At Interana, we love data scientists: not only do they share our passion for data, they enable enterprises to be data-informed. However, these PhD-wielding coders are outnumbered by business people, their many questions, and massive volumes of data. Fortunately, Self-Service Analytics empower business users to explore data themselves, to understand the business and the behavior of customers. This frees up data scientists to work on more technically challenging projects.
Large Quantities of Data and Questions
Companies have long understood that there is value in their data -- thus they save it. Lots of it. The volume of questions arising in data-informed companies is often as large as the volume of data. As more employees experience the power of data exploration, they want access to their data. They bring their expectations from internet research to data analysis: fast results, many exploration cycles, iterating in rapid succession. [See previous blog post Event Data Exploration is a Process Rather Than a Singular Step]
Overwhelming Data Scientists
Data scientists combine mathematical, coding, and business skills in order to discover insights in a company’s “Big Data.” Enterprises cannot hire enough of them, as the typical exploration cycle starts with a business user wanting an answer to a specific question. They frequently do not have the skills to write a massive SQL query or mapreduce job, so they go to their data scientist and explain their question. Some time later, the data scientist returns with the answer, only to be instantly asked the next question.
Frequently, initial questions in data exploration cycles are rather common: counts and basic groupings. These should not require PhD’s, and yet these questions are what data scientist can spend the majority of their time answering. Even intermediate questions about cohorts, sessions, or funnels should be simple enough for everyone to ask on their own. Business users can get frustrated with the time it takes to an answer.
Self-Service Analytics to the Rescue
Self serve analytic tools like Interana save data scientists time and enable business people to be data-informed. These tools lower the technical barriers for accessing and analyzing data, so that people of varying skill levels can ask day-to-day questions by themselves. Interana makes rapid iteration a natural workflow, as employees can explore massive volumes of data in seconds. Delivering insights using a self-service model enables all employees to get faster and easier access to data; while letting the data scientists focus on the strategic data initiatives they were hired for.
[This blog post is part of a series kicked off with article Becoming a Data-Driven Organization: Democratizing Access to Data through Self-Service Analytics]