based upon a purely fictional hotel business
• Any resemblances to real hotel chains are unintentional and purely coincidental
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• 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
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• Fictitious but realistic
• Safe environment
• No wrong answers!
The Case Study
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• 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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
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Break (10:30 – 10:45)
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The components of an Enterprise DQ / DG
Strategy:Making it happen
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What do YOU think are the key components of successful enterprise
DQ / DG?
ACTIVITY
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Data Governance barriers: one approach
OPTION 1 ADDRESS BARRIERS
REACTIVELY
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Data Governance barriers: a better approach
OPTION 2 ANTICIPATE BARRIERS
PROACTIVELY
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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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
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The components of an Enterprise DQ / DG
Strategy:Vision & Strategy
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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
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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?
Global Data Strategy, Ltd. 2016
Vision & Strategy: Suggested main deliverables
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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?
Global Data Strategy, Ltd. 2016
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
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Common Set of Goals & Guidelines
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Sample Business Motivation Model
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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
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How to Make Enterprise DQ & DG a Reality:
Trying it out for yourselves(2)
Global Data Strategy, Ltd. 2016
• Produce short Corporate Mission & Corporate Vision statements for Gwesty
• List External and Internal drivers for better DQ and DG across the Gwesty organisation
ACTIVITY
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Enterprise DQ / DG business cases – making the case
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LOOKING AHEAD
LOOKING BACK LOOKING
BOTHWAYS
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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.
Global Data Strategy, Ltd. 2016
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?”
Global Data Strategy, Ltd. 2016
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)
Global Data Strategy, Ltd. 2016
Example – Actual summary and benefit analysis (partial)
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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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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!
Global Data Strategy, Ltd. 2016
Making the business case – further tips
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Include Market
/ Competitorperspective
Get expert help
Include‘Do nothing’
option
Seek Incremental
funding
Global Data Strategy, Ltd. 2016
Creating a DG / DQ Strategy (<5 pages)
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• 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
Global Data Strategy, Ltd. 2016
Lunch (12:15 – 13:15)
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The components of an Enterprise DQ / DG
Strategy:Organisation & People
Global Data Strategy, Ltd. 2016
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?
Global Data Strategy, Ltd. 2016
The 5 Basic Models of Data Governance
Process Centric
Contingent / Blended
SystemsCentric
Data Centric
OrganisationCentric
Global Data Strategy, Ltd. 2016
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
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Global Data Strategy, Ltd. 2016
What are the pros and cons of each model of Data Governance?
ACTIVITY
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
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Global Data Strategy, Ltd. 2016
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
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Global Data Strategy, Ltd. 2016
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
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Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
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102
The components of an Enterprise DQ / DG
Strategy:Data Management & Metrics
Global Data Strategy, Ltd. 2016
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?
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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”?
Global Data Strategy, Ltd. 2016
Data Management & Measures: Example Data Dashboard
Source: Trillium Software
107
The components of an Enterprise DQ / DG
Strategy:Processes & Workflows
Global Data Strategy, Ltd. 2016
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?
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
Business processes: LEAN thinking can help…
..identify the “hidden factories” in your organisation
111
The components of an Enterprise DQ / DG
Strategy:Culture & Communications
Global Data Strategy, Ltd. 2016
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?
Global Data Strategy, Ltd. 2016
How would we feel….?
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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???
Global Data Strategy, Ltd. 2016
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!
Global Data Strategy, Ltd. 2016
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.
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117
The components of an Enterprise DQ / DG
Strategy:Tools & Technology
Global Data Strategy, Ltd. 2016
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?
Global Data Strategy, Ltd. 2016
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
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Enterprise DQ / DG Strategy:
Creating the Action Roadmap
Global Data Strategy, Ltd. 2016
CURRENT MATURITY / READINESS FOR
ENTERPRISE DQ / DG AS IS
ENTERPRISE DQ / DG IS BUSINESS AS
USUAL TO BE
Enterprise DQ & DG – creating the Roadmap
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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%
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
An enterprise approach to DG & DQ improvement
Global Data Strategy, Ltd. 2016
• 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?
Global Data Strategy, Ltd. 2016
Enterprise DQ / DG: putting it into practice • Every Enterprise DQ / DG Roadmap is unique
Global Data Strategy, Ltd. 2016
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.
Global Data Strategy, Ltd. 2016
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?)
Global Data Strategy, Ltd. 2016
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)
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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)
Global Data Strategy, Ltd. 2016
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…
Global Data Strategy, Ltd. 2016
Sample Business Motivation Model
143
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
Global Data Strategy, Ltd. 2016
Break (14:45 – 15:00)
Global Data Strategy, Ltd. 2016
Example Gwesty Motivation Model
Corporate Mission Corporate Vision
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
Global Data Strategy, Ltd. 2016
Presentation to
Gwesty Executive Board
Gwesty Hotel Group
Global Data Strategy, Ltd. 2016
• 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
Global Data Strategy, Ltd. 2016
• 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
Global Data Strategy, Ltd. 2016
• 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
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Moving towards Enterprise DQ & DG:
Some additional useful techniques
Global Data Strategy, Ltd. 2016
...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
Global Data Strategy, Ltd. 2016
...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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
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)
Global Data Strategy, Ltd. 2016
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?
Global Data Strategy, Ltd. 2016
And the winner is…
158
Making Enterprise Data Quality a Reality
Summary
Global Data Strategy, Ltd. 2016
• 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
Global Data Strategy, Ltd. 2016
• 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
Global Data Strategy, Ltd. 2016
• 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
Global Data Strategy, Ltd. 2016
Data Governance –it’s not about IF you do it but about HOW you do it
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Global Data Strategy, Ltd. 2016
And remember…
“It is not in the stars to hold our
destiny but in ourselves”
William Shakespeare(Julius Caesar)
Global Data Strategy, Ltd. 2016
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
Global Data Strategy, Ltd. 2016
Contact Info
• Email: [email protected]
• Twitter: @NigelTurner8
• Website: www.globaldatastrategy.com
• Linkedin: uk.linkedin.com/in/nigelturnerdataman
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Global Data Strategy, Ltd. 2016
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