How to Strengthen Enterprise Data Governance with Data Quality

1. Harald Smith Davinity Powis How to Strengthen Enterprise Data Governance with Data Quality

2. Agenda Introduction Why Data Quality & Data Governance are top of mind Data Quality & Data Governance: a symbiotic relationship How Data Quality strengthens Enterprise Data Governance Summary Syncsort Confidential and Proprietary - do not copy or distribute

3. Speakers Harald Smith Director of Product Management, Trillium Software 20 years in Information Management incl. data quality, integration, and governance Co-author of Patterns of Information Management Author of two Redbooks on Information Governance and Data Integration Davinity Powis Pre-Sales Consultant for Syncsort Founded UK-based data-marketing agency until its acquisition in 2012 Specialises in Data Quality, Data Governance, Data Integration and Big Data. Particular interest in data quality and enrichment Passionate about making data understandable and exciting! Syncsort Confidential and Proprietary - do not copy or distribute

4. Data: the fuel of the future Data is to this century, what oil was to the last one: a driver of growth and change. The Economist: Fuel of the future - Data is giving rise to a new economy: 6th May 2017 Flows of data have created new infrastructures, new businesses, new monopolies, new politics and crucially new economics. Digital information is unlike any previous resource: it is extracted, refined, valued, bought and sold in different ways. It changes the rules for markets and it demands new approaches from regulators. Many a battle will be fought over who should own, and benefit from, data. Syncsort Confidential and Proprietary - do not copy or distribute

5. Many sources are predicting exponential data growth toward 2020 and beyond. In almost a repeat of Moore’s Law, they are all in broad agreement that the size of the digital universe will double every two years at least. Human-generated data is experiencing an overall 10x faster growth rate than traditional business data, and machine data is increasing even more rapidly at 50x the growth rate! Acceleration due to: IoT, AI, ML, Big Data, Block Chain Data Governance & Quality are top of mind Volume and complexity of data is growing new tools allowing more granular data dissection Broader and deeper compliance & regulation expectations trust & confidence Syncsort Confidential and Proprietary - do not copy or distribute

6. A CDO’s nightmare! Can I even trust this data? Is duplication causing ‘permission clash’ Where is all my data? How many places store the same data? Are we compliant with all necessary regulations? Can we prove it? Do we know what & how much customer data we even hold? Do we have right internal training & policies to manage this much data? Syncsort Confidential and Proprietary - do not copy or distribute Is my customer data safe & secure? Could we survive the bad publicity & financial impact of a GDPR fine?

7. Why is Data Quality so important?

8. Data impacts all areas of the business sales marketing financelegal IT logistics management Analysis Sales reports Dashboards Performance metrics Territory management Segmentation SCV / 360 Understanding & CRM Content Campaign management ROI UX All reports! Aggregations Forecasting & modelling Cash flow Contingency planning Data compliance Data regulation Governance Risk Access Security Disaster recovery Scheduling Workloads Performance planning Route planning Capacity management Environmental Competitor analysis HR / recruitment Overall business strategy!Overall business strategy! Syncsort Confidential and Proprietary - do not copy or distribute

9. Data Governance is the set of policies, processes, rules, roles and responsibilities that help organisations manage data as a corporate asset. It ensures the availability, usability, integrity, accuracy, compliance and security of data. Terminology Data Quality refers to ensuring that data is “fit for use” in its intended operational, decision-making and other roles. It covers the accuracy, completeness, consistency, relevance, timeliness and validity of data. Data Quality ACCURACY COMPLETENESS CONSISTENCY RELEVANCE TIMELINESS VALIDITY Data Governance PEOPLE PROCESSES POLICIES RULES STANDARDS DOCUMENTATION SECURITY Data Availability Data Compliance Defining Key Data Elements Assigning Data Stewards & Council Glossaries & Dictionaries Data Consistency & Standardisation Monitoring Analytics Policies & Rules Metrics Data Lineage Reporting In practiceAreas of common interest Cleansing Enrichment Parsing Discovery & Profiling Matching, Suppression & Deduplication Syncsort Confidential and Proprietary - do not copy or distribute

10. Symbiosis “a relationship between two entities for mutual benefit, often without competing with each other” Data Quality & Data Governance share a ‘symbiotic relationship’ Syncsort Confidential and Proprietary - do not copy or distribute

11. Relevant Rules & Policies DQ needs appropriate DG tools to ensure the data is cleaned and maintained within an appropriate data framework which is relevant and pertinent to the business needs Symbiotic relationship between DQ & DG High Quality Data DG needs appropriate DQ tools to not-only clean the raw data, but to illustrate data errors, peculiarities and issues, in order to help compile the best standards and monitor the data quality over time Syncsort Confidential and Proprietary - do not copy or distribute DQDG

12. But they are only useful if they are accurate!We all use information, intelligence & insight Essex Kent Surrey Shrops surrey London Cornwall Merseyside Surry W. Sussex PRE-DQ POST-DQ Syncsort Confidential and Proprietary - do not copy or distribute

13. But they are only useful if they are accurate! Essex Kent Surrey Shrops surrey London Cornwall Merseyside Surry W. Sussex PRE-DQ POST-DQ Syncsort Confidential and Proprietary - do not copy or distribute Essex Kent Surrey Shrops surrey London Cornwall Merseyside Surry W. Sussex PRE-DQ POST-DQ What you don’t know CAN hurt you! Other changes to data quality quickly undermine trust Signal loss Noise Differing aggregations Invalid correlations Unexpressed assumptions Incorrect defaults Lack of context Missing inputs

14. More than simply ‘understanding’ your data!What you don’t know CAN hurt you! Essex Kent Surrey Shrops surrey London Cornwall Merseyside Surry W. Sussex POST-DQPRE-DQ Syncsort Confidential and Proprietary - do not copy or distribute Signal loss Noise Differing aggregations Invalid correlations Unexpressed assumptions Incorrect defaults Lack of context Missing inputs Other changes to data quality quickly undermine trustNecessary to actively Record, Monitor & Measure Enumerate Establish the criteria defining goals, relevance, and fitness for purpose Acquire Capture the metadata for data sources being considered and used Discover Profile the data sources which are required for the desired analysis Validate Evaluate the data sources for the identified and required qualities Document Document and store the findings about data sources and processes Catalog Provide and communicate findings about data sources and processes for others to utilize

15. The role of DQ in DG It is challenging for organisations to respond to regulatory mandates in a timely manner. Data typically comes from multiple disparate systems & sources The number of touchpoints has grown dramatically. There is a higher demand and expectation for real-time data. Regardless of the compliance mandate, the simple fact is that they all require accurate source data. Rubbish-in: rubbish-out is more pertinent than ever before! Syncsort Confidential and Proprietary - do not copy or distribute

16. What are the regulations there for? Regulations are there to protect and regulate: privacy disclosure risk management fraud prevention anti-money laundering anti-terrorism anti-usury lending, and the promotion of lending to lower-income populations. Syncsort Confidential and Proprietary - do not copy or distribute

17. Types of regulations Risk & Compliance GDPR CCPA FSCS FATCA Customer Data Management & KYC Regulatory Reporting & Data Assurance Operational Governance BCBS 239 Data Stewardship ANACREDIT HIPPA BASEL II/III CCAR / Stress Testing DQ Assurance AML Syncsort Confidential and Proprietary - do not copy or distribute

18. GDPR

19. What personal & sensitive data you hold – and is it up-to-date? What you are doing with it & how you are processing it? That you have permission to use it Where it is stored? Is it duplicated? Who has access to it? How are you keeping it SAFE? GDPR is essentially about knowing: Syncsort Confidential and Proprietary - do not copy or distribute

20. What do you know about me? Right to access data plus receive a copy of data Customers are now recognising their new power Data about me is wrong - fix it! Right to inaccurate data correction Erase all my data for good! Right to be forgotten Has my data been breached? Right to be informed within 72 hours How do you use my data? Right to limit processing of personal data and object to how it is processed Demand human interaction Right to not participate in fully-automated decisions based on customer profile Syncsort Confidential and Proprietary - do not copy or distribute

