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).
What is a Citizen Data Scientist (Citizen Analyst)?
Gartner defines a Citizen Data Scientist as ‘a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.’ A Citizen Data Scientist is different from a true Data Scientist in one crucial way; namely, they do not have the skills or training to be an analyst or a programmer but, with the right tools, they are capable of generating reports, analyzing data and sharing data to make decisions.
Citizen Analysts represent a new breed of business user. These business users have adopted business intelligence and advanced analytical tools to gather and analyze data from varied data sources and use that analysis to identify the root cause of problems, identify opportunities, solve problems and share crucial data to support business decisions. Citizen Analysts create and generate data models and use sophisticated analytics that are supported by easy-to-use interactive BI dashboards. By definition, Citizen Analysts are not data scientists, or professional analysts or IT staff. Rather, they hold varied positions within the business organization and use data analysis to support decisions made within their business role, team or responsibility.
How Does the Analytics Translator Role Differ?
The Analytics Translator is an important member of the new analytical team. As organizations encourage data democratization and implement self-serve business intelligence and advanced analytics, business users can leverage machine learning, self-serve data preparation, and predictive analytics for business users to gather, prepare an analyze data. The emerging role of Analytics Translator adds resources to a team that includes IT, data scientists, data architects and others.
Analytics Translators do not have to be analytical specialists or trained professionals.
With the right tools, they can easily translate data and analysis without the skills of a highly trained data pro. Using their knowledge of the business and their area of expertise, translators can help the management team focus on targeted areas like production, distribution, pricing and even cross-functional initiatives. With self-serve, advanced analytics tools, translators can then identify patterns, trends and opportunities, and problems. This information is then handed off to data scientists and professionals to further clarify and produce crucial reports and data with which management teams can make strategic and operational decisions.
When identifying possible candidates to perform the Analytics Translator role, the organization should look for skills that can be nurtured and optimized as an asset.
- A power user of self-serve BI tools
- Recognized as an expert in a functional, industry or organizational role
- Comfortable with building and presenting reports and use cases
- Works well with technical and management teams
- Manages projects, milestones and dependencies with ease
- Able to translate analysis and conclusions into actionable recommendations
- Comfortable with metrics, measurements and prioritization
- Acts as a role model for user and team member adoption of new processes and data-driven decisions
If this role is recognized as important to the organization, most enterprises will structure a logical program to identify and train candidates to ensure uniform skills and performance.
By combining domain, organizational and industry skills with self-serve analytical tools, the Analytics Translator can help the enterprise to achieve low total cost of ownership (TCO) and rapid return on investment (ROI) for its Business Intelligence and Advanced Analytics initiatives and can encourage and nurture data democratization and optimal analytical business results within the organization.
Citizen Data Scientists/Citizen Analysts play a crucial role in day-to-day analysis and decision-making, using Self-Serve Business Intelligence Tools. Analytics Translators bridge the gap between IT, data scientists and business users, and move initiatives forward by acting as a liaison and topic expert to help the organization focus on the right things to achieve its goals.
Does your organization need Citizen Data Scientists AND Analytics Translators? The answer is, YES!
Original Post: Analytics Translator? Citizen Data Scientist? What is the Difference?