The Importance of Data Governance

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1 The Importance of Data Governance Hans Heerooms Information Builders Copyright 2011, Information Builders. Slide 1

2 Objective of this presentation Explain the concepts and benefits of Enterprise Information Management (EIM), Master Data Management (MDM) and Data Governance An overview of iway Software approach to EIM, MDM and Data Governance A Case Study of MDM and Data Governance using iway Software

3 Enterprise Information Management What s it all about? Regulatory compliance Comprehensive customer knowledge Improved customer service Consistent reporting Improved competitiveness Improved operational efficiency and reduced costs Improved decision making Improved risk management

4 What is Data Governance? Wikipedia: Data governance is an emerging discipline that embodies a convergence of data quality, data management, business process management, and risk management surrounding the handling of data in an organization. Through data governance, organizations are looking to exercise positive control over the processes and methods used by their data stewards to handle data From MDM Institute: The formal orchestration of people, process, and technology to enable an organization to leverage data as an enterprise asset From Data Governance Institute Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.

5 Data Governance Three Pillars People Organizations need executive sponsorship Process Documented repeatable processes and procedures Technology Data Integration, Data Quality, Data Synchronization, and Data Management

6 A Reality Check Less than 15 percent of organizations surveyed understand the sources and quality of their master data, and have a roadmap to address missing data domains..more than 55 percent of the respondents in the survey manage their data quality on an ad-hoc basis Only 18 percent of respondents have an enterprise data management strategy to ensure that data is treated as an asset to the organization. Most respondents handle data at the department or functional level and do not have an enterprise view of their master data. Almost 60 percent of respondents have no strategy to integrate data across operational applications

7 Only 10 percent of respondents have the ability to share data that was mastered in a master data hub A Reality Check About 55 percent of respondents said they perform this integration on an ad hoc basis, and in many cases, it is done manually Approximately 50 percent of respondents spend manual efforts cleansing and normalizing data..organizations need the ability to standardize data for customers, products, sites, suppliers and financial accounts; however, less than 10 percent of respondents have technology in place to automatically resolve duplicates

8 Data Governance Maturity Model Collaborative & Proactive Problem Solving Standards Enterprise-wide Information Is a 2nd (or 3 rd or ) Class Citizen "We COULD Do This in a Complete and Consistent Way, If We Could Ever Justify It" Unaware "Data Has Business Value and Risk???" "We Live and Die by High- Quality, Relevant Data Let's Build an Infrastructure to Make It Available to Everyone" Opportunistic "Let's Add More Resources So We Can Service All These One-off Requests" "Information Is Our Greatest Asset" Parochial & Reactive Problem Solving

9 EIM Lifecycle Monitoring and reporting Data understanding Ongoing monitoring Deviance identification Profiling Metadata understanding Issues causes identification KPI definition Parsing Association (householding) Format correction Deduplication / identification Content evaluation Unification Enrichment Standardization Context-based cleansing Automatic correction Data enhancement Data cleansing

10 iway Software Approach to Data Governance

11 Practical Approach to Data Governance Prioritize Prioritize areas for business improvement Maximize Maximize availability of information assets Create Create roles, responsibilities, and rules Improve Improve / Assure information asset integrity Establish Establish accountability infrastructure Convert Convert to a master data -based culture Develop Develop a feedback mechanism for process improvement

12 Establish Accountability Infrastructure

13 Convert to a Master Data Based Culture Master Data are the facts describing your core business entities: customers, suppliers, partners, products, materials, bill of materials, chart of accounts, location and employees. It is the high value information an organization uses repeatedly across many business processes. Master Data Management decouples master information from individual applications and ensures consistent master information across transactional and analytical systems MDM solutions are software products that: Support the global identification, linking and synchronization of customer information across heterogeneous data sources Create and manage a central repository or a databasebased system of record Enable the delivery of a single view for all stakeholders An MDM program potentially encompasses the management of customer, product, asset, person or party, supplier and financial masters.

14 Develop a Feedback Mechanism for Process Improvement Compare and trend analysis of information quality over time

15 iway Enterprise Information Management (EIM) Data Governance within the iway Software Framework Consulting Strategy Roadmap Education Implementation Mentor Advocacy Best Practices Experience Data Governance Data Policy Roles & Responsibilities Business Process Stewardship Business Intelligence (Analytics/Operations) Master Data Management (Single view of the Business) Data Quality Data Integration System, Data, & Intellectual Fragmentation (Costly Business & Technical Problems) Profile Cleanse Match Remediate Data Access Data Movement MD Center DQ Center DQ Profiler DQ Portal Service Manager Real Time & Batch

16 Case Study Mount Sinai Medical Center

17 About Mount Sinai Medical Center Founded in ,171-bed tertiary- and quaternary-care teaching facility Mount Sinai School of Medicine, established in 1968, has more than 3,400 faculty in 32 departments and 15 institutes, and ranks among the top 20 medical schools. Nearly 60,000 people were treated at Mount Sinai as inpatients last year, and approximately 530,000 outpatient visited last year.

18 Data Quality Challenges 1. Mount Sinai Medical Center knows that ailments don t only strike its patients. 2. As a technology leader, Mount Sinai devotes a great deal of time to curing data quality problems as well. 3. As other businesses grow, so did Mt. Sinai via acquisition of another hospitals (and its attendant application portfolio and infrastructure). 4. Inconsistencies in these systems as patient records are created, updated and exchanged. 5. As high as 20 percent of patient database contains incorrect and duplicate information.

19 Benefits 1. Ensure that appropriate information to guide medical decisions is available at the time and place of care. 2. Improve health care quality, reduce medical errors, and advance the delivery of appropriate, evidence-based medical care. 3. Reduce health care costs resulting from inefficiency, medical errors, inappropriate care, and incomplete information 4. Promote a more effective marketplace, greater competition, and increased choice through the wider availability of accurate information on health care costs, quality, and outcomes

20 Implementation - Phased Approach 1. Phase I: KEANE patient management system K records 2. 10% of data needs investigation 3. Standardization and Cleansing data 4. Matching and Merging 2. Phase II: CERNER patient management system M records % of data needs investigation 3. Standardization and Cleansing data 4. Matching, Merging and Linking 3. Phase III: MPI Synchronization 1. Synchronize multiple MPIs (KEANE, CERNER, EPIC, IDX) 2. Integration with iway Service Manager 3. Real-time integration with HL7 Exchange Gateway 4. Real-time Issue Resolution (Synchronization & Linking)

21 Data Stewardship Architecture

22 DG Reference Architecture Phase 1

23 DG Reference Architecture Phase 2

24 DG Reference Architecture Phase 3

25 Project Success Story 1. Delived on all phases: Cleansed data in CERNER System in 3 months when compared to 18 months; Integrated KEANE and CERNER; Built an Enterprise Master Patient Index across KEANE, CERNER, IDX and EPIC systems 2. Improves the coordination of care and information among hospitals, laboratories, and physician offices. 3. Data Quality Plans and Business rules for continuous data quality monitoring and cleansing 4. Helped in progressing to Master Data Management 5. Helped in establishing Data Governance in Mount Sinai Medical Center

26 Demonstration iway Data Governance in Action

27 iway EMPI Center

28 Governance Management

29 Governance Issue Resolution (Cleansing / Standardization)

30 Governance Issue Resolution (Merging)

31 EMPI Center Patient Search

32 EMPI Center Patient 360 View

33 EMPI Center Patient Fuzzy Search

34 Governance Issue Auditing and History

35 Governance Reports

36 Thank you! Question & Answers?

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