Data Quality: What you need to know to Create and Sustain a Data Quality Program

1. Data Quality: What you need to know to Create and Sustain a Data Quality Program

2. Panel Members • Daniel Wallace Manager, Financial InformaticsArkansas Blue Cross & Blue Shield • Gayle Bunn, Data Warehouse Analyst, EDWBlue Cross and Blue Shield of Idaho • Amit Bhagat, President & Principal ConsultantAmitech Solutions

3. Data Quality Panel Objectives: To share information and insight on: • Overall organizational approach to creating and sustaining data quality program

4. Panel Presentation • Please provide us with a brief overview of the overall approach to creating and sustaining the data quality program in your organization

5. What You Need to Know to Create and Sustain a Data Quality Program Daniel Wallace Manager, Financial Informatics Arkansas Blue Cross & Blue Shield Contact Info: Phone: 501-396-4090 Email: dpwallace@arkbluecross.com

6. Agenda • Creating a Data Quality Program • The People • The Scope • The Processes • The Tools • Sustaining a Data Quality Program • Policy • Communication • Demonstrate Value

7. Data Quality • Creating a Data Quality Program • The People • Knowledge of the Business • Multidiscipline Staff • Skill Set • Ability to handle large and complex datasets • Ability to test and verify systems processes to understand causes of data issues • Ability to query/profile data using SQL, SAS, Excel • Ability to communicate with business areas and management

8. Data Quality • Creating a Data Quality Program • The Scope • Importance of Defining • Likely to solve a real problem • Able to quantify value of DQ program • Where to Begin • System Level? • Process Level? • Subject Area Level? • Application Level? • Project Level?

9. Data Quality • Creating a Data Quality Program • The Processes (Assess, Improve) • Assessment • Data Profiling • Define DQ Rules • Define Measure (from DQ rules) • Improvement • Data Cleansing • Improve Processes • Measure Quality • Monitor Quality

10. Data Quality • Creating a Data Quality Program • The Tools • Purpose/Need • Understanding your data • Profiling and Rule Discovery • Data Standardization • Data Cleansing • Metadata Management • People Manage Data Quality not Tools

11. Data Quality • Sustaining a Data Quality Program • The Need for a DQ Policy • Policy Guidelines • Treat Information as a Product/Asset • Focus on the Business Side • Define Roles and Responsibilities • Resolution Management • Proactive Approach • Data Standards

12. Data Quality • Sustaining a Data Quality Program • Communication • Make/Break your DQ initiatives • Stakeholders • Their Role in DQ/DG Program • Successful DQ program must be done with them • Include all functional areas that create or use data • Regular meetings needed

13. Data Quality • Sustaining a Data Quality Program • Demonstrate Value & Communicate It • Identify DQ Issue to Target • Engage Management • Select Metrics to Measure, Establish Baseline • Implement Solution • DQ program can mitigate inefficiencies, excessive costs associate with poor data, compliance risks, improve customer satisfaction

14. Data Quality Gayle Bunn Blue Cross of Idaho

15. Biography • Gayle Bunn, MBA, PMP, BSEE • Data Warehouse Analyst • Enterprise Data Warehouse (EDW) • Blue Cross of Idaho • Responsible for EDW Data Quality, Support & Maintenance, Training, Customer Service, and Data Governance • Contact Info: • Phone: (208)331-7487 • Email: gbunn@bcidaho.com

16. Current Steps at BCI • Started small – EDW focus • Established data quality workflow • Established 1 automated touch point • Added initial data quality metrics • Timeliness • Completeness • Socialized timeliness • Socialized completeness • Data quality evolvedinto many flavors • Established S.M.A.R.T. data quality metrics • Performed ongoing process improvement • Major milestone occurred! • Data governanceand MDMemerges

17. 1. Started Small – EDW Focus We need better data quality! Enterprise Data Warehouse (EDW) Data Analyst Community Member Medical Dental Drug EDW Team Data Quality Review Team (DQRT) formed. We need to work together & discuss issues!

18. 2. Established Data Quality Workflow The data is still wrong! Mark Fixed! Data Quality Review Team (DQRT) Faster please! Data Analyst Community Manual Fix Enterprise Data Warehouse (EDW) Document data quality issues • SharePoint List • Title • Description • Assigned To • Resolved (yes/no) Yay! EDW Team Prioritize Wrong?

