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Data analytics and Access Program Recommendations
- 1. 1 Current as of 10/19/2020 Phase 1B Recommendations and Roadmap DAAP – Data Architecture and Analytical Platform February 5, 20XX
- 2. 2 Current as of 10/19/2020 Focus Areas The Data Driven Organization Summary of Findings TOWS Analysis Voice of the Enterprise Primary Issues Data Strategy Business Requirements Architecture Current Phase 1B Phase 2 Transition Phase 3 Transition Program Definition Appendix
- 3. 3 Current as of 10/19/2020 The Data Driven Organization ❑ What is it? ᵒ Use of Analytics to make fact-based business decisions ᵒ Gather relevant data from all aspects of the business ❑ What does it Mean? ᵒ High variety of data for analysis ᵒ Access as needed ᵒ Data is centralized and organized ᵒ Tools allow for intuitive reasoning and are easy to use ❑ How? (Organization and Resources) ᵒ Capabilities are challenged ᵒ Capabilities are transformed
- 4. 4 Current as of 10/19/2020 Summary of Findings
- 5. 5 Current as of 10/19/2020 Threats •Loss of relevance •Clients taking data and doing their own analytics •Lack of interest to pursue work associated with data strategy •Lack of funding available to implement data strategy •Client belief that as a captive malpractice insurance provider that it is impervious to outside competition or changes in the healthcare industry Opportunities •History and Size of current base can entice new Institutions to use Client for business intelligence and analytics •Access to large hospital population provides sizable denominator base •Increase in availability of data from 3rd parties allows for increased analysis and reporting •Strong relationships with current institutional base •Changes in technology allow for increased ease and efficiencies in analysis of data Weaknesses •Underlying data structure and organization does not support requisite reporting and business intelligence •No ‘single source of the truth” •Single point of addressing analytics issues •Structure on which CRIT was developed has been retired by the vendor •Extensively modified vendor software inhibits upgrade path and scalability •This is the 4th time an assessment of this type has been undertaken – this may indicate a lack of commitment •Report KPIs have not changed, differ •No internal KPIs Strengths •Strong analytics base and understanding of needs of the customer •Strong analytics skillset available in-house •Repeatable report and query types make generation of analytics easier •Age and size make Client a perceived force to be reckoned with TOWS Analysis
- 6. 6 Current as of 10/19/2020 Who we Spoke With Name Department Date IT IT Patient Safety IT IT CEO Patient Safety Graphics Patient Safety Underwriting Patient Safety Strategies Name Date Finance CFO Patient Safety CMO Claims Claims Finance Patient Safety IT CIO IT IT IT
- 7. 7 Current as of 10/19/2020 Voice of the Customer What is the right Analytics tool for our business? I rely on institutional knowledge for data access & data usage..… wish there was a better way. What is this data field? Has any other team built a similar metrics that we can leverage? It takes a long time and cost a lot to add data to EDW. Where should I look for my data? There are no guiding principles for us (users) to do what we should be doing! Wish I could get data from EDW whenever I want to and faster!
- 8. 8 Current as of 10/19/2020 Organizational •Reluctance to change •Lack of interest; it’s “Good Enough” •Reactive rather than Proactive •Ineffectively align (e.g., silos) •Lack of effective coordination and collaboration across departments •Business Units and organizations leery to share data •Difficult to gain consensus on Assumptions & Parameters •Difficult to match data to business needs •Primary Care example •Difficult to establish business rules and drivers •Distrust in IT’s ability to serve organization •“Just give me all the transaction data and I’ll do the analytics…” Voice of the Enterprise
- 9. 9 Current as of 10/19/2020 Process •Lack of governance •Unknown ownership of data, subject, or application •Exhaustive manual manipulation required to produce a single report (pervasive observations and comments) •2—3 months to develop typical report •1 year to develop Benchmark report •Ad-hoc, one-off nature of report builds •No library for standard queries •Business rules differ •Business Rules embedded in scripts, ETL, queries •Business Rules also differ due to latency and developer •Coding differences •Manual collection and application data •3rd party provided •Location •Practice •“Be able to predict before it happens” •“Organization grapple with what it does not know and cannot solve for” •“Lack of Clarity” •“Everything requires an analyst.” Voice of the Enterprise
