It’s no secret that organizations have been increasingly turning to advanced analytics and artificial intelligence (AI) to improve decision making across business processes — from research and design to supply chain and risk management.
Along the way, there’s been plenty of literature and executive hand-wringing over hiring and deploying ever-scarce data scientists to make this happen. Data scientists need to build the analytics models — largely machine learning and, increasingly, deep learning — capable of turning vast amