When Answering Questions Through Analytics Falls Short
Be it a data model or analytics, framing smart risks helps you make good decisions on when to use data for your decisions
Business people from all roles hear of the virtues analytics brings to an organization. The overheard comments are rooted in a certain truth — analytics ushers an ability to effectively guide business operations to profitable marketing and cost-efficient operations.
But there are times when analytics can fall short of lofty business expectations. The key to identifying an imminent downfall lies in understanding why analytics should be applied to a given business challenge.
Analyzing data to form a model is not a “snap your fingers” event — despite tools that have “seemingly” made the tasks in creating a model more reliable and easier.
You must rate the importance of the questions you need to solve.
You must know when you’re dealing with decisions that solve good problems or threatening problems.
Not distinguishing the quality of a problem ruins your priorities, because some questions create more tasks — and thus take more effort and time to solve. You want to work on good problems with data models or analytics while addressing…