21. Source: Oliver Wyman, Global Management Consultancy (May 2017) Suddenly it’s serious! Google hit with £44m GDPR fine over ads Syncsort Confidential and Proprietary - do not copy or distribute

22. ID Title Forename Surname Full Name Address 1 City Postcode email Phone SMI20033 XXX XXX Dr B. Smith 3 Davy Dr Maltby S66 7EN bob.smith@hotmail.comXXX XXX bob.smith @hotmail.com Bob Smith bob.smith@hotmail.com 2000138604 Dr Smith xxx xxxBob bob.smith@hotmail.com 134567542 Smith 3 Davey Drive Rotherham S667EN 01189407600Bob SMI16975 Dr B. Smith 3 Davy Dryve MALtby S66 7EN 07123 5579421bob.smith@hotmail.com Dr Smith 3 Davy Drive Rotherham S66 7EN 01189407600 07123 5579421 Bob bob.smith@hotmail.com Multiple touchpoints/databases - which is ‘right’? Permission xxxxxxxxxxxxx Syncsort Confidential and Proprietary - do not copy or distribute

23. Single View enables accuracy and excellence in… Analytics Analysis of clean data will be accurate Segmentation & Targeting Marketers will place consumers into the correct segments. Campaigns are more relevant Reporting & Visualisation Reports will be reliable. Dashboards show correct findings - giving a true representation. Customer Experience Customers will receive consistent messaging and communications. Accurate understanding leads to appropriate communications and dialogue. Customer Understanding Strategy All these lead to accurate, sensible business decisions. Syncsort Confidential and Proprietary - do not copy or distribute

24. Regulation demands evidence & documentation ARTICLE 5 ARTICLE 30 ARTICLE 32 ARTICLE 35 Provide evidence that your company’s personal data processing adheres to GDPR principles: Processed lawfully, transparently Collected for specific purposes Limited to data relevant for specific purposes Kept accurate and current Processed securely and protected Provide documentation on your company’s Record Processing Activities Provide documentation on your company’s Security of Processing Provide documentation on your company’s Data Protection Impact Assessment Syncsort Confidential and Proprietary - do not copy or distribute GDPR is about more than just data quality though

25. Data Quality tools are no longer a “nice to have” Syncsort Confidential and Proprietary - do not copy or distribute

26. GDPR – where DQ helps deliver compliance 3. Data Integration Integration with Data Governance tools. Triggers issue management and controls. Integration with analytical & dashboarding tools so that GDPR rules and reports (and overall compliance) can be easily understood and monitored. 2. Data Quality Processing Real-time & batch data cleansing & matching across multiple data sources generating SCV; enabling businesses to locate records by a single record quickly SCV also means customer permissions are respected, records can be amended or suppressed / deleted, plus businesses can react to SAR requests quickly Full traceability of original data source Documented DQ routines for transparency & auditing (e.g. user & process control, security) 1. Data Discovery Highlights bad data, typos, mis- fielded data, outlying data not conforming to policy, formatting, structure, syntax etc Exposes text fields with buried, unexpected personal & sensitive data Build Technical business rules to mirror DG rules and identify and monitor ongoing data issues Syncsort Confidential and Proprietary - do not copy or distribute

27. GDPR mandates tight control of customer data! Without DQ, duplication and poor data will propagate, resulting in mis-understanding and mis-respecting the customers’ wishes and demands. Over time, this will inevitably escalate to non-compliance of GDPR! DQ helps ensure DG compliance GDPR Summary Syncsort Confidential and Proprietary - do not copy or distribute

28. FATCA

29. FATCA FATCA is an abbreviation for: Foreign Account Tax Compliance Act. 2010 US federal law to enforce the requirement for US citizens (including those living outside the US) to file yearly reports on their non-US financial accounts to the Financial Crimes Enforcement Network (FinCEN). Introduced April 2015, it requires all non-US financial institutions to search their records for customers with indicia (flags) of ‘US citizen' status, such as a US place of birth, and to flag & identify such records for further inspection. Syncsort Confidential and Proprietary - do not copy or distribute

