Published on September 27, 2007
Data Management and the CMM/CMMI:Translating Capability Maturity Models to Organizational Functions: Data Management and the CMM/CMMI: Translating Capability Maturity Models to Organizational Functions Cynthia C. Hauer Millennium Data Management, Incorporated Huntsville, Alabama CDM Industry Data Management Chair NDIA TID Technical Information Division Symposium Royal Sonesta Hotel, New Orleans, LA August, 2003 … Because Data Transcends Time Agenda: Agenda Address the role of Data Management in the CMM/CMMI Assess CMMI guidance for DM Identify Missing Links Share the DM Maturity Model DM in the CMMI: DM in the CMMI Under project Management, Project Planning Description and definition of DM Data Requirements Data Content Data Collection Data Cost Typical work products Sub-practices Plan for Data Management: Plan for Data Management Various forms of documentation Administrative, engineering, CM, financial, logistics, quality, safety, manufacturing, and procurement Various formats Deliverables or non-deliverable Distribution forms (physical or electronic) SP 2.3-1 Plan for Data Management Plan for the management of project data. Description/Definition of DM: Description/Definition of DM CMMI does not really define DM Functionally described, rather than defined Data is described in terms of “documentation” Data Content: Data Content Forms Media “Deliverability” Distribution Data Requirements: Data Requirements Established for the project For data items, content, and form Based on a common or standard set of “data requirements” “Uniform content and format requirements for data items facilitate understanding of data content and help with consistent management of the data resources”. Data Collection & Costs: Data Collection & Costs Reason should be clear Task includes analysis and verification applies to project deliverables and non-deliverables Contract deliverables and non-contract data requirements customer-supplied data Stipulates understanding of how data will be used, prior to collection Data is costly, and should be collected only when needed Typical Work Products: Typical Work Products DM Plan Master List of managed data Data content and formal description Data requirements lists Privacy requirements Security requirements Mechanism for data retrieval, repro, and distribution Schedule for collection of project data Listing of project data to be collected Sub-Practices: Sub-Practices Establish requirements and procedures to ensure privacy and security of the data Procedures must be established to identify who has access to what data as well as when they have access to the data Establish a mechanism to archive data and to access archived data Understandable form or represented as originally generated Determine the project data to be identified, collected, and distributed. Assessment : Assessment Rudimentary, but complete Functionally-oriented Evolved thinking DM is basically interwoven all over the CMMI A clear, concise definition of DM would be of great value to all CMMI users Slide12: Transferring CMMI Guidance to the Implementation Level What can Maturity Models Measure?: What can Maturity Models Measure? Both the quantitative and qualitative aspects of success Quantitative Factors Planning Tracking Measurement Quality Goals Documented Processes Peer Reviews Allocation of Dedicated Resources Qualitative Factors Leadership Vision Communication Decision making Collaboration Integration of Processes & Disciplines Quantitative is Measured, Qualitative is Acknowledged Slide14: Establishing Value Step One: Measurement Criteria Cost - acquisition and life cycle (investment potential) Price - against risk and investment (return) Re-use - with metadata and characterization (leverage factor) Measurable consistency - from project to project (data integrity) Evolving - quality decision data (KM or collaborative quality, use, and outcome) Key: Establishing & calculating visible, measurable worth for effort and assets expended, saved, re-used Establishing Value Step Two: Maturity Model: Establishing Value Step Two: Maturity Model The three essential macro states of DM maturity Initial Transitional Excellence Manual, inconsistent methods that are not repeatable (Asset Ignorance) Course corrections that are applied in certain cases, over time Methods improve and gain consistency with understanding & use (Asset Recognition) Improvements are predictable, proven, and intentionally created Repeatable methods create opportunities for efficiencies & economies of scale (Asset Use) TIME, TECHNOLOGY, UNDERSTANDING & QUALITY Slide16: The Data Management Maturity Model Almost complete certainly of results is achieved Reliability and predictability of results is significantly improved; e.g. six sigma vs three sigma Good quality results within expected tolerances most of the time; poorest individual performers improve towards best performers; more leverage achieved for best performers Variable quality with some predictability; best individual performers assigned to business critical projects to reduce risk and improve results Organization depends entirely upon individuals; little or no corporate visibility into DM cost or performance; variable quality, low results predictability, little to no repeatability. TIME, TECHNOLOGY, UNDERSTANDING & QUALITY Quality, Predictability of Results Slide17: Value Determination Factors 5 Fully Optimized 4 Predictable Risk 3 Corporate Competency 2 Managed 1 Baseline Model Level Value Determination Characteristics Obvious value for services received; risk reduced, unnecessary costs avoided, clear best practices & sector leadership Lower ROI in investments in DM are accepted in exchange for reduced risks Measurable, able to recognize costs and benefits, perform cost/benefits analyses, maximize ROI, good results faster, better trained workforce Anecdotal, based on individual performers’ capabilities and specific memorable events Subjective, gut feel for performance, benefit, costs, and value received Gains: Consistency, Repeatability, Cost & Business Model Awareness Summary: Summary Maturity Models have potential for success To be successful, they must be understood at - and mapped to - the application level of the enterprise processes DM and CM have the capability to integrate their areas of expertise to address most organizational challenges, as they touch the enterprise everywhere DM is making a contribution to the CMM/CMMI Discussion: How can we improve and leverage the CMMI opportunity to benefit DM, CM, and our organizations?