lean data quality management
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DESCRIPTION
There are many approaches to Data Quality Management today. This is an introduction to an approach we have developed predominantly within Financial Services which we believe yields fast, cost effective results for our clients. Our approach is inspired by Lean Manufacturing and uses the latest Data Profiling tools and techniques to achieve faster results than traditional approaches to Data Quality Management.TRANSCRIPT
- 1. September 2013 Introduction to Lean Data Quality services
- 2. Data to Value Nigel Higgs Experienced Information Management Consultant who has led practices within Financial Services, Insurance & Government. James Phare Former Head of Data Architecture & Information Management at Man Group plc. Data to Value is a newly formed consultancy specialising in all aspects of Information Management Specialists at bridging technology & business understanding gap within Financial Services We offer a fresh approach compared to other large consulting firms Our principles are founded on lean & agile approaches with our clients requirements & priorities at the forefront of our work
- 3. Traditional approaches to DQ Management Do nothing / ignore (not recommended!) Enterprise data quality programmes Ad hoc using traditional tools (Excel etc) Outsource responsibility to third party
- 4. Lean Approach to DQ Management Focused on minimising the allocation of resources to any activity not linked to creating value for the customer Key features: Early adopters, prototyping & Minimum Viable Products (MVPs). Wide IM skillsets & cross functional teams. Smaller batch sizes / more frequent iterations. Hypotheses, metrics & validated learnings. Build-Measure-Learn cycle & Pivots.
- 5. The Process Data loaded & profiled, discovery process begins DQ Rules used to test hypotheses, capture metrics & quantify defects Results presented for review Defects prioritised, managed & resolved Results inform both recurring & ad hoc responses to DQ issues by Business data owners Discover MeasureManage
- 6. Scorecards with dimensions Key Performance Indicators (KPIs) 80% 85% 90% 95% 100% Integrity Completeness UniquenessConsistency Conformity 75% 80% 85% 90% 95% 100% May June July August Overall Quality Completeness Consistency Conformity Uniqueness Integrity - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 May June July August Dept A Dept B Dept C Dept D Data Quality Scorecard Product Data May 2013 Commentary: - Data coverage of priority field A increased from 30% to 80%. - Tier 1 incidents down 30% - Data Dependency X now live. - Data Maintenance requirement reduced by 30% over 3 months to date. - 2 additional power users trained in DQ tool. - 2 additional Data Stewards within Product Master Data Set. Status = Green May DQ dimensions Latest dependenciesData Maintenance activity Monthly DQ dimensions Aimed at - Sponsors - Decision makers Aimed at - Data practitioners - Highly dependent stakeholders Tailored presentation of results
- 7. Demonstration of X88 Pandora for Lean Data Quality Management
- 8. Lean Information Management Specialists. Data to Value Ltd 42-44 Bishopsgate London EC2N 4AH T +44 (0)208 278 7351 www.datatovalue.co.uk Nigel Higgs [email protected] James Phare [email protected]