How Can Analysts Become More Productive?
A typical analyst at a typical organization should be able to complete, on average, at least 1-2 typical analytics projects (think of a typical dashboard build or ad hoc analysis) per week. In fact, at a level 3 analytics maturity organization, the typical analyst (and especially high performing analysts) could generally complete 3 or more typical analytics projects in a week through using the most efficient analytics tools and techniques. Now it is possible that an organization’s analytics project could be particularly unique and complex, truly requiring more time to complete on average than at other organizations; but there could be a big opportunity for an analytics team to become more efficient if the typical analytics project is taking more than a week to complete on average. A simple dashboard build or ad hoc analysis could be completed in a day or less. Even a data science model build could take 3 days or less if using an efficient reusable asset to build, assess, and deploy it. Yet, in reality, the same data science model build could take several months (or even an entire year!) to build and implement at an organization where an inefficient process is being used.
You might wonder “how can there be such a drastic difference in analyst productivity across organizations?”. An analytics team’s productivity can be influenced by several factors. A big one is the efficiency of the tools they’re using. If your organization’s reporting is primarily manually created in Excel, your analytics team’s productivity could be drastically improved by migrating to an automated and interactive dashboarding tool like Power BI or Tableau. It can reduce or eliminate the time spent manually updating reporting and reduce or eliminate the time spent manually addressing follow-up requests to see the report filtered down to a particular geography or business segment. In addition, using analytics coding tools like SQL and Python for analysis can be much more efficient in the long-run than trying to perform advanced analytics in Excel.
Another reason for the difference in efficiency level across analytics teams could be the processes they’re using. While one analytics team struggles to find, create, or understand data tables available, another analytics team has implemented a process to easily do this with a set of documented and curated datasets. While one analytics team addresses each analytics project in isolation, another analytics team creates “reusable assets” for common requests that completely or significantly automates the solution for future requests.
Whether your analytics team’s efficiency is lacking due to the tools its using, the processes its using, or a number of other reasons, it may be time for an analytics transformation. This is an ambitious task, but Value Driven Analytics can dramatically increase the value your analytics team creates by helping it move to the next level in analytics maturity with a process that has proven to work at multiple companies.
