Data Quality is not an option. DAMA UK 15 June 2007 Tom Scampion, Director, UK

Transcription

1 Data Quality is not an option. DAMA UK 15 June 2007 Tom Scampion, Director, UK

2 What this presentation covers Deloitte Data Management Deloitte Data Value Proposition Data Risk Management How to deliver Data Quality 2 DAMA June 2007

3 Deloitte Data Management 250 staff from across the UK firm combining Audit and onsulting Largest practice of its kind in the UK Presence in London, Birmingham, Leeds, Manchester, Edinburgh, Glasgow and more Innovation status within Deloitte 3 DAMA June 2007

4 Deloitte Data Value Proposition The Three dimensions of Data Quality The Deloitte approach to data quality has three dimensions that uniquely combine to deliver business value. By recognising the importance of each component of data quality - as well as their interactions - we are able to view data as a corporate asset as opposed to a corporate liability. These three dimensions are shown below. Data Risk Management providing data assurance in the form of investigations, reconciliations, reviews and assessments to deliver immediate business solutions. The intention is that over time, these reconciliations become part of Business As Usual, and a level of confidence becomes pervasive. At this point, data quality monitoring is performed on an exceptions basis Data Governance creating the policies and identifying the people who govern the retention and disposition of all corporate information to build the framework for a data-driven enterprise. Accountability and a culture of data ownership are the goal. Data Quality Technology selecting, implementing and integrating the infrastructure required to ensure effective enterprise data management. The appropriate selection and implementation of a technology suite to support Data Quality will enable both a solid foundation and a flexible framework within which to improve. 4 DAMA June 2007

5 Where to start - Data Risk Management The Dimensions of Data Quality The first stage in any data quality initiative is to define what we mean by good quality data. Relevance is the information relevant for its intended purpose? Accuracy is the information correct and can it be validated? Integrity does it have a coherent, logical structure? onsistency is the data consistent and easily understood? ompleteness does it provide all the information requested? Validity is it within the parameters required by the business? Timeliness is it up-to-date and available when required? Accessibility can the data be easily accessed and exported to the target application? The omponents of Data Quality Data Quality should be viewed as an ongoing process, as opposed to a point in time solution. From the moment it is collected, data is potentially out-of-date. High quality data comes from continually measuring, assessing and refining the data quality process: Profile and understand: In the form of data quality assessments Standardise and classify: Parsing or classifying complex data to understand components Verify and enhance: Validate existing and supplement missing data Match and link: Identify groups of related records Establish processes: Gain buy-in and accountability Return to profile and understand How we an Help - Our Data Quality Approach Five key stages: What is the data today? What should it be? What are the Why are there Making what it is = what it should be 5 DAMA June 2007

6 Our Approach Phase 1 What is the data today? What should it be? What are the Why are there Making what it is = what it should be ` ` ustomer Application Process ustomer contacts 3 branch 3 Branch enters application redit heck OK? L No ustomer notified of failed application Yes No Enter account Enter payment Yes Alternative available? Mapping the data flows What data is held? Where? By whom? Who uses data? When? Why? When is data created? Added? hanged? Removed? What is manual entry and what is automatic? Why? What depends on what? Why? No Payment OK? L Yes ustomer assigned phone and sent welcome to 3 pack ustomer data entered L Action results logged 6 DAMA June 2007

7 Our Approach Phase 2 What is the data today? What should it be? What are the Why are there Making what it is = what it should be Defining the gold standard Define the data types Who should be responsible for that data? What attributes should the data have? What are the relationships between the data and are they accurately reflected in the data model? Gold standard defined based on best practice and business requirements Data Address Details Description: A valid UK postal address Owner: Head of ustomer Services Specification: 1. House Name/Number 2. Street 3. Town/ity 4. ounty 5. Post ode Bank Details Description: A valid UK bank account Owner: Head of ustomer Services Specification: 1. Account Number 2. Sort ode Relevance Enable postal delivery of statements, letters and marketing literature Enable direct debit billing Accuracy 100% match to external postal address data source 100% match to external bank account data source 7 DAMA June 2007

8 Our Approach Phase 3 What is the data today? What should it be? What are the Why are there Making what it is = what it should be Analysing the current situation Profile data sources and compare to Gold Standard Assess data map against Gold Standard dependencies reate dashboard of performance to identify gaps Data Relevance Accuracy Address Details Bank Details 8 DAMA June 2007

9 ustomer contacts branch Branch enters application redit heck OK? Yes Enter account L No Enter payment Yes No Alternative available? No Payment OK? Yes M L ustomer notified of failed application ustomer assigned phone and sent welcome pack ustomer data entered Action results logged Our Approach Phase 4 What is the data today? What should it be? What are the Why are there Making what it is = what it should be Data Relevance Accuracy Address Details Bank Details Identifying the root causes Examine in detail key findings Further analyse data to deepen understanding and quantify effects Understand root causes for gaps in Data Quality in terms of people, process, technology and governance Review Gold Standard for realism and appropriateness 9 DAMA June 2007

10 Our Approach Phase 5 What is the data today? What should it be? What are the Why are there Making what it is = what it should be Setting a path to improvement Recommendations on quick wins to resolve tactical issues Recommendations on strategy for future improvements and target Data Quality framework Agree high level roadmap for delivery of target framework Evidence based conclusions from root cause analysis not speculation 10 DAMA June 2007

11 How to deliver Data Quality reate the appropriate governance data is a firm s biggest asset and it deserves top level sponsorship Take a risk based approach focus on the areas that pose the biggest risk to the business Maintain business benefit perfect data is not the aim, only that data is fit for purpose. A flash report does not need to be as accurate as the end product Top-down and bottom-up - Address known issues that derive from poor DQ; Investigate data to uncover unknown issues and assess impact on business strategy Iterative - Focus on quick wins, then prove concept, fine-tune and replicate Integrated - We recognise that DQ is only possible by aligning process, technology, organisation and governance to a common goal Sustainable - Establish a long-term model that can be continuously operated; Don t just clean the data act on root causes Flexible - Focusing on the most relevant needs 11 DAMA June 2007

12 This document is confidential and prepared solely for your information. Therefore you should not, without our prior written consent, refer to or use our name or this document for any other purpose, disclose them or refer to them in any prospectus or other document, or make them available or communicate them to any other party. No other party is entitled to rely on our document for any purpose whatsoever and thus we accept no liability to any other party who is shown or gains access to this document. Deloitte & Touche LLP is a limited liability partnership registered in England and Wales with registered number O and its registered office at Stonecutter ourt, 1 Stonecutter Street, London E4A 4TR, United Kingdom. Deloitte & Touche LLP is the United Kingdom member firm of Deloitte Touche Tohmatsu ('DTT'), a Swiss Verein whose member firms are separate and independent legal entities. Neither DTT nor any of its member firms has any liability for each other's acts or omissions. Services are provided by member firms or their subsidiaries and not by DTT. 12 DAMA June 2007

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