Data Driven Decision Making Model

Keys to an Effective Data-Driven Decision Making Model

Stats Assistance understands the importance of gathering data to help in making informed decisions on the way we do business, improve schools and programs, and impact the community. We have worked with a number of businesses and organizations in developing sound data-driven systems that help them understand themselves better, understand the populations they are engaged with, the outcomes they receive, and their strengths and areas of improvements. Below is an outline of key strategies needed for developing a sound Data-Driven Decision Making System.

Stage 1. Develop a Culture of Data Collection

Often businesses and organizations are confused about how to collect data, what data to collect, and when to collect data. It is important to first develop a plan for what data you need and why you need these data. Second, it is important to develop a data collection system (DCS). This can be a system created in Excel, Access, FileMaker, etc. But the important thing here is to have a place where you house data. We have found Google Docs ((Forms) to be one of the low cost effective ways to collect data in a centralized location. It is also important to develop rubrics and/or surveys that will produce data you can use. Relicheck is a good tool for finding out of the data you collect is reliable.

Stage 2. What Does the Data Gathered Mean

Develop a continuous process for reviewing and interpreting data. Here, it is important that you do not view data once a year. Assessing data should happen continuously, at least three times a year. Also, when interpreting data, it is important that the data being interpreted relate to desired outcomes. So, in your planning stage, thing about the outcomes or standards you need to assessment. In the interpretation stage, talk about what the data tells you about your business or organizations in relationship to your standards or outcomes.

Stage 3. Developing a Plan of Action

Once you have examined your data and get a better understanding of your strengths and areas that need improvements, it is now time to develop an action plan. The plan should include key actions and key questions. Key actions include: identify short-term & annual goals & objectives, identify what data to collect, identify strategies that will be implemented, identify activities that will support the strategy, and identify people responsible for implementing strategies, collecting the assessment data, and monitoring overall progress. Key questions should center on: what should the objectives be for this year's plan, what data of attainment will we accept, what data will be collected, what capacity do we need to build to implement the plan, and what strategies and activities will help us attain our objective? For short-range goals, it is important that you do not try to tackle everything at once. The short-range goals should consist of one to two areas you want to improve on. Additionally, long-range goals can vary for one to 5 goals that will be accomplished over a year, three years, to five years. Also, this is a great place to think about developing or improving on your Strategic Plan. Additionally, in the planning process, think about what are the outcomes you are looking for, what data will be gathered to ensure that the goals are success, when will you gather data for the goal, when will you assess data on the goal.

Stage 4. Enacting Change

A critical mistake we see is that people do not scale their action plan. Meaning, when people see alarming data, they want to make dramatic changes right away. However, it is important that, if not changes are not life threatening, the changes are made small, assessed, bugs worked out, then rolled out to a larger audience. Business and organizations need to consider what training or professional development is needed to carry out the action plan. Finally, the business or organization must think about the holistic impact change might have on the organization's function, structure, mission, and the clients. Therefore start small, as most innovation start small.


Data Driven Model


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