- 10. 10 Current as of 10/19/2020 Technology •Data Architecture that does not support Reporting and Business Intelligence, or move into Advanced Analytics •No ‘single source of the truth” •Structure on which CRIT was developed has been retired by the vendor •Extensively modified vendor software inhibits upgrade path and scalability •Poor performance of applications, queries, and scripts •Cannot rollup or aggregate properly •Cannot apply corrections to source systems •Confusing mart structures, naming conventions •Distrust of the quality and veracity of data, reports; irreconcilable differences •Manual data quality maintenance •80% time spent cleansing & prepping data •Difficulty providing historical views, as-of, as-was processing •Shadow IT (pervasive observations) •Data held locally that is unavailable to the enterprise •Individual’s knowledge not shared outside business unit •Addresses inability of IT to service organization •Cannot leverage proper resources •3rd Party data •Locally held data Voice of the Enterprise
- 11. 11 Current as of 10/19/2020 Client Data Strategy Crawl • “Do better with what we have…” • Restructure Data Architecture to support initial set of reports • Bring in initial 3rd Party Data Walk • “Use current data in a way that is more meaningful…” • Additional reports • Iterative and Incremental Expansion of supporting Data Architecture Run • Systematic use of 3rd Party Data • Additional reports • Iterative and Incremental Expansion of supporting Data Architecture Fly • Advanced Analytics • Predictive and Prescriptive Analytics • Data Mining • Additional Supporting Technologies required (e.g., Data Lake, Internet of Things, Big Data) Identify Risk Understand Risk Mitigate Risk Predict Risk Backward Looking Forward Looking
- 12. 12 Current as of 10/19/2020 ❑ Providing Consistent, Integrated Data ᵒ A Single Source of Truth ᵒ Eliminating the need for individual Departmental Marts ❑ Making Data Available Across the Enterprise ᵒ Ensuring that everyone has access to the same information ᵒ Eliminating the need for ‘Shadow’ Marts ❑ Providing Consistent Reporting ᵒ Allowing for differences in methodology, the same question asked of different people should yield the same result ❑ Governance ᵒ Ensuring the ownership and quality of the data ❑ Aging Technologies ᵒ Increasing functions and features ᵒ Reducing costs and maintenance issues Primary Business Requirements
- 13. 13 Current as of 10/19/2020 Same Data Used by All Resources Manifestations Issue Examples • Root Cause People Process Technology Proposed Solution • Proliferation of conflicting data • Terminology • Methodology • Business Rules • Metrics • SMT cannot make informed business decisions • Incurred Loss (e.g., different Incurred Loss results produced by CRIT and BO) • Case Rates per 100 PCY • Finance • Patient Safety (Claims) • Lack of Governance • No Metadata Management (e.g., business glossary; derivations, business rules, and metrics catalogs) • No “Single Source of Truth” • Tolerance of substandard behaviors • Necessity for workarounds • Locally held and maintained data • Data not shared across enterprise • Reports produced using different tools leads to discrepancies that are difficult to be adequately reconciled • Differing or inconsistent definitions and derivations • Finance • Claims • Patient Safety • Arbitrary application of business rule, metrics, and derivations • Lack of automation leads to extensive manual manipulation of data • Lack of integrated data • Excel is not an enterprise database • Business rules, metrics, and derivations embedded in ETL, Scripts, and Application customizations over time, inconsistently • Uncouple embedded business rules, metrics, and derivations from ETL and script and queries • Create Business Glossary with conformed, agreed upon definitions • Create Metric Catalog using conformed definitions and establishing precise derivations • Create new ETL to extract source data • Create new underlying data structures Provide consistent, integrated data
- 14. 14 Current as of 10/19/2020 Data from Same Source Available Across Enterprise Create “Single Source of Truth” Manifestations Issue Examples Root Cause People Process Technology Proposed Solution • Inability to incorporate additional departments' data • Inability to handle specialized or boutique institutions • SMT cannot make informed business decisions • Underwriting and Claims difficult to link • Premium Collected versus Payouts • Primary Care • Manually tracking of Trial Results • Locally held Finance Data • Lack of governance • Mastering of data Location • Practice • Reference Data (e.g., Taxonomies, Classification Schemes, Hierarchies) • Data models • Ineffectively structured database and data marts • Tolerance of substandard behaviors • Necessity for workarounds • Locally held and maintained data • Data not shared across enterprise • Individual departments focus on solving their critical issues • Do not recognize opportunities for collaboration • Necessity for workarounds • Locally held and maintained data • Data not shared across enterprise • Improperly structured data does not support historical analyses • Inability to support competing perspectives • Inability to properly aggregate data • Create “Single Source of Truth” • Procurement of Vendor MDM Solution • Restructure data to support integrated, enterprise reporting • Aggregations • History • Competing Perspectives • Dimensional Make that data available across the enterprise
