MinnesotaHelp.Info™ Data Management Lessons Learned

1. MinnesotaHelp.Info™Data ManagementLessons Learned Michael Charney, Manager Data Management HousingLink Posted 6-14-05

2. “Connecting People of All Ages to Community Help”

3. “Connecting People of All Ages to Community Help” Connecting Consumers Web Access Other Tools Resource Database Professionals Providers

4. Our Mission Ensure the highest possible level of consumer satisfactionby providing quality data to consumers that identifies the best serviceslocally availableto meet consumer needs We Need Your Help!

5. “Connecting People of All Ages to Community Help” Data Management Challenge • Data migrated initially from another entity • Requires a lot of cleanup • The most comprehensive data in the state, therefore, the most data to be maintained • Maybe 20,000 or more sources of data – individual providers plus external databases • Multiple input modes – Paper surveys, email surveys, Provider Portal, emails to ProviderUpdates@HousingLink.org, paper catalogs, MS Word catalogs, mailers and other marketing literature, newsletters, web sites, spreadsheets, and MS Access databases • Original data management tools designed for use by I&R specialists not data management specialists – Philosophy of one migration followed by record level updates • We don’t have programmatic access to the database • Batch infusions of data -- E.g. 24,000 DHS Licensees with quarterly updates, weekly status changes, and mixed update rights • Integration of Data from SME remote systems – E.g. Housing Registry • Batch extractions of data – MC Access database for LMIC DataNet • Public trust

6. “Connecting People of All Ages to Community Help” Superior Resource Database • State endorsed and state sponsored • Long term commitment • Broad social service focus • 29,000 services; 12,500 agencies (12/12/2004) • Non-profits, for-profits – adding DHS licensed providers, Medicare & Medicaid • Key partnerships and expanding collaboration • Minnesota Board on Aging, Department of Human Services • Senior LinkAge Line TM, Disability Linkage Line TM • Minnesota Area Associations on Aging, and the Metropolitan and Southwest Centers for Independent Living • HousingLink • University of Minnesota Center for Aging • Leadership role in multi-state collaborations • Free to consumers, providers, and professionals • Professional data management • Full time, centralized, trained, committed resources • Defined business processes for data acquisition and maintenance; including regular updates • Social services orientation; database management technical skills

7. 9 Lessons Learned 1. Centralize Data Management 2. Have Sufficient Database Expertise And Use It 3. Have Broad Knowledge Of Social Service Agencies And Access To Relevant Sme’s (Subject Matter Experts) 4. Identify Types Of Customers And How They Use The Data 5. Manage A Data Maintenance Factory 6. Split The Work Into Projects And Data Maintenance 7. Document Data Maintenance Policy And Operating Procedures 8. Think Long Term And Work Towards A Strategic Data Management Plan 9. Be A Good Team Player In Your Virtual Enterprise

8. Lesson #1: Centralize Data Management Observed From Geographic Decentralized Data Management • Can’t afford sufficient expertise in each location (very mixed quality) • No uniform policies and procedures • Duplicate and fragmented agencies • No clear identification of responsibilities (finger pointing) • Difficult to present consistent interface to customers • Logistic nightmare to route data changes to responsible party

9. Lesson 2: Have sufficient Database expertise and use it • A database is a highly structured representation of the real word • The real world is a very loosely structured highly complicated mess • Understanding how to represent the real world in a database is a database skill; not an I&R skill • Users, including I&R users, need to understand how it was done, not how to do it • The Data Management Organization needs skills like data modeling and information architecture design to organize the data so it is understandable to the user • Requires a many faceted global view of current and future uses of the data and how those uses map to underlying database structures • Don’t rely on a software development shop for data management expertise – these are related but quite different skills • Expert understanding of the database structure can extend users horizons and suggest opportunities for valuable new functionality (multiple resource views and manipulations) • Ability to efficiently (programmatically) convert data from other sources • Ability to perform database analysis for gaps, inconsistencies, dirty data, and specialized reports • Operational efficiencies through batch data manipulations • Data cleansing • Data imports • Data exports • Ability to execute operation and technical partnerships with other entities and even to envision the possibilities of how to do this.

