The goal of data science is to translate ALL business problems into scientific problems which can be managed and/or improved in a systematic data-driven way. It is the job of a data scientist to do exactly this. All business problems will benefit from the application of a more scientific and data-driven approach.
Translating a business problem into a scientific one inadvertently clarifies/reveals the true problem and can provide immediate insight in terms of revising how you think about solving the problem.
- There is a strategic component in framing the problem and separating out what is irrelevant from what is truly important.
- There is a tactical component of actually selecting the methodology and tools to tackle the solution.
- There are operational questions, such as:
- What are the right data?
- How to access that data?
- What kind of infrastructure are required to support the analyses?
- How will that data be translated to information?
- How will the consumers of that information translate it to action?
- How will those actions feedback into the data and the model?
These questions must be looked at within the context of understanding how the analytic pipeline that you develop will eventually mesh with the real operations of the business.
It is insufficient to merely report a problem, someone has to take action on it in a measured way, and gather feedback to understand how effective the intervention was.
So, there is a question of numeracy or fluency that involves helping operational teams to understand/learn how to use the analytics effectively to drive positive change, and how to use the same analytics to diagnose process issues.
Culturally, numeracy is perhaps the most underrated trend developing today — more people are learning to develop the skill of translating the numbers and graphs they see on a screen to physical reality, and vice-versa. To a certain extent, people have always done this, but today they are learning to read new kinds of charts, graphs, KPIs, etc.
While many businesses are ‘comfortable’ using the same KPIs that everyone else is using — the real trick to getting ahead is to develop/use KPIs that your competition are not using, to gain an edge. The name of the game is to customize your business edge. If you’re doing what everyone else is doing, you are merely treading water at best. This gets to the heart of why you want a good data science team playing offense and defense on your behalf, in a proactive manner.
As an example, at Mastech InfoTrellis, we are not just pitching fancy data science to our clients, we are pro-actively using operational analytics to help us navigate the turbulent waters ahead during this global pandemic crisis that is impacting the balance sheets and futures of businesses across the globe. We took rapid early action and determined the critical KPIs that would be helpful for us to understand the health of our business and incorporated these into our weekly reporting dashboards that enable us to view at-a-glance how we’re doing relative to the changes in the market place, and allows us to provide our clients with a fair and accurate assessment of the risks to their mission-critical applications as our workforce switches to a work-from-home operating model. In every case, fortunately, we’ve determined that our robust operating model is well positioned for these changes, with little impact on client operations. This proactive approach to knowing our business, from top to bottom, enables us to get ahead of the dangers/risks, and provide safety and security for our employees, and greater visibility/continuity to our clients, in this time of uncertainty.
We would love to hear from you: How is your business leveraging data science and/or analytics to get ahead of the evolving situation?