- 15. 15 Current as of 10/19/2020 Same underlying data supports Competing Perspectives Manifestations Issue Examples Root Causes People Process Technology Proposed Solution • Lack of integrated enterprise data repository • SMT cannot make informed business decisions • Quarterly Report • Inability to perform as-of, as-was reporting • Lack of governance • No Metadata Management (e.g., business glossary; derivations, business rules, and metrics catalogs) • Mastering of data Location • Practice • Reference Data (e.g., Taxonomies, Classification Schemes, Hierarchies) • Data models • Library of Standard Reports • Catalog of Standard Metrics • No “single source of the truth” • Tolerance of substandard behaviors • Plethora of tools and scripting confounds ability to provide consistent reporting • Cobbling together reports using data from various tools • Necessitates multiple queries to satisfy singe requirements to avoid inflating reported values • Lack of automation leads to extensive manual manipulation of data • Current complement • SAP Business Objects • Tableau • SAS • Scripts • Inability to support competing reporting perspectives • Inability to properly aggregate data • Inability to report on requisite dimensions • Create “Single Source of Truth” • Incorporated governance into SDLC • Gates • Create new report definition, implementation, and execution parameters • Create standardized parameter-based reporting structures • Portal • Self-service • Facilitate Ad-hoc requests Provide Consistent Reporting
- 16. 16 Current as of 10/19/2020 Instill Quality, Reliability, and Confidence Manifestations Issue Examples Root Cause People Process Technology Proposed Solution • Insufficient Data Governance in place • Ownership • Accountability • Metadata Management • Mastering of Data • Lost Productivity • Loss of Confidence in Data • Governance in- place • Business Analytics Meetings • Data Stewardship Meeting • Underwriting Policy Task Force • Prevalent distrust of quality of data available • No data ownership assigned • No data-related accountabilities assigned • Relationship between Business and IT • Lack of intolerance for substandard behaviors • Necessity for workarounds • Locally held and maintained data • Data not shared across enterprise • Difficult for teams to resolve inter- departmental issues • Lack of automation leads to extensive manual manipulation of data • Inability to effectively manage and change business requirements • Inability to perform Cross- business unit requirements analysis • Lack of automation • Data changes manually maintained in Excel spreadsheet that reside on a local machine • Changes not reflected in source systems • Changes are not fed back to source systems for update • Stand up Data Governance Organization • Automate Governance Processes Governance
- 17. 17 Current as of 10/19/2020 Ease of Use and Maintenance Costs Associated Manifestations Issue Examples Root Cause People Process Technology Proposed Solution • CRIT and other technologies have reached point of obsolescence • Lack of vendor support • Lack of upgrade path • Can no longer sell capabilities to clients • CRIT • Has become ostensibly unusable • Suffers from extremely poor performance • Current MDM is custom developed software • No accountability assigned for tool usage • Lack of funding for upgrades or replacement • Tolerance of substandard behaviors • Causes extensive reliance on manual manipulations to provide reporting • Severely curtails ability to provide timely reports from CRIT-held data • CRIT • Seen as being accurate • Extremely poor performance • Unsupported Technology (Microsoft ProClarity) • MDM • Home-grown, custom software • Not extensible, scalable, or flexible • Lack of upgrade path • Business Objects • Out of rev • Thoroughly decompose CRIT capabilities and outputs • Instantiate CRIT- provided capabilities using Business Objects • Provide client- facing portal to new capabilities • Articulate and instantiate proper security • Upgrade Business Objects to current release Aging Technologies
- 18. 18 Current as of 10/19/2020 Architecture
- 19. 19 Current as of 10/19/2020 Current State
- 20. 20 Current as of 10/19/2020 Phase 1B State
- 21. 21 Current as of 10/19/2020 Phase 2 Transition
- 22. 22 Current as of 10/19/2020 Phase 3 Transition
- 23. 23 Current as of 10/19/2020 Future State
- 24. 24 Current as of 10/19/2020 DAAP Program Definition
- 25. 25 Current as of 10/19/2020 Phase 1B •Deliver prioritized set of 6 reports targeted for internal RMF needs •Build foundational Data Management components •Build EDW with focus on subset of Data entities (claims, case financials, OPE, Reference data) •Bring RMF and strategies data into EDW for those entities •Build basic business glossary and metrics catalog (foundation for Data Governance) Phase 2 •Deliver next set of 8 reports for internal RMF needs •Evaluate and migrate CRIT reports to EDW •Build external portal to deliver CRIT reports •Bring MMS data into EDW •Extend and scale foundational Data Management components •Build and Scale data governance Phase 3 •Migrate rest of existing BO reports and Sunset existing BO reports/environment •Bring external 3rd party ANA data •Enhance CBS process to include 3rd party data •Build CBS as a batch driven process DAAP Program Definition
