Advanced Analytics is the new frontier of competition and business success. While some businesses have the luxury of employing data scientists and professional analysts to help them sift through and analyze data, others do not have the budget or the structure to support these roles and, even those who employ professional data scientists do not have unlimited resources or budgets.
With the growth of self-serve, Augmented Analytics, business executives can consider another alternative to complement existing data scientist staff or help fill the void of data experts in a smaller business.
As business users embrace and adopt self-serve augmented data discovery tools, the enterprise will witness the emergence of Citizen Data Scientists. The goal of enabling Citizen Data Scientists is to optimize business decisions and the time of data scientists so that business users can confidently leverage advanced analytics tools to make decisions and data scientists can focus on more critical, strategic activities. 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.
Yet, another emerging role in the world of self-serve augmented analytics is that of Analytics Translator. This role can be filled by power users and those team members who have or develop a good grasp of analytical concepts. The Analytics Translator is an important member of the new analytical team that includes IT, data scientists, data architects and others.
Analytics Translators are not analytical specialists or data scientists but, with the right tools, they can translate data and analysis and add considerable value. These power users can leverage their knowledge of the business to focus on targeted areas like production, sales, purchasing, distribution and cross-functional initiatives. They can identify patterns, trends and opportunities and, where necessary, hand off information to data scientists to clarify or produce reports for critical strategic and operational decisions.
Possible Analytics Translator candidates can come from varied backgrounds. They might be power users of self-serve advanced analytics tools, or experts in industry, organizational or functional areas. Good candidates will also include those with a reasonable knowledge and comfort with analytics who might serve as liaison between IT and business users or between analysts and business users, or those who are able to work with metrics, measurements and project milestones and those who are forward-thinking role models within the organization.
Analytics Translators can combine domain, organizational and industry skills with self-serve advanced analytical tools to encourage and nurture data democratization, and optimize analytical business results within the organization. This approach to leveraged advanced analytics can add value to management, to data scientists, IT and to business users.
The role of Analytics Translators 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 an organization consciously focuses on the cultural shift that is made possible by data democratization and bringing advanced analytics to business users, it can transform business users into Citizen Data Scientists and identify Analytics Translators to act as liaisons and role models and encourage the positive culture change and improvements in productivity and decision-making across the enterprise.
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