30. FATCA – where DQ helps deliver compliance DQ processing is typically used as precursor to a bank’s internal FATCA process it uses all key steps such as parsing, standardisation, cleansing, matching, commonisation and merging to deliver Single Customer View (SCV). SCV ensures all duplicate records are linked, often highlighting conflicting information and indicia, such as: Country of Origin of address (US vs. Non-US) US Birthplace US Telephone numbers De-minimis (aggregated account balances with currency conversion) Once data is remediated and harmonised, the right decisions can be made, ensuring the organisation is FATCA compliant. PO Box/Care of addresses US Social Security Numbers US Citizenship Syncsort Confidential and Proprietary - do not copy or distribute

31. Identifies the real country of origin - irrespective of data captured. DQ: highlights address indicia errors Non-US country codes which would otherwise have been incorrectly prevented them from FATCA processing Erroneous US country codes which would have incorrectly included them in FATCA processing, unnecessarily wasting time and resource. Syncsort Confidential and Proprietary - do not copy or distribute

32. Identifies where duplicate records contain conflicting Nationality indicia. Different records have/not been have implicated for FATCA, leading to fuzzy decisions. DQ harmonises the cluster so that each record has the same indicia. DQ: highlights Nationality indicia conflicts Syncsort Confidential and Proprietary - do not copy or distribute

33. No Data Quality = inaccurate decisionsDQ: results Implicated Records which clearly contain implicated indicia Not Implicated Records which do not contain implicated indicia Suspect Records which may contain implicated indicia. = sensible decisions Syncsort Confidential and Proprietary - do not copy or distribute

34. Not performing DQ processing before FATCA procedures could easily lead to missing implicated records from selection. Thus failing FATCA regulation! DQ helps ensure DG compliance FATCA Summary Syncsort Confidential and Proprietary - do not copy or distribute

35. AML

36. AML Money laundering refers to the exchange of money or assets that were obtained criminally for money. It also includes money that is used to fund terrorism, however it’s obtained. Introduced in May 2018, FS organisations must put in place controls to prevent their business from being used for money laundering: checking the identity of your customers checking the identity of ‘beneficial owners’ of corporate bodies and partnerships monitoring your customers’ business activities and reporting anything suspicious to the National Crime Agency (NCA) making sure you have the necessary management control systems in place keeping all documents that relate to financial transactions, the identity of your customers, risk assessment and management procedures and processes Syncsort Confidential and Proprietary - do not copy or distribute

37. AML – where DQ helps deliver compliance DQ processing is typically used as prerequisite to a bank’s internal AML process It uses key steps such as parsing, standardisation and cleansing to ensure the bank’s own data is of the highest standard possible. It also allows the organisation to link all monetary activities to specific individuals, giving the firm the best chance of identifying and combatting potential money-laundering and other financial crimes, and take appropriate actions. Syncsort Confidential and Proprietary - do not copy or distribute

38. DQ: enabling accurate matching & suppression Syncsort Confidential and Proprietary - do not copy or distribute PRE-DQPOST-DQ Once standardised and cleansed, the bank’s data then has the optimum chance of matching data on sanctions lists of known money launderers, criminals or terrorists.

39. When banks transfer money and data SWIFT messages are the format or schema used by financial institutions to exchange data SWIFT messages are complex data structures consisting of five blocks of data including three headers, message content and a trailer. Data Quality is paramount for operational, reporting, governance, and AML requirements. DQ ensures SWIFT message quality Syncsort Confidential and Proprietary - do not copy or distribute