- 26. 26 Current as of 10/19/2020 Project / Phase Project Provide Consistent, Integrated Data Make Data Available Across the Enterprise Provide Consistent Reporting Governance Aging Technologies 1B/A Deliver prioritized set of 6 reports targeted for internal RMF needs X 1B/B Build foundational Data Management components X X X X 1B/C Build EDW with focus on subset of Data entities (claims, case financials, OPE, Reference data) X X X 1B/D Bring RMF and strategies data into EDW for those entities X X X 1B/E Build basic business glossary and metrics catalog (foundation for Data Governance) X X Project-Theme Connection (Phase 1B)
- 27. 27 Current as of 10/19/2020 Project / Phase Project Provide Consistent, Integrated Data Make Data Available Across the Enterprise Provide Consistent Reporting Governance Aging Technologies 2/A Deliver next set of 8 reports for internal RMF needs X X X 2/B Evaluate and migrate CRIT reports to EDW X X X 2/C Build external portal to deliver CRIT reports X X X 2/D Extend and scale foundational Data Management components X X 2/E Build and Scale data governance X X 3/A Migrate rest of existing BO reports and Sunset existing BO reports/environment X X X X 3/B Bring external 3 rd party ANA data X X 3/C Enhance CBS process to include 3 rd party data X X 3/D Build CBS as a batch driven process X X X X Project-Theme Connection (Phase2-3)
- 28. 28 Current as of 10/19/2020 Enabling Paradigm Definition Providing Consistent, Integrated Data Making Data Available Across the Enterprise Providing Consistent Reporting Governance Aging Technologies Single source of truth across enterprise Ensure consistency, currency, meaning, integrity and quality of data used within or across multiple business areas or processes X X Historical reporting Accurate historical data & DB structures to support “AS-WAS” and “AS-IS” reporting. X X X RMF and Strategies data stored together. The EDW data model supports multi-tenancy, thereby co-locating RMF and Strategies data X X Create Metric Catalog/Data Dictionary Create metric catalog/business glossary using conformed definitions and establishing precise derivations X X CRIT remediation Design and develop an alternative for CRIT which would satisfy current and future needs of Client and Strategies clients. X X Parameter Driven Portal Create two separate parameter driven portals for reporting needs. One portal for RMF (internal) and second for Strategies client. X Loss Abstract document/report in BO Ensure Loss Abstract document is available through SAP Business Objects X X SAP Business Objects XI R 4.1 Upgrade SAP Business Objects to XI R 4.1 version X X 3rd Party Data Create 3 rd Party Data Integration and Enrichment Capabilities X X Create Data Quality Engine Metadata driven quality checks on critical data elements. X X Data Standardization Apply data cleaning and standardization rules. Ensures data from multiple sources are stored in a common format. X X Audit Balance & Control (ABC) metrics Perform reconciliation at entity level as data processes through different data management functions/processes X X X Requirements Satisfaction
- 29. 29 Current as of 10/19/2020 Phase 1B Project Key Objective Key Deliverables Architecture Modification Revise Architecture to support new components ▪ Installation of any necessary hardware and software components EDW Build EDW with focus on subset of Data entities (claims, case financials, OPE, Reference data) ▪ Initial EDW capable of supporting Phase 1B requirements Reports Deliver prioritized set of 6 reports targeted for internal RMF needs ▪ Defined reports Data Management Foundation Build foundational Data Management components ▪ Foundation for Data Management and Governance RMF Strategies Data Bring RMF and strategies data into EDW for those entities ▪ Inclusion of data in the EDW allowing for increased reporting and analytics Business Glossary Build basic business glossary and metrics catalog (foundation for Data Governance) ▪ Business Glossary and Taxonomy
- 30. 30 Current as of 10/19/2020 Phase 2 Project Key Objective Key Deliverables RMF Report Deliver the next set of 8 reports for RMF Needs ▪ Defined reports CRIT Data Evaluate and migrate CRIT Data and Reports to the new architecture ▪ CRIT data available within the EDW for analytics and reporting Portal Build the external portal to make CRIT reports available ▪ External portal defined and built. Capable of handling internal and external report queries. CRIT reports for external users included MMS Data Bring MMS data into the EDW ▪ MMS data now available for reporting and analytics Data Management Extend and scale foundational Data Management components ▪ Data Management paradigm capable of supporting all new data within the EDW Data Governance Build and Scale Data Governance ▪ Increased Data Governance capabilities and functionality
