Taming the waves:the foundations of effective data

management Nigel Turner

DAMA UK Committee MemberPrincipal Information Management Consultant EMEA, Global Data Strategy

Marine & Maritime GIS Workshop 17 November 2015

Introductions

The data explosion

How ready are organisations to cope?

What happens when data is not managed

Fixing it – putting Data Governance at the core

Planning for and implementing Data Governance

Summary

Objectives of session

My 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

• Data Management Association United Kingdom Committee member for 7 years and Data mentorship scheme lead

Some organisations I have worked with on enterprise data management

About DAMA

DAMA International non-profit, vendor-independent www.dama.org• Professionals advancing information and data management

• Based in the US, chapters on every continent

DAMA UK (the local chapter) www.damauk.org

Mission: To champion the value of Information Management and provide training and support for data and information professionals in the UK

• Three face to face learning sessions per year

• Regular webinars covering best practice, vendor show and tell tool demonstrations, career guidance and industry trends

• Mentoring scheme providing 1-1 support, guidance & career advice

• Opportunity to develop the profession by contributing to working groups

Taming the data waves

‘Big’ Data – Volumes

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

AVERAGE BUSINESS DATA VOLUMES DOUBLE EVERY 1.2 YEARS

2.5 QUINTILLIONGRAINS OF SANDON EARTH

7.5 QUINTILLIONBYTES OF NEW DATA CREATED EVERY DAY

The Dimensions of Big Data

Velocity

Volume

VarietyVeracity

What’s causing this data wave?

So how prepared are most companies?

Key data questions

• What data do we store or have access to?

• How and where is it held?

• Who owns the data or is accountable for it?

• Who has access to it and why?

• What does it mean - how well is it tagged?

• How good is its quality & trustworthiness?

• How can I best exploit it?

• What laws and regulations must I comply with?

How it can go wrong…

The industry impact of poor DM – 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

Millions of UK National Health Service patient records sold to insurance firms

On average, organizations

waste 15-18% of budgets dealing with datainaccuracies

The US economy loses$3.1 trillion a year because poor data quality

High profile data horror stories (1)

• Major global bank

• Supports community projects in Hong Kong

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

High profile data horror stories (2)

High profile data horror stories (3)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

High profile data horror stories (4)

“Millions of NHS records sold to insurance firms”Daily Telegraph 17 June 2014

Poor data management –impact on organisations

ECONOMIC

LAW & REGULATION

BRAND & REPUTATION

Poor data governance:impact on individuals

ANNOYANCE REPUTATIONAL DAMAGE

DEATH TELL THEIR FRIENDS

The ripple effect

So how do you fix it?

• Make data a business responsibility, not an IT function

• Implement a data strategy – embrace both improvement & exploitation

• Enforce a data policy to control access and usage rules

• Monitor and measure key data • Create and run data enhancement projects • Implement Data Governance

The DAMA Data Management Body of Knowledge (DMBOK) wheel

The data problem –the Data Governance solution

Sales Operations Despatch Finance

CUSTOMER DATA

PRODUCT DATA

FINANCE DATA

EMPLOYEE DATA

Data Governance – a definition

“A process for managing and

improving data for the benefit of all stakeholders”

The core principles of data governance

• Key data items & domains are identified and defined – what are they? (Customer, Supplier, Finance etc.), where are they are held? etc.

• Individual business people are made accountable for these data items and domains– often called Data Stewards

• All critical data is defined, indexed, measured regularly & reported on by Stewards

• As problems are uncovered, data improvement initiatives are launched to address them

Data Governance – the dangers

Traps for the unwary –why Data Governance 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

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

Data Governance framework:some sample useful tools & techniques

Vision & Strategy: Example Business Motivation Model

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Mission 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

DriversDigital Self-Service

Increasing Regulation Pressures

Online Community & Social Media

Customer Demand for Instant Provision

Internal

Cost Reduction

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

• Engage in innovative ways to use 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

Organisation & People:create a CDO led virtual organisation

Chief Data Officer (CDO)

Data Consumers

Business Data Owner

Data Steward(s)

Business Data Owner

Business Data Owner

CIO or IT Lead Privacy & Security Experts

Data Steward(s)

Data Steward(s)

IT Subject matter

Experts(s)

Strategy setting / Data Governance steering group

Execution / Data Governance working group(s)

Processes & workflows: apply ‘Lean’ methods

..identify the “hidden factories” in your organisation

Data Management & Measures:Example Enterprise Data Model

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

DG

Visio

n

Bu

siness

Drivers

Desired State

Example Maturity AssessmentDescription + - 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

The Roadmap

DATAGOVERNANCE

DATAIMPROVEMENT

IMPROVEMENTCYCLES

DG DRIVERS & DATA PROBLEMS

IMPROVED DATAEVOLVING BAU

ENTERPRISEDATA GOVERNANCE

LAUNCH THE DG FOUNDATION

In summary: taming the waves

• More & more organisations are transforming the way they do business through data…– More efficient ways of operating (e.g. marketing, issue

resolution)

– New business models (e.g. customer self-service)

• To create an effective data strategy ensure you:– Align data strategy with business strategy

– Implement Data Governance

– Focus on improving and not measuring data

– Remember that all in your organisation share the responsibility for good data management

– Deliver benefits early and regularly

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Contact Info

• Email: nigel.turner@globaldatastrategy.com

• Twitter: @NigelTurner8

• Website: www.globaldatastrategy.com

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

• DAMA UK: www.damauk.org and use Contact Us section

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Thank you