It creates a very ambiguous situation for the so-called data scientist. The business departments are eagerly awaiting exciting new insights, but meanwhile the biggest challenge is to keep atop of the data mountain (or the data lake, if you prefer) and to store and analyze them as cost-effectively as possible.
Before entering the analysis path, however, a lot of preparation needs to be done. This includes what used to be called ‘data crunching’:
- taking the available data
- cleansing it from double entries and other noise
- transforming it into a format that can be searched and analyzed
The name for this activity may have changed, and the available tools to perform the job may have improved, but the work still needs to be done. The quality of your analysis is only as good as the quality of your data. Or, to put it in more colloquial words: garbage in, garbage out.
And there are other more tedious tasks that should not be forgotten in the entire analytics process. Tasks such as securing the data, or data governance (who can see what? how long must data be stored? should data be anonymized?...) will never be remembered as the most heroic part of the job. But when omitted, they will surely be pointed out as the cause of failure.
On the other end of the analytics process, when the data are true and well analyzed, the data analyst or data scientist may well have discovered some interesting insights. But they will still face the challenge of convincing the management of the value of these insights. Business managers need to see the story behind the figures, the business impact of the analytics results. It may seem obvious, but nevertheless some valuable insights have been neglected because of the analytics department’s failure to tell the story in a way that made sense to the business. It has happened before, and it will happen again.
Unfortunately for analysts, analytics does not begin nor end with ‘just analytics’. If you fail to see that, it is high time that you leave your job to someone who does see the full picture. Fortunately there are many tools to help you with the crunching part. And more and more professionals manage to bridge the gap between analytics and business. So the companies’ high expectations are still fully justified.