- 31. 31 Current as of 10/19/2020 Phase 3 Project Key Objective Key Deliverables Reports Migrate remaining BO reports and sunset existing BO reports and environment ▪ All reports operating within new environment. Enhanced reporting and analytic capabilities 3rd Party ANA Integrate 3rd party ANA data into EDW ▪ Increased reporting and analytic capabilities based on increased data availability CBS Enhancements CBS process enhanced to include 3rd part ANA data ▪ Increased reporting and analytic capabilities based on increased data availability CBS Batch Rebuild of CBS as a batch driven process ▪ Reduced processing time for CBS data
- 32. 32 Current as of 10/19/2020 Next Steps • Ensure DAAP Roadmap and Strategy are aligned with Business Strategy • Complete Socialization with Executive Stakeholders • Jump start Phase 1B by: • Detail design of reports to be delivered • Begin definition of EDW data model • Determine resources necessary to support definition of data dictionary and metrics catalogue • Define success metrics for Data Governance • Determine and acquire resourcing
- 33. 33 Current as of 10/19/2020 Appendix
- 34. 34 Current as of 10/19/2020 Reporting Business Units Impact Results Finance Underwriting Claims Strategies Patient Safety Client Quarterly "CEO" Report X X X Exhaustive manual effort required to assemble Independent data collection form various sources leads to discrepancies that are unable to be adequately resolved Different understanding of definitions and derivations SMT unable to make informed business decisions Cost of lost productivity (1 month to prepare) Client Institution Report X X X Inability to incorporate additional departments' data Inability to handle specialized or boutique institutions Lack of automation leads to extensive manual manipulation of data Loss of relevance to clients Distrust of data presented to clients Cost of lost productivity (2-3 months to prepare) Inability to perform Cross-institution analysis Claims Dashboard X Manually created Excel spreadsheet that resides on a local machine Excel is not an enterprise database Need to be institutionalized Needs to source data from enterprise repositories SMT cannot make informed decisions about KPIs and Metrics Primary Care Report X X X New report meant to ascertain Primary Care Issues Location and Practice not supported by current data repositories (Location held locally in Excel Spreadsheet) SMT cannot make informed business decisions about PSO opportunities, claims management, case financials, and Underwriting exposure Excel is not an enterprise database Specialty – OB, ED, Surgery X Manually created Excel spreadsheet that resides on a local machine Distrust of data quality and veracity Inability to properly ascertain issues pertaining to specialties Loss of relevance to clients Excel is not an enterprise database Claim rates by specialty, trend report X X X Historical exposure of trends and claims Inability to dynamically set premiums Inability to fully understand trends pertaining to exposures and claims Inability of SMT to make informed decisions Phase 1B Reports
- 35. 35 Current as of 10/19/2020 Report Description Current State Future State Department(s) Contacts Client Quarterly "CEO" Report Mark Reynolds sends out quarterly email report to Board Members and Quality Leaders -- this report includes sections on Claims Management, Current Financial Standing, Corporate Achievements of Note. In particular, the Claims Management section includes trend data (with comparisons to past benchmark data) for closed and asserted cases -- for several KPIs, including Closed-With- Payment; %High-Severity, Payment->$1M. Data pulled/submitted independently by Patient Safety, Finance, Claims -- collected and organized by Communications. Some disagreement between numbers from different sources. Some philosophical disagreement about how meaningful it is to report quarterly numbers and how they should be projected/interpreted. Address issues in "Current State." Define whole-brain consensus spec with definitions and single source of data (along with underlying data structures and data governance). Patient Safety, Claims, Finance Jonathan, Beth, Sean Claims (frequency, indemnity, closed cases) case financials trends coding taxonomy policy-coverage not needed billing financials not needed Client Institution Report Client Patient Safety leadership, along with data analyst, and Patient Safety Director, meet with Quality/Safety leadership for each insured institution 2x/year (~ 20 meetings each time). The purpose of the meeting is to inform the institution about trends in their claims data, highlight any emerging risks or things the institution "needs to know." And, to align the presentation with Experience Adjustments (Spring) and present topics of interest to the institution (e.g. HIT Risk or Primary Care Risk). In