40. 50K|/809615 01178139~MR BOB WONG~53 NEEDLESS RD~LINCOLN LINCOLNSHIRE~LN21 |52A|BEASHKHHXXX|59|/1995 8242 207458~WONG MEI LING AND WONG BOB|57A| 5 | CANADA SQU LONDON|SENDER|LOYDGB2XXX| RECEIVER|BKCHHKHH Title Forename Recoded Forename Surname HouseNo StreetName StreetType City County Postcode Country Clean / Correct / Validation Cleanses, corrects, validates and enriches customer information on SWIFT message to enable accurate AML checks DQ: highlights & remediates data in-flight <OrderingCustomer> … <Name>MR BOB WONG</Name> <Address> <Line1>53 NEEDLESS RD</Line1> <Line2>LINCOLN LINCOLNSHIRE</Line2> <Line3>LN21 </Line3> </Address> … </OrderingCustomer> <BeneficiaryInstitution> … <BIC></BIC> <Address> <Line1> </Line1> <Line2>5 CANADA SQU </Line2> <Line3>LONDON</Line3> </Address> <Account/> … </BeneficiaryInstitution> Parse Syncsort Confidential and Proprietary - do not copy or distribute MR BOB WONG 53 NEEDLESS RD LINCOLN LINCOLNSHIRE LN21 ROBERT ROAD LN21 1RW GBR

41. Match / Link / Deduplication Cleanses, corrects, validates and enriches Beneficiary Institution by matching BIC codes on SWIFT message to enable accurate AML checks DQ: highlights & remediates data in-flight Bank of America NA BOFAGB22SCP E14 5AQ Syncsort Confidential and Proprietary - do not copy or distribute

42. If there was no DQ processing, it would directly increase the chances of unknowingly processing illegal transactions, and/or trading with known criminals. They would have failed AML regulation! DQ helps ensure DG compliance AML Summary Syncsort Confidential and Proprietary - do not copy or distribute

43. Execution Strategy

44. 1. Start small: challenges & best practices Information overload Multiple versions of the truth Data challenges Lack of agility Identify Business Objectives • Increase revenue • Minimize risk • Decrease costs Secure Executive Sponsorship • Identify pain • Understand policies • Determine metrics Initiate Small Projects • Align to objectives • Adopt what you need • Adapt how you see fit • Gain quick wins Evaluate Progress • Understand successes/failures • Shift as needed • Establish a ‘way of thinking’ Syncsort Confidential and Proprietary - do not copy or distribute

45. 2. Collaborate: challenges & best practices Lack of Common Terminology Organizational Barriers & Silos Isolated or Unknown Work Lack of Engagement Establish a Common Language • Define terminology – a ‘stake in the ground’ • Map information • Support with policies/standards Gain Broader Buy In • Bring stakeholders together • Build the structure, culture, ownership, steering groups, stewardship over time Enrich Information • Discover what you don’t know • Resolve differences • Enhance/annotate to increase insight Share Insights Regularly • Produce and share tangible outcomes • Highlight ‘wins’ • Demonstrate efficiencies & savings Syncsort Confidential and Proprietary - do not copy or distribute

46. 3. Quantify: challenges & best practices Hidden Activities Money, Time and Resource Waste Lack of Transparency and Trust Disconnect Between Process and Measures Identify Baseline Measures • Keep a focus on lean and agile • Define value accurately for the business Link to Business Performance • Create and refine streams of value • Transform culture through action and empowerment Monitor, Report and Remediate Issues • Continuously review • Ensure issues are visible and understood • Understand root causes • Address/resolve issues Quantify Impact of Changes • Demonstrate through clearly understood measures • Establish value continuously • Finish early, finish often Syncsort Confidential and Proprietary - do not copy or distribute

47. Summary

48. The accuracy of data directly impinges on any activity downstream – from analytics, reporting & dashboards, segmentation & targeting, customer care through to risk & compliance… in fact ANY business decision! DQ not only strengthens DG compliance; it also means you make SENSIBLE BUSINESS DECISIONS Summary Syncsort Confidential and Proprietary - do not copy or distribute

49. harald.smith@syncsort.com davinity.powis@syncsort.com

How to Strengthen Enterprise Data Governance with Data Quality

How to Strengthen Enterprise Data Governance with Data Quality

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