The Anatomy of a Successful Data Management team ? 2013-06-13 The Anatomy of a Successful Data

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  • 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

    Its 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