Spring, data analyst produces "standard" claims summary Excel book, which is reviewed with the institution contact. Areas of interest/focus are identified; further data queries and analysis done iteratively until data is presented. In addition, PT Safety Director reads/reviews specific claim summaries and is prepared to talk about them. Sometimes, a Claims Rep presents, too. If there is a specific topic of interest, data/slides are prepared for that topic, as well. "Boutique" institutions may get review of overall Client data or customized presentations. In 2016, there will be 2 meetings per institution. The Spring meeting will involve both Patient Safety and Finance and include review of claims data, as well as Experience Adjustment. Fall meeting will be more topical. A goal is to reduce amount of customized work. Q: Involvement of Claims Department? Q: How to handle boutique institutions? An automation opportunity is initial data pull/reporting book (for each institution). Patient Safety, Finance, Claims Winnie, Sean, Carl. coding taxonomy contributing factors - compare to Harvard and CBS peers org claims case financials Claims Dashboard Provides Claims Department (Beth, Carl, Kay) with information needed to track claims activity -- includes trends of cases/defendants over time by different types of dates; losses/defendant sliced by firm, etc. Astrid has built SAS data set and uses Microsoft Excel as front end. Make Production-ready. Verify what works well, what doesn't, and what new KPA's/displays are wanted (by Claims). Claims Beth Phase 1B Reports Detail
- 36. 36 Current as of 10/19/2020 Report Description Current State Future State Department(s) Contacts Primary Care Report Primary Care is a Strategic focus for Client. The content of a Primary Care "Report" will need to be defined-- interventions will focus in a number of areas, but especially Patient Engagement and Referral Management -- reports could focus on these areas, building on Diagnostic Process of Care and Referral Process. Custom analyses. Inability to know where PCPs are practicing-- currently, Client data does not include concept of "practice." A high-level report. What is really needed is clear understanding of what business owner (Carol) wants to be able to know about Primary Care. And, data definition/acquisition/mapping for PCP, practice so that we can report on this data, i.e. a Primary Care Data Mart. Deliverable here may be a data mart with Excel/Tableau front end. Underwriting is another involved stakeholder here. And, we need to understand implications for MMS. Patient Safety, Underwriting Carol, Caren- Elise Gaps - site & location not available for enterprise Claims Case financials Org Claims has location-site stored either in CMAPS/Excel Patient Safety - OPE - also captures primary care practice sites UW - also captures location-site information Need to identify master record for location-site and also build hierarchy Specialty – OB, ED, Surgery Prepared for Chief's convening. Includes trend/comparison data for claim rate (per 10K births), academic versus community. Benchmarked against CBS Denominator (and numerator) data required preparation/cleaning -- knowing where birth occurred versus sponsoring org was key. Automate this. Note: denominators are different for different specialties (ED, Surgery). OB uses births from AHA. Patient Safety OB - Lisa, Tom Beatty ED – Jay Scherer Surgery – Bill, Kathy Dwyer Claim rates by specialty, trend report In 2015, Elena/Astrid used our Claim Risk Model to plot observed claim rate trends by specialty, broken down by Academic/Community status. Using the model, they were able to predict what the claims rate should be (and compare it with the observed rate). What was most useful about this exercise was the ability to look at each specialty's trend lines and see any interesting or concerning patterns. Automate this. Patient Safety Jonathan, Astrid, Elena Phase 1B Reports Detail
- 37. 37 Current as of 10/19/2020 Data Layer Future-state EDW – BI Delivery Framework Analysts Power Users/Trading partner data extracts Scorecards, Dashboard, holistic view of business 1 more additional level of detail plus pre built reports Detailed data, investigative analysis, helps setup reporting capability for user community User Community Interest Detailed base layer (Atomic 100% data elements) Data Refresh Frequency Monthly Daily Weekly Daily Dashboards Information Consumption Layer BO WebI BO Universe / Extracts Framework EDW – Single Source of Truth for all Information Consumption needs Executives ✓ Atomic detailed data serves Power Users who are interested in detailed data investigation serviced through BO Universe/Extract framework ✓ Most Used data layer serves Analysts who are interested in pre built reports serviced through BO WebI. ✓ Aggregate data layer serves Executives who are interested in scorecards, dashboards and higher aggregate business metrics serviced through dashboards. Most Used data layer (60% data elements) Aggregate data layer (20% data elements) Sandbox