One of the greatest benefits of an enterprise-wide analytics platform is that it enables data analysts and scientists to work collaboratively and brainstorm on approaches for solving a company’s top challenges.
Because certain analytics platforms promote sharing and collaboration between users, data analysts and scientists can manipulate and dig down on data and view the results for all to see.
Using Data to Solve Problems
Let’s say members of an analytics team for an electronics retailer are working on a business challenge together: to determine the top reasons why the company has been losing market share over the past six months.
A cursory examination of the data reveals that show rooming – consumers test out electronics equipment in a physical store before ordering products online at lower prices – has resulted in a four percent drop in market share over the past six months.
But that only explains part of the downturn. By probing the data together, the analytics team applies different variables to the challenge. These include differences in prices between the electronics retailer, online, and physical competitors, as well as comparisons between messaging and offers made by the retailer and rivals during this same period.
Further analysis reveals a drop in the retailer’s customer satisfaction and net promoter score (a customer’s willingness to recommend a company to others) during the six-month period.
An even deeper dive by a few of the data scientists reveals that customer satisfaction and the net promoter score for the retailer’s biggest rival rose dramatically during this period while the retailer’s scores remained stagnant.
Collaborating to Get the Job Done
By teaming together on the challenge and exploring different possibilities for the root cause behind the drop in market share, the analytics team is able to provide senior management with meaningful insights that can be used to address the market share conundrum.
In some cases, data analysts and data scientists like to work independently. While there are certainly advantages to pooling brainpower to attack corporate challenges, there are ways to satisfy the lone-wolf data scientist while achieving a level of collaborative analytics.
Because independent data scientists have their own unique approaches to problem solving, decision-makers can compare and contrast the results of analytical efforts by data scientists who are charged with attacking the same problem.
Doing so enables organizational leaders to compare and contrast the hypotheses and results proffered by each data scientist. From there, they can either act on the most suitable findings or use a blended approach to problem-solving.