19. 3. Established 1 Automated Touch Point TP = Touch Point Enterprise Data Warehouse (EDW) Yay! Member Some of the data is missing! Medical Extract Dental X-form Can we have the data faster? Drug TP Load Can we have more data? 1 Automated Touch Point (Check for missing data) Cool! Hard to please! Stopload if data is notcomplete! We need Service Level Agreements (SLA’s)!

20. 4. Added Initial Data Quality Metrics More Data Delivered Yay! Enterprise Data Warehouse (EDW) Member What does “completeness” mean? New Touch Point Medical Vision Extract Grouper Dental X-form TP Sales Drug TP Fix Timeliness Jobs completed on time. Premium Load Very cool! Completeness Amount of data without noise. Automate Fix for Common Problems We need to socialize this! Noise = Missing data in Fact Tables

21. 5. Socializing 1st Metric - Timeliness EDW SLA - SharePoint I can tell when jobs finish! Manual: Track when weekly/monthly jobs complete. SQL Server Reporting Services (SSRS) Automate: Graph when jobs miss SLA. I can see where to improve!

22. -4 -3 -2 -1 Default Missing Error Not Applicable 6. Socializing 1st Metric - Completeness NOISE SQL Server Integration Services (SSIS) to SharePoint Track Noise in Fact Tables Dimension PK Value Automate: Track noise in data. Only 2.19% noise? The data is more complete than I thought! Count when dimension data is not available in a Fact record (PK<0). SQL Server Reporting Services (SSRS) Automate: Graph when noise issues occur. I can see where to improve! What do we mean when we say data quality anyway?

23. 7. Data Quality Evolved into Many Flavors No Noise (missing data) Reconciles Appropriately I have a data quality problem! Appropriate Data Matches Source You mean opportunity! What flavor? Correct Business Rules On Time Delivery Successfully Performed in BCI’s Enterprise Data Warehouse (EDW)

24. 8. Established Data Quality Metrics Data Quality Metrics Potential Data Quality Metrics • % data loads where data reconciles • # accuracy incidents Accuracy (Reconciles) Accessibility • % of Critical Data Fields provided • % data loads where data matches source • # consistency incidents Consistency (Match Source) Uniqueness • % total where duplicate records exist Timeliness (Right Time) Compliance • % data loads delivered on-time • # timeliness incidents • # of regulatory noncompliance data issues with HIPAA, PHI Integrity (Right Rules) • % load with Appropriate Business Rules Applied • # integrity incidents Efficiency • Avg. time taken for data quality issues to be resolved Validity (Right Data) • % loads with appropriate date range • # validity incidents VALUE Completeness (No Noise) • % records without noise (missing data) • # noise incidents

25. 9. Performed Ongoing Process Improvement Enterprise Data Warehouse (EDW) Completeness (no noise) Accuracy (Reconcile) Validate (matches source) Extract Data Sources Enterprise Service Bus (ESB) X-form TP TP TP Fix! Fix! TP Fix! Use Data Quality metrics to identify issues other TP’s don’t. Load Fix! Fix! Use Data Quality process to fix source issues.

26. 10. Major Milestone Occurred Milestone: No Issues! Data Quality Review Team (DQRT) Finally! Data Analyst Community Enterprise Data Warehouse (EDW) Yay! Yay! • SharePoint List • Title • Description • Assigned To • Resolved (yes/no) • Data Quality Area Yay! Yay! Yay! Yes! EDW Team There’s one in every crowd!

27. 11. Data Governance & MDM Emerges Master Data Management (MDM) Data Governance Data Governance is emerging around Data Quality Data Quality With Success: The small bird’s chirp of data qualitywas heard! MDM is emerging around Data Governance

28. Critical Success Factors at BCI Gain Steering Committee sponsorship Establish a clear Mission Statement/Purpose Develop Program Goals for the Team Establish cross-functional DQRT representation (including across IS) Create a non-blame, non-judgmental environment Use a divide and conquer approach to issue resolution (broad participation) Establish continuous improvement over time (Rome was not built in a day) Conduct regular meeting schedule, frequency dependent on need Appoint a data quality champion

29. “Data Quality”: What you need to know to create and sustain data a quality program • Amit Bhagat • President & Principal Consultant • Amitech Solutions • Contact Info: • Phone: 314-480-6301 • Email: Amit.Bhagat@amitechsolutions.com

30. Agenda • DQ Symptoms • Use Case • DQ Myths & Reality • DQ Design • Approach • Business Need • Define • Profile • Remediate • Sustain

31. DQ Symptoms • “The data is wrong – I will do it myself.” • “We spent $5 million on the ‘claims’ system and it still sends incorrect payments.” • “We get a different member month count depending on whom we ask.” • “We are not sure if our MLR is correct.”

