Data literacy is a very popular idea these days. As business users adopt and embrace data and advanced analytics, features like predictive analytics for business users make it easier for a user with average skills to leverage data to make decisions and share information and, in so doing, to become more literate about data analytics.
No business, large or small, has unlimited funds and resources. In a world where data analytics is more important than ever to the business bottom line and competitive position, the typical business cannot afford to hire dozens of data scientists but it absolutely must have access to detailed, clear data analysis that will drive the bottom line and ensure success.
As the need for advanced analytics increases in organizations, enterprises large and small struggle to find and sustain the professional resources they need to meet their requirements for data, analysis and strategic direction.
A business that does not optimize its resources is doomed to fail. In this rapidly changing business environment and market, every organization must make the best of precious human resources. No one has enough funding to hire additional resources to get the job done and, when there are extra funds, those funds are quickly earmarked for new products, marketing and other crucial activities.
So you want to transform your business users and encourage learning for Citizen Data Scientists to enable data literacy across your enterprise? If your business is like most, your average business user doesn’t know (or need to know) the details of sophisticated algorithms and analytical techniques. But, you DO want to encourage data literacy and provide your business users with the tools they need to perform analytics.
There is a new business role on the horizon and, at first glance, it may seem very much like a role that was introduced a few short years ago. This new enterprise role is known as an ‘Analytics Translator’ and, while there is some confusion regarding the distinction between this role and the newly minted Citizen Data Scientist or Citizen Analyst, there are some subtle but important differences. In a previous article (What is an Analytics Translator and Why is the Role Important to Your Organization?), we discussed the definition of an Analytics Translator. Here, we will discuss the role of Citizen Data Scientist and Analytics Translator and how they differ. To understand these roles, let’s look first at the somewhat more familiar role of Citizen Data Scientist (AKA Citizen Analyst).
There is a lot to business intelligence and it is easy to get lost in the details but, if you want to promote a self-serve BI environment for your users, you will want to have a good understanding of your product options. Look for a solution that offers key performance indicator analytics, with an easy-to-personalize KPI dashboard. Look for business intelligence reporting tools that allow for drill down, drill through, and produce attractive, clear, concise reports that can be easily shared and that are accessible on mobile devices with diverse sizes and screen resolution.
To be successful in business, every organization must find a way to accurately forecast and predict the future of its market, and its internal operations, and better understand the buying behavior of its customers and prospects. With the democratization of business intelligence and the advent of self-serve business intelligence tools, organizations hope to encourage and create Citizen Data Scientists and enable the average business user to leverage sophisticated predictive algorithms and BI tools without the expertise and skill of a trained data scientist. This approach allows users who are not statisticians, data scientists, programmers or analysts to leverage self-service tools to confidently make business decisions, share data, and reports and present data in a way that allows the organization to identify trends, patterns, opportunities and challenges.
It seems that everyone today is talking about Citizen Data Scientists. For some organizations the concept is perhaps frightening. The term may conjure up images of novices trying to do the work of someone with years of training and failing miserably in the process. For others, the image may be of an organization run amuck with business users leveraging sophisticated advanced analytics tools to create and share data that may be misinterpreted or distributed improperly.
