A couple of questions pop up before we understand why we need data analytics in the first place. I think we need to answer the central question- is it going to increase the efficiency of the system to make employees more productive and hence improve the returns on investment?
The public health authorities, for example, embrace “digitisation” by churning out mobile applications- which perhaps doesn’t see traction. Those are usually written off as “business expenses” and amounts to a colossal wastage of public resources.
The other approaches to “data-led organisation” are-
- Business Case for incorporating data analytics. Frankly, leaders are often clueless, depending on minions to speak their minds.
- Allowing multiple departments to create their own data pools.
- To generate a lot of data within to organisation and see what really fits.
Data science can’t happen in a silo. It must be tightly integrated into the business organization, operations and processes:
- There needs to be joint prioritization. Business leaders and data scientists should jointly decide which business problems to focus on. If there is any question about priority, the final call should go the business heads.
- Leaders need to be conversant in data science. Business leaders don’t need in-depth expertise in data science, but they require a basic, working understanding. Being conversant enables business leaders to work effectively with their data science teams.
- You may need to accept “inconvenient outcomes.” Data inevitably creates transparency and reveals business insights that can be unexpected, uncomfortable, and unwelcome.
Going forward, I think, it is crucial to first identify the problem, generate a use case scenario for ROI and then proceed forward for getting the data inputs in place.
Via What’s the Best Approach to Data Analytics?