by Marc Edwards B.Sc., PMP, Content Analytics, MBA
So, if you read the last post, you now know that proper executive buy-in is essential to creating a Health Analytics program within any organization. And notice the use of the word, “proper”. Here, I’m talking about executive support backed by a well-defined business problem and business case. I can’t tell you how many times I’ve come across instances where the analytics function is viewed as a “nice-to-have” by executives, resulting in a listless, marooned analytics team as a result. That being said, if you DO get proper executive buy-in, it is not enough. In fact, you’re only just scratching the surface.
So…who’s going to manage this program? If your organization is large enough, the role of CIO (chief information officer), CAO (chief analytics officer), or CDO (chief data officer) may be developed to maintain that executive level of support and to help develop the new program’s mandate and strategy. Doing this will help establish the narrative for understanding and validating the required investments and process changes to realize the business strategy.
If your organization is not large enough or if the infrastructure of the business is not ready to accept another c-level executive, then having a director or manager of Health Analytics should do. It will be important to ensure that “analytics” is present in the team’s name as you want it to be clear to everyone on what the function of the new team will be.
When picking a Health Analytics leader, be aware that this person should have a strong inkling of a vision for the program, should be able to create an environment of exploration and learning, and should have a strong nose for detecting and nurturing talent.
Once the leadership situation has been finalized, you will now need a strong multi-discipline group:
This is the person who can take raw data and convert it into plain English and help companies make better decisions. The data analysts’ job should include the following:
- Determine the data requirements
- Identify the sources of data throughout the enterprise and develop methods of collecting that data
- Data processing to provide greater structure
- Data cleansing
- Some data modelling
- Data visualization
Depending upon whom you ask or what blog you read, the data scientist role is the sexist position in the workforce today. The job requirements can often be so specific and uniqe that a good data scientist can be hard to find and very expensive to hire. If the costs end up being too much, consider developing the appropriate skills sets in-house to compensate, or hire co-op students from your local high school, college, or university, who are specializing in fields, such as, computer science and statistics. Either way, the data scientist’s job should include the following:
- Analysis of large data sets
- Statistical analysis
- Use of analytic tools, such as, R, Python, SAS, SPPS, SQL, and Hadoop
- Natural language processing to structure unstructured data
- Data modelling
- Database creation
- Data architecture creation
- Data visualization
Project Management & Business Analysis
When implementing a Health Analytics team, the analytics projects that will come your way will ultimately require project managers to execute them. It is especially important, during the early stages of the creation of the team, to be able to demonstrate value, ROI, and align projects to the overall corporate strategy. Rigorous project management can do this. Given the scarcity of qualified individuals with the appropriate data scientist skills set, it would be even more unlikely to find someone with those skills as well as project management ones. Therefore, the more likely rout would be to hire someone specifically for the project manager role.
A skill that is often underrated but would arguably be the most important for your Health Analytics team would be business analysis. The business analyst will be the person who identifies and shapes the major business problem at hand. Think about it, how can you use analytics to solve your client’s problems when you don’t fully understand the problem yourself?
Once you’ve develop a fully function and capable Health Analytics team, you now need to take stock of the data currently being collected within your organization and who are the users of this data, something we’ll dig deep into in my next post.