The Anatomy of a Successful Data

Management team

21st June 2011

Jon Asprey

VP, Strategic Consulting

Overview

• The continuing challenge today

– Making DQ a demonstrable success

– Maturing market (increased focus)

• Business led vs. IT project led

• DQ is an end to end problem

– Organisations are structured in silos

– To truly manage DQ need to look end to end

• A multi-faceted team is required to the challenge

– Sponsorship and roles

– Skills needed in team

The Data (Quality) Management Challenge

Frustration PictureSome of the factors….

• Legacy issues

• Many involved parties

• Lack of definition &

existing documentation

• No ownership or sponsorship

• Resource intensive

It’s HARD out there!

Ensuring success – “fit for purpose” data

How do we make

sure we get the

data we want?

• Identify, quantify & validate DQ issues

• Ongoing monitoring and measurement

• Build framework for accountability & change

• Form decision making groups

Data governance

Data quality

Why is it so difficult?

Consider the data flows for a key process

There are many “moving parts” and involved parties

• “True” cross-organisational scope – Multiple, diverse stakeholders

• Differing business requirements – Consensus and agreement difficult

• Complex data flows and lineage – Undocumented & sporadic knowledge

• No structure for enforcing change – Lack of ownership & responsibility

What is needed?

A structure with an investment in time and resources

Strategy – “Brains”

• Direction

• Leadership

Management – “Muscle”

• Enforcement of process

• Ownership of resolution

Activity – “Legwork”

• Research into processes

• Definition of rules & standards

• Analysis of data

How does this translate?

Executive – “Brains”

• Policy & process

• Escalation/Prioritisation

• Conflict resolution

Management & Ownership – “Muscle”

• Resolution of DQ issues

• Enforcement of policy

• Coordination of resources

Working Group - “Legwork”

• Understanding business

processes

• Investigating issues raised

• Building business rules and

performing data analysis- Centre of excellence

Building the team – understand data flows

Map out the data flows for key processes,

Sales

Originators

Location

Product/service

Candidate

Contact

Sales, Marketing

Finance, Legal, BI/IT

Sales (super users)Head of CRMSales, FinanceOrganisation

ConsumersData Steward (s)Responsible execModifiersData domain

Then understand the stakeholders,

Stakeholder group for Organisation data domain

Will be subject to

process change

Accountable for

DQ improvements

& monitoring

Involved in

agreeing data

standards

The “why” - building the business case

• To ensure executive sponsorship and business

participation a “robust” business case is key

• Input will be required from multiple departments

Risk Management

Cost reductionIncrease

revenue/profit

Additional storage required for duplicate organisation

entries

CostYIT

Duplicates affecting targeting and call completion ratesCost – sales

efficiency

NSales*

Y

N

Y

Tangible &

Measured

Unable to confirm compliance due to missing

attributes

Risk -

compliance

Legal

Email undelivered for promotional campaignsBrand & lead

gen

Marketing

Invoices “bounced” as customer name not legal entityRevenue/

Cash flow

FinanceOrganisation

Data

DescriptionTypeConsumerData Domain

Example – business case map

Tangible business case points are critical, strengthened by supporting points

Tangible

Tangible

Tangible

Supporting

Supporting

Data management roles and structures

There is not one answer, structure needs to fit with your business

Collaboration is key, along with interdepartmental communication

Balance of centralised vs. distributed governance & control

Example structure 2Example structure 1

Some resistance to collaboration

and business leadership

Internal development culture

good relationship with change

delivery

Technology support

Finance – data management and

profiling team. Developing

service approach.

Change delivery – centre of

excellence. Service based

approach.

Back office

data management

Processing centres – manually

cleansing data defects

Agents, branches, lines of business

– feeding requirementsFront office

data management

Head of FinanceChief Data OfficerExecutive Sponsor

Capability &

services led with

strong emphasis

on change

Data management &

governance led

from within a

business function

Evolution and culture change

It is an evolving process

• Improving data quality

• Realising business benefits

• Engendering process change

Data Governance “push”

Data Management team support

DQ Control Bus. rulesDQ ProcessFeedback

SMEs Data Analysts Business Analysts

Conclusions

Executive Group

Management Group

Working Group

SMEsBusiness

Analysts

Data

Analysts

Responsible

ExecData Steward

• Understand your organisation

• Stakeholders in data

• Scope of Data Management task

• Combination of skills required

• Technical capability

• Business consulting skills

• Process mapping skills

• Cross functional group involved

• Coordination of mixed resources

• Participation of end users & SMEs

Thank you & Questions