5 Crucial Steps Toward Data-Driven Decision Making

A recent survey of over 300 benefits leaders from a variety of industry sectors performed by Artemis Health showed only 53% of respondents feel they are very successful in using non-traditional data sources, non-medical and Rx claim data, in making benefit decisions. Working for an organization committed to providing our members objective, unbiased productivity research and unparalleled industry benchmarking, I am both encouraged 53% of the survey respondents are very successful, and concerned 47% of the respondents are not. While Artemis attributes this difference primarily to lack of tools, which could be a contributing factor, there are other considerations potentially affecting respondents lack of success.

Data-driven decision making (D³ M) best practice models generally focus on a holistic evaluation of the business goals, informational needs, and data availability. For instance, research performed by Marymount University reiterates the conclusions of other similar research papers of the major considerations for implementing a successful D³ M program. This can apply to traditional or non-traditional data sources.

1. Determine your business objectives – Talk to your stakeholders! Understand your informational needs before jumping right to evaluating tools, designing reports, or hiring consultants. This step is critical to evaluate whether your implementation is successful. Additionally, do not try to “boil the ocean.” If you have had difficulty in the past, or are relatively new to employing a D³ M program, start small. Focus on a particular organizational function, a specific product, or a specific wellness program.
   
   
2. Evaluate data sources – Explore internal data sources as well as externally available sources. For instance, IBI benchmarking may be an excellent external source of data to identify opportunities for intervention and mitigation of leave and disability claims based on variances to the benchmark. Suppliers of leave and management benefits may also be an excellent source of external data for employer evaluation. Internal sources may include employee surveys, claims data, internal focus groups, and direct employee feedback.
   
   
3. Visualize and analyze the data – This is the time to evaluate organizational tools and data analysis resources. Tableau is one example of a data visualization application. There are certainly a variety of others which can be evaluated based on the size of the database, the reporting needs of the organization, and price. Many organizations may be suffering from data overload or “analysis paralysis.” They are creating so many reports, leaders are overwhelmed and not able to appropriately synthesize what they have received and make actionable recommendations. Visualizing the data may help to uncover opportunities and lead to better decision making.
   
   
4. Develop an implementation strategy – Determine the users of the data, the users of the reporting, the SME’s for any necessary customization or programming, the cadence of the output, and the timeline for operationalizing the recommended solution. Again, focusing on a piece of the business will make the process less daunting and disruptive to the organization.
   
   
5. Continuous feedback and process improvement – The implementation may be completed, but the work is not done. As users become more comfortable with the new or enhanced data analytics, they will certainly ask for more. A successful program is not static, but responds to informational needs and includes built in flexibility to grow, change, and respond to evolving informational needs.

Whether your organization is large, and you are suffering from data validation and data overload issues, or smaller, suffering from resource constraints and adoption issues, enhancing your effective use of your data assets are a primary concern (International Journal of Advanced Science and Technology, 2020). If you cannot evaluate your data usage as very successful, you have opportunities to improve. Effective use of data can facilitate informed decision making, employee wellness, and ultimately employee productivity.