32. Use Case • Business Problem • Ensure accurate risk scoring for membership under the ACA for payment transfer between carriers. • Data Profiling • Missing or incorrect diagnosis code in claims data. • Outcomes • Pay other plans, potentially 2% or more of loss ratio because we may "appear" healthier than others in our market. • Focus on diagnosis code as a critical data element.

33. DQ Myths & Reality • Reality • Quality requires people, process, culture, and technology to work in concert. • Quality is a “fit for purpose” process that delivers the highest data quality over time. • Myths • Quality is solved by technology alone. • Quality is an IT problem. • Quality is best fixed at the point of entry. • Quality is the sole responsibility of the data ”owners.” • Quality requires all data to perfect.

34. DQ Design: Approach

35. DQ Design: 5 Step Process 1. Business need 2. Define 5. Sustain Optimal DQ 4. Remediate 3. Profile

36. 1. Business Need • Acquire business goals • Identify levers • Identify components • Identify candidates for DQ Determine the scope and business relevance of DQ effort.

37. 2. Define: DQ Objectives & Measures • Identify completion criteria for current DQ iteration: • Reduce member duplicates by 10%. • Determine metrics to be developed: • What you are measuring (measure). • When you are measuring (milestone). • Why you are measuring (business impact).

38. 3. Profile • Identify specific tools / techniques to be used. • Review initial measures for relevance and accuracy. • Verify accuracy of what was intended vs. actual. • Analyze data for business rule conformance. • Profiling reports are analyzed, and root causes and business impacts are identified and reported. This step determines the exact sources, location, and types of techniques to use to assess DQ:

39. Technology Apply tools to cleanse and standardize data in the ETL process to ensure required levels of quality are met. 4. Remediate: Technology & Process Process & Standards • Develop and implement business processes • Develop work flows to fix bad data at source • Develop and implement data movement controls • Use cleansing & standardization tools • Develop audit, balance, and control • Integrate DQ with Enterprise Information Management program Consistent application of process and standards to outline the expectations for data quality across the enterprise. Develop the immediate and ongoing technical architecture and process components required to reduce or eliminate DQ problems.

40. Data Governance Provides the framework and ongoing oversight to enable effective management. 5. Sustain Change Management Implementation of various culture change management efforts to sustain data quality efforts. • This step covers the culture change, governance, and ongoing support and progress reporting of the DQ effort.

41. Summary • Data quality is a known, “for sure” problem. • Existing processes that create bad data must be addressed. Technology cannot be the only road to a solution. • People: • Perceptions of “doing bad things” are inevitable. • Manage resistance, politics, priorities. • Culture management mandatory. • Technology: • Integrate with EIM. • Lots of new stuff!

42. Share Your Experience Panel Members • Daniel Wallace Manager, Financial InformaticsArkansas Blue Cross & Blue Shield • Gayle Bunn, Data Warehouse Analyst, EDWBlue Cross and Blue Shield of Idaho • Amit Bhagat, President & Principal ConsultantAmitech Solutions

43. Question # 1 • How does data quality program fit into your strategy for information management?

44. Question # 2 • Are you able to produce "one version of the truth" throughout the whole company, or do various versions surface from different areas? • What subject areas are you currently managing in your data quality program?

45. Question # 3 • Are data definitions established at the individual, department, or enterprise level? • Are you leveraging data governance program for data quality? How?

46. Question # 4 • Describe what impact data quality has on the delivery of business value through analytics and BI? • Tell us how your organization manages data quality and how it responds to data quality issues (as a matter of project work, daily operations, planning, etc). • Does your organization have ways of measuring or quantifying “poor quality” and the results of poor quality data?

47. Question # 5 • In your organization, how do the various stakeholders around any given data quality project work together?

48. Question # 6 • Have you integrated master data in your DQ program? • What was your approach? • How did it go? • Successes? • Lessons learned?

49. Question # 7 • What are your next steps? • New efforts toward data quality?