10. Lesson 3: Have broad knowledge of social service Agencies and access to relevant SME’s (Subject Matter Experts) • Content knowledge is required as well as container (database) knowledge • Don’t rely solely on database experts that claim they can manage any kind of database • The Data Management Organization must have broad knowledge of the agencies • Typical, large, small • Likely degree of technical expertise of people • Ability to communicate with service providers in their language • Explain limitations of electronic databases in a way that does not cause frustration • Recognize meaningful provider feedback that should alter operations or the database • Access to SME’s • Unrealistic to be expert in details of 100’s or 1000’s of different types of services • Others already have this expertise (a lot of others have small pieces of it) • SME’s often have relationships with their related provider communities • Effective reviews of data for agencies within their field • Recognize missing providers

11. “Connecting People of All Ages to Community Help” Lesson 4: Identify types of customers and how they use the data you might be surprised and it might matter in putting data in the proper fields for all customers not just the easiest place for viewing on the web or by specialists – make sure data stands up to advanced uses like PUI stuff • Who are the users? • What do we offer the users? (What do the users need?)

12. Lesson 4: Identify types of customers and how they use the data (continued)

13. Lesson 5: Manage a Data Maintenance Factory • Sources of data additions, deletions, and changes • Multiple external request methods • Email • Provider Portal • Special capabilities for Priority Data Reviewers (tool based, batch inputs) • Initiate cyclic updates (weekly) • Identify your target volume of activity • 2 X per year per agency  24,000/Yr.  2,000/Mn.  500/wk.  100/day • SLA’s (Service Level Agreements) • 3-5 business days for Provider Portal change requests • 4 weeks for general requests (longer for large batches) • Activity tracking • Every agency and every change request is important • Basis for statistical process control through measurements • Measurements • Manage the backlog • Aging reports • Productivity measures to determine capacity • Reporting • Regular “production control meetings” • Train your people – it takes time • Automation is a key to productivity – need tools for data maintenance

14. “Connecting People of All Ages to Community Help” Data Updates by Month

15. Lesson 6: Split the work into data maintenance and projects • Data maintenance is high volume repetitive work handled through the factory • Projects vary in frequency and size, some are repetitive and benefit from consistent processes • We use about 30-40% of our resources on projects • Types of projects • Database analyses and reports (gaps, service types, geographical) • Data cleansing • One time data conversion and import (fielded electronic input really helps) • Working with data source partners • Periodic conversion and import (licensed providers) • Document retrieval (licensing actions) • Web page partnering (Housing Registry) • Working with data publishing partners • Periodic export (GIS [Geographical Information System]) • Web page partnering (counties, Children and Families website access to licensed providers) • Database structure extensions for other partners’ special needs • County contractors (providers contract with multiple counties [87 in Minnesota]) • Quality Data Profile for Nursing Homes (annual updates with opportunity for provider review) • Data maintenance tool development and process improvements

16. Lesson 7: Document data maintenance policy and operating procedures • Policies establish mutual expectations between Data Management Organization and customers • Service Level Agreements • Change request formats and requirements • Requirements for new listings • Methods of communication • Who controls what elements of the data • Providers know their business best • Can’t rely on providers for all information • Priority Data Reviewers • Rules for changes to partner data (e.g. license data) • Processes establish methods of operation within the Data Management Organizatione.g. (not all steps are shown) • Enter request in Issue Tracking • Analyze request • Ask requestor for more information if needed (track status) • If new agency request check to see if it’s already in the database • Process request • Test change from customer viewpoint • Notify customer request was completed • Keep your customers informed

17. Lesson 8: Think long term and work towards a strategic data management plan Leverage your resources • Turn project work into regular operational work • Some  Factory • Some  Improved data access tools for customers • Enlist everyone’s help -- Push maintenance to the field • Provider Portal • I&R tool input • Maintain control – require review and acceptance by Data Management Organization • Practice continuous improvement for productivity and quality (measure  analyze  improve  ) • Utilize partners who maintain their own data when that makes sense • Invest in communications (mailings, literature, video conferences, …) • Invest in automation

18. Lesson 9: Be a good team player in your virtual enterprise • Support I&R users with training about the data and data structure • Support all customers with communications about data maintenance • Status of their changes • Information about how to provide changes to you • Use knowledge to help identify tool improvements • Provide options to enterprise leaders on what the data can be used for • Provide options to enterprise leaders on how the data can be used to solve business problems • Support enterprise leaders in meeting management requests (requests?) • Be a voice that helps build the presence and credibility of MinnesotaHelp.info™ ! I hope I have accomplished that today Thank you for listening