Request for Information Page 1 of 9 Data Management Applications & Services

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1 Request for Information Page 1 of 9 Data Management Implementation Analysis and Recommendations About MD Anderson M. D. Anderson is a component of the University of Texas System and was created by the Texas Legislature in M. D. Anderson has established an international reputation as one of the world s preeminent centers for cancer patient care, research, education and prevention. The multiple missions of M. D. Anderson drive a complex academic, research, and patient care focused IT environment. Any solution must address the needs of a University of Texas (a state agency) and be consistent with our reputation as a premier comprehensive cancer center. Project Overview The Division of Information Services reflects the complexity and diversity of key clients within the University of Texas M. D. Anderson Cancer Center. VP & CIO Data Center Operations and Technical Services Network Services EMR Development & Support Internet Services IS Operations & Integrated Desktop Services Clinical Applications Administrative & Financial Systems Clinical Research Information Systems Research Information Systems & Technical Services Project Coordination & Support Clinical Care & Ops Telecommunications Services & 4-INFO Information Security Data Management Applications & Services

2 Request for Information Page 2 of 9 The (DMAS) department was formed in June 2006 with the purpose of providing institutional leadership through consistently developed and applied data management solutions throughout the organization. The current organizational structure is indicated below. Data Tools & Infrastructure Team(s) Data Integration Team(s) Data Delivery Team(s) BI Software Developers BI Tools & Training BPM Software Management Data Modeling & DBA ETL Development MDM / Metadata Ontology Terminology Data Architects Data Analysts Solution Architects Application Systems Analysts Project Managers In late October 2006, the team held its first informational session with key institutional thought leaders who represented the pillars of M. D. Anderson s mission: academic, research and patient care. Throughout the session, the team educated and led the discussion by informing these thought (and data) leaders on data management concepts and terminology and why enterprisewide data management is critical to the long term success of M. D. Anderson. Using the visual wheel shown here, we have componentized data management into the following areas (with our working definitions): Data Governance: policies, procedures, and standards used to govern over all other components of data management. Stewardship: the person that defines data and requirements, produces data, consumes and/or uses data, provides data quality standards, and defines appropriate access guidelines. Integration: a way to de-couple / re-use data and identify areas of synergy in data sharing. Quality Ontology Data Governance Stewardship Meta Data MDACC Data Security 25% Integration Repository

3 Request for Information Page 3 of 9 Repository: developing retention strategies, identifying system of record, and developing standards and guidelines. Security: information is an asset that should be controlled and protected and have processes around it. Ontology: maturing the definitions of our business practices (clinical, research, and administrative) into a synchronized nomenclature of comparable and known terms and their relationships. Quality: building processes to assist in the defining, detecting, reporting, and improvement of data quality. Metadata: business, process, technical and application data about data. Project Scope Analysis and Recommendation Request: This RFI is intended to solicit information from firms who can provide an understanding of the breadth and scope of information available related to data management, along with the varying strategies, tools, etc. used to implement a successful data management program. In addition, this RFI is intended to assess each firm s level of knowledge and expertise in some or all of the components of data management, as well as their ability to provide appropriate services in these areas. Please note that throughout the RFI cycle, we have no expectation of a black box solution; but, instead are seeking only clear, consistent, transparent, and repeatable processes that can be maintained and matured by our existing staff. Data Management Program 1 Define data management. 2 What areas should be considered in the broad scope of a data management program? 3 Describe the program implementation: Which components? Which skill (employee) mix? What type of modification to the components (wheel)? What is the time period to accomplish (months/years)? What are activities that need to be completed before initiating? How procedurally (process) to implement components (to grow on the prior activity)? Are there hardware or software considerations (needed before implementation)? Other? 4 Should the creation of one or more Center(s) of Excellence (CoE) or a Competency Center(s) (CC) or other be established? If so, which specialty? What are the goals and responsibilities of the CoE or CC? What are the organizational behavior changes/growth strains to be expected? Suggested behavior change process for the organizational development? What are the measurable metrics?

