Business Intelligence is changing. It used to be enough to deliver information in the form of reports, dashboards and visualizations. However, over the last few years a new kind of BI consumer has emerged. This is a business user who demands more than just KPIs and data dumps into Excel; who expects the latest analytical technologies to be available at their fingertips: Citizen Data Scientists.
A data scientist is an employee who specializes in extracting knowledge and insight from (often big) data. Typically this is quite a niche and specialized role, performed by only a few people within a business. Citizen data scientists, on the other hand, are traditional business users who are being given the tools to perform this deep analysis themselves.
Why has this happened? One reason is that the traditional data scientist role requires a combination of statistical knowledge, intimate understanding of business processes and a high level of technical ability. The problem is that combining all these skills is really difficult, making quality data scientists a rare and expensive commodity.
On top of this, BI and analytics are becoming more prevalent. Whether it is in your online bank account, sleep tracker app or Fitbit, BI and analysis of data is now commonplace in our lives. This means BI consumers are now starting to expect robust reporting as a minimum. They aren’t afraid of trying to explore the data deeper to gain more meaningful understanding themselves. It also conveniently provides demonstrable value of analytics in their lives. In turn, this has put analytics on the agenda in the boardroom.
As a result, data discovery tools are proliferating fast. Products like SAP’s Predictive Analytics are tailored towards enabling traditional end users to do meaningful analytics. They provide an intuitive UI, but sit upon advanced in-memory databases. This means that not only do users have potent front end tools, but also the computing power behind them to enable large and complicated queries.
Additionally, big data is now being meaningfully brought into data analysis engines. Tools like HANA VORA enable the querying of Hadoop ‘big-data’ clusters in existing tools. This democratizes big data. Users can now get the questions they want answered themselves.
Imagine you’re an area manager, figuring out which of your customers to target in the next campaign.
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