Improvement Methodologies

© 2001 millennium strategies

Background

What we have learned in concert with our clients is that business is much larger than just building a data warehouse. To address these issues we break the issue into two pieces: the decision support environment and improvement methodologies. In this paper we talk about improvement methodologies. You can learn more about our approach to decision support environments at our NCLB and Decision Support page which more directly addresses decision support in schools.

Decision support begins with the data warehouse as a repository for the data that is gathered and retained by the organization during the process of doing business. While this has been happening for some time, it is only within the last ten years that there has been a concerted attempt to actually use that data for some good beyond audit trails. The implementation of processes to use the data encompasses another critical phase to plan and enact changes suggested by the data. We have developed these elements into a four-phase strategy for successful change management.

Business improvement

The motivations for business improvement have been well discussed in the trade press. The business improvement strategies presented here apply equally well to K-12 education where No Child Left Behind and the requirement for adequate yearly progress have radically changed the landscape for decision support and change management in our nation’s schools. Because data warehousing, decision support and change management are new concepts in education, the methodology presented here provides a way to make both the process and the benefit realistic and understandable.

The primary objective is to show how data and an active decision support environment can be used to improve an organizations' performance in a cost effective manner. All too often organizations jump into the implementation stage without due diligence where aggressive sales people  over-promise results of their tools and services. Only through an understanding of all aspects of the process can the potential benefits be accurately balanced against tangible and intangible costs. Also critically important are identification of appropriate data sources and development of a realistic project plan.

Secondarily, we believe that if organizations apply a formal implementation methodology the process is streamlined and positive results more assured. Our approach to implementation leads clients through processes with tools and templates that enable executives to identify and implement the necessary data systems, analytic tools and decision infrastructure to support positive change.

Lastly we have found that organizations need help clarifying and interpreting the information condensed out of the data. With a robust methodology for change, it is much easier to develop options for change based on the data as well as to incorporate innovative methods. One can be more assured that conclusions are correct and that they are developing remediation and proactive plans that address the root problems and not just symptoms.

In order to be successful implementing an Improvement Methodology, organizations must have access to the following skills:

• • Project planning, administration and leadership
• • Technical design and engineering
• • Statistical analysis and information extraction
• • Options generation and assessment
• • Improvement champions and change agents.

The primary elements of an effective improvement environment include:

•    Data Warehouse includes the systems, software and processes for data acquisition, data staging, data storage and data query; it also includes processes for backup and archiving the data and performing systems maintenance.
•    Decision Support Methodology includes processes for analysis of data, investigation and generation of options for change, decision strategies and data capture strategies.
•    Improvement Methodology puts a comprehensive process around all of the components of the improvement environment from systems implementation to change initiatives.

Structure of Improvement Methodology

Our Improvement Methodology provides a comprehensive and coherent approach to decision support and change management within the organization. Consisting of four phases it breaks the entire process into manageable steps with decision points for evaluation of success and accommodation of changing circumstances.

•    Awareness  builds an understanding of decision support with an outline of the project and requirements with a return-on-investment analysis
•    • Background knowledge
•    • Project outline and requirements
•    • Return on investment analysis
•    Readiness  sets expectations and assess the potential for implementation of systems and processes for decision support and change management
•    • Organizational objectives and value proposition
•    • Readiness assessment report
•    • Recommendations for perparations
•    Implementation  includes integration of the processes and systems required for development of the data warehouse and decision support systems
•    • Systems and processes in place
•    • Warehouse loaded
•    • Processes initiated
•    Change  is a process to analyze the data, develop options for change from the data and other sources, and to act to incorporate changes into organizational processes.
•    • Change options
•    • enactment plan
•    • change initiatives

Phase Framework

The Improvement Methodology can be used to implement a diverse set of decision support and organizational improvement environments.

The data warehouse and data management systems may be located on- or off-site or with consolidated structures aggregating data from multiple organizations into a single warehouse. Decision structures include reporting (summary statistics), query-answer (statistical analysis) and hypothesize-test (statistical learning). Change structure can respond to events and specific statistical information (event-driven), or to options based on data analysis (data-driven), or as part of the hypothesis-test architecture (model-driven).

The Millennium Strategies a³ decision support environment is developed to implement an on-site data warehouse with statistical learning decision structure and model-driven change structure. This is one of many decision support environments that can be implemented under this methodology. Organizations are able to mix different types of data management with decision and change structures and can migrate from an initial state to other states in the future.

The important building blocks of the complete methodology are outlined in the table below.