4 Request for Information Page 4 of 9 What is a realistic interval for measurement? 5 Please address all the following for each of these components where you have experience (data governance, stewardship, integration, repository, security, ontology, quality, metadata, and other). # of engagements (statistic). Facility size ($ revenue) (statistic). Facility size (staff) (statistic). Industry / Healthcare specific (statistic). Education / Research (statistic). Private/ Public/Government (statistic). Engagement Size (duration) (statistic). Engagement Size (staff assigned) (statistic). Relevant case studies on data management (e.g. white papers). 6 Provide a recommendation for the vehicle to communicate progress to the institution (website, dashboard, etc.?) what metrics or types of information would be measured and/or released? 7 Describe the approach to the roll out of data management components. 8 Describe typical financial / engagements fees per component. 9 Provide an overview of partnering with MDACC staff for knowledge transfer. 10 Describe ability (or practice) to be on-site for the partnership. 11 Describe the recommended life cycle for a data management program. 12 In working with other organizations, what were their 1 yr, 3 yr, and 5 yr designs for their data management program? What stage are they in their roadmap? 13 Document best practices and standards related to a data management program. 14 Describe the characteristics of a data centric organization. 15 Describe the steps needed to become a data centric organization. 16 Are there other topics that we should consider? Data Governance 1 Define data governance. 2 What areas should be considered in the broad scope of a data governance initiative? 3 Describe the implementation of a data governance structure? Is the staff that would implement the data governance consolidated in one department or federated within IT or federated throughout the organization? o What would the structure look like (organizational design)? o Please identify examples. What type of staff would provide the implementation? o Skill mix. o Department areas. o Organization chart. Are SLAs with other IT areas suggested or with institutional end users? 4 Describe the on-going data governance component. How is compliance monitored? o Would we let areas (or everyone) self govern?

5 Request for Information Page 5 of 9 o Or would be audit? o At what interval or which components? Is the staff that would support the data governance consolidated in one department or federated within IT or federated throughout the organization? o What would the structure look like (organizational design)? o Please identify examples. What type of staff would provide the ongoing operations? o Skill mix. o Department areas. o Organization chart. 4 Describe the relationship between IT governance, SOA governance, and data governance. 5 Describe the recommended life cycle for a data governance program. 6 In working with other organizations, what were their 1 yr, 3 yr, and 5 yr designs for their data governance program? What stage are they in their roadmap? 7 Document best practices and standards related to data governance. 8 Are there other topics that we should consider? Data Stewardship 1 Define the structures, roles, and responsibilities of a data steward. 2 Provide suggestions for developing and implementing a formal data stewardship program. 3 Describe the approach of source systems vs. subject areas for identifying the appropriate data steward? 4 Differentiate the following - data stewardship, data custodian, data owner, and process owner? 5 Describe the recommended life cycle for a data stewardship program. 6 In working with other organizations, what were their 1 yr, 3 yr, and 5 yr designs for their data stewardship program? What stage are they in their roadmap? 7 Document best practices and standards related to a data stewardship program. 8 Are there other topics that we should consider? Data Integration 1 Define data integration. 2 Provide a recommended approach for defining data integration maps within the Institution. 3 Discuss strategies for approaching enterprise data modeling (top down, bottom up, subject model, etc.) and the skill mix relevant for each approach (including, but not limited to the following): 4 Recommendations on how and where to implement a refresh (replace all data) rather than a chance data capture approach. 5 Recommendations on when to use staging tables. 6 Recommendations on how to process dimensional data. 7 Recommendations on when to use summary or aggregation tables. 8 Recommendations on when to use ETL tool vs. custom code. 9 Differentiate uses of EII, EAI, HL7, and ETL. 10 Provide insights into integrating and mining business content which includes both structured and

6 Request for Information Page 6 of 9 unstructured data sources. Cite implementation experiences / examples. 11 Describe the recommended life cycle for a data integration program. 12 In working with other organizations, what were their 1 yr, 3 yr, and 5 yr designs for their data integration program? What stage are they in their roadmap? 13 Describe best practices and standards for building decoupled / reusable data integrations. 14 Describe best practices and standards and scenario using HL7 beyond traditional clinical applications. 15 Are there other topics that we should consider? Repository Management 1 Define repository management. Describe best practices and standards for repository design When to use a data store? What type of staging area? Recommendation on when to move data to a warehouse environment). 2 Provide a recommended approach for developing an applications and / or data registry (data repository) inventory? For example: By subject? By data element? By system? Other? 3 Describe a visual/pictorial view of the application / repository inventory. 4 Describe best practices and standards for data retention policy design for: Structured data Unstructured data 5 Describe best practices and standards for providing seamless access to multiple repositories from BI tools. 6 Describe suggested recommendations for developing retention strategies. 7 Describe suggested recommendations for system of record decisions. 8 List experience implementing an enterprise search engine. 9 Document best practices and standards related to enterprise search engines. 10 How does the enterprise search engine tool integrate with document management systems? 11 What tools are available regarding selection, procurement and implementation of an enterprise search engine to find relevant information across the wide range of data sources (relational databases, word-processing documents, s, powerpoint presentations, multi-media files, and PDFs) that M. D. Anderson manages? Does the tool include the following: A crawler or spider for creating copies and indexes of information accessed by staff members to reduce future search times? Security enforcement for user authentication, access control, and security policy enforcement? On-the-fly filtering that reconfirms authorizations before displaying each document? Metadata extraction for creating categories and content tags that improve the accuracy of search results?

7 Request for Information Page 7 of 9 12 Describe best practices for use of specialist technologies for business event monitoring (BEM)? 13 Cite implementation experiences and suggestions for building a proof of concept for BEM, enterprise search engine, and repository management. 14 Describe the recommended life cycle for a repository management program. 15 In working with other organizations, what were their 1 yr, 3 yr, and 5 yr designs for their repository management program? What stage are they in their roadmap? 16 Are there other topics that we should consider? Ontologies/Terminology/Taxonomy 1 Define ontologies. 2 Define terminology. 3 Define taxonomy. 4 What tools are available regarding selection, procurement and implementation of terminology? 5 How often and for what type of data have terminology tools been managed? Financial, non financial, or healthcare (Physicians, Nursing, by Specialty)? 6 Describe the recommended life cycle for a terminology tool (with emphasis on a cancer / research hospital). 7 Describe best practices and standards for building terminology tool. 8 In working with other organizations, what were their 1 yr, 3 yr, and 5 yr designs for their terminology roadmap? What stage are they in their roadmap? 9 Are there other topics that we should consider? Master Data Management 1 Define master data management. 2 What tools are available regarding selection, procurement and implementation of master data management (MDM)? 3 Describe a recommendation for metadata / MDM program implementation? 4 Define and differentiate analytical MDM, operational MDM, and collaborative MDM. 5 Define and describe the relationship between MDM and SOA. 6 Define the business environment where more than one (1) MDM solution would be implemented in the year 2007 and the year 2010 (citing the maturity of MDM as a practice). 7 How often and for what type of data have MDM hierarchies been managed? Financial, non financial, or healthcare? 8 Describe the recommended life cycle for master data management. 9 Describe best practices and standards for developing and implementing master data management. 10 In working with other organizations, what were their 1 yr, 3 yr, and 5 yr designs for their master data roadmap? What stage are they in their roadmap? 11 Are there other topics that we should consider?

8 Request for Information Page 8 of 9 Data Quality 1 Define data quality. 2 What tools are available to facilitate implementing a data quality program? 3 Describe the recommended life cycle for a data quality program implementation? 4 Address data quality from proprietary systems vs. open systems? 5 Describe the approach for integrating formal data stewardship and data quality programs? 6 Define, differentiate, and identify data quality, completion rates, missing data, and improper data. 7 Document best practices and standards related to a data quality program. 8 In working with other organizations, what were their 1 yr, 3 yr, and 5 yr designs for their data quality roadmap? What stage are they in their roadmap? 9 Are there other topics that we should consider? MetaData 1 Define metadata. 2 Differentiate the approach to business metadata vs. technical metadata? 3 Describe the recommendation for obtaining and maintaining business metadata? 4 Describe an approach to facilitate the development and maintenance of a symantec meta layer for reporting? 5 Citing prior experience, provide a recommendation on a build vs. buy approach in implementing an enterprise metadata management application. 6 What enterprise metadata management tools are available? 7 Describe recommendations for capturing intellectual capital in metadata. 8 Describe recommendations for launching a pilot project. 9 Provide examples of how an organization (hospital setting, if possible) effectively uses metadata management to define common processes within a services oriented architecture environment. 10 Describe approaches to ensure usability of metadata content and ways of providing value of metadata beyond technical uses. 11 Describe the recommended life cycle for a metadata implementation? 12 Document best practices and standards related to metadata program. 13 In working with other organizations, what were their 1 yr, 3 yr, and 5 yr designs for their metadata roadmap? What stage are they in their roadmap? 14 Are there other topics that we should consider?

9 Request for Information Page 9 of 9 Project Background The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research and prevention, and through education for undergraduate and graduate students, trainees, professionals, employees and the public. It is this three-pronged mission in service of patient care, research and education that has created a complex and non-governed data landscape. People M. D. Anderson employs over 17,000 people in clinical, research, education or the administration of services with approximately 1,200 physicians, 2000 nurses, and 500 mid level providers. The IT areas at M. D. Anderson are a mix of centralized and federated systems. Current Environment In 2004, the IT Division began a practice of IT Governance with an overall governance group tasked with project approval (funding and execution). Sub committees were established for Clinical, Research, Finance, and Infrastructure. The approval process is cyclical and begins with the new fiscal year. Approvals, work plans, technical walk-throughs are critical to the continued financial support. It is the intent that all projects be vetted through this governance cycle. Projects that do not receive funding are not necessarily cancelled, but may be funded by the hosting department (outside governance). This population of requests and presumed system manifestation is a critical part of understanding the complexity of application inventory to be undertaken with this project. Response Requirements: Please respond to chosen components and questions as outlined above, plus any additional areas you feel this project should explore, along with a listing of services offered, resource plans, background(s) of consultants to be offered, and expected timelines no later than April 30, Questions in advance of the formal due date can be addressed to cwrange@mdanderson.org.