The Anatomy of a Successful Data Management team

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2 The Anatomy of a Successful Data Management team 21 st June 2011 Jon Asprey VP, Strategic Consulting

3 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

4 The Data (Quality) Management Challenge Frustration Picture Some of the factors. Legacy issues Many involved parties Lack of definition & existing documentation No ownership or sponsorship Resource intensive It s HARD out there!

5 Ensuring success fit for purpose data Identify, quantify & validate DQ issues Ongoing monitoring and measurement Data quality How do we make sure we get the data we want? Data governance Build framework for accountability & change Form decision making groups

6 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

7 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

8 How does this translate? - Centre of excellence 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

9 Building the team understand data flows Map out the data flows for key processes, Then understand the stakeholders, Data domain Originators Modifiers Responsible exec Data Steward (s) Consumers Organisation Contact Sales Sales, Finance Head of CRM Sales (super users) Stakeholder group for Organisation data domain Sales, Marketing Finance, Legal, BI/IT Candidate Product/service Location Will be subject to process change Accountable for DQ improvements & monitoring Involved in agreeing data standards

10 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 Example business case map Increase revenue/profit Risk Management Cost reduction Data Domain Consumer Tangible & Measured Type Description Organisation Data Finance Y Revenue/ Cash flow Invoices bounced as customer name not legal entity Tangible Marketing N Brand & lead gen undelivered for promotional campaigns Supporting Legal Y Risk - compliance Unable to confirm compliance due to missing attributes Tangible Sales* N Cost sales efficiency Duplicates affecting targeting and call completion rates Supporting IT Y Cost Additional storage required for duplicate organisation Tangible entries Tangible business case points are critical, strengthened by supporting points

11 Data management roles and structures There is not one answer, structure needs to fit with your business Executive Sponsor Front office data management Back office data management Technology support Example structure 1 Chief Data Officer Agents, branches, lines of business feeding requirements Capability & services led with strong emphasis Change delivery centre of excellence. Service based approach. on change Internal development culture good relationship with change delivery Example structure 2 Head of Finance Processing centres manually cleansing data defects Data management & governance led Finance data management and profiling from team. within Developing a service business approach. function Some resistance to collaboration and business leadership Collaboration is key, along with interdepartmental communication Balance of centralised vs. distributed governance & control

12 Evolution and culture change It is an evolving process Improving data quality Realising business benefits Engendering process change SMEs Data Analysts Business Analysts Data Management team support DQ Process DQ Control Bus. rules Feedback Data Governance push

13 Conclusions Executive Group Understand your organisation Stakeholders in data Scope of Data Management task Management Group Responsible Exec Data Steward Combination of skills required Technical capability Business consulting skills Process mapping skills Working Group Cross functional group involved SMEs Business Analysts Data Analysts Coordination of mixed resources Participation of end users & SMEs

14 Thank you & Questions