Today, enterprises recognize the critical value of advanced analytics within the organization and they are implementing data democratization initiatives. As these initiatives evolve, new roles emerge in the organization. The newest of these analysis-related roles is the ‘analytics translator’. As the enterprise considers the relevance of this new role within the business, it is important to understand the responsibilities of an Analytics Translator, and how this role might help the organization to achieve its goals.
What is an Analytics Translator?
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.
Why is an Analytics Translator Important to Your Organization?
IT resources and data professionals are typically in short supply within an organization and, if the enterprise wishes to increase staff, the cost of these highly skilled professionals can be prohibitive. In the average organization, these resources are usually stretched thin and time is wasted on projects that are:
- Too complex for business team members
- Conceived or inappropriate for attention at the data scientist or IT level
- Comprised of incomplete requirements
- Required for day-to-day or immediate analysis or data sharing initiatives
- Tactical or low-level operational in nature
The time it takes for a data professional or IT professional to review the project and assign a priority, will take them away from more strategic or more critical tasks and, in the process, the business user may miss day-to-day deadlines or information that is critical to them. Perhaps, the data professional may need more information on requirements, which will further delay the project. There are many examples of unnecessary or inappropriate data analysis requests and many instances where a business user with access to analytical tools might be able to do the work themselves. But, there are even more examples of projects or analytical requirements that fall somewhere between the skills of a business user and the skills of a trained data scientist and just as many examples of poorly understood or poorly translated data analysis that sends a business user off in the wrong direction.
That is where the Analytics Translator comes in. Using her or his knowledge of the industry, the organization, the team and the analytics tools, the translator can play a crucial role in understanding requirements, preparing data and producing and explaining information in a way that is accurate and clear. As this role evolves within your organization, you will find that, by allowing the average business user to work with the Analytics Translator, that business user will become more knowledgeable and skilled in interpreting and understanding data.
The Ideal Analytics Translator
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.
As self-serve Advanced Analytics and data democratization becomes more common across industries and organizations, the role of the Analytics Translator will also become more important. As a power-user of BI tools and Self-Serve Analytics, the translator functions as a liaison between critical analytical and technical resources and the business user community and ensures that BI tools will be adopted and shared across the enterprise.
In our next article, we will consider the difference between the Analytics Translator and the Citizen Data Scientist or Citizen Analyst.
Published at Fri, 23 Feb 2018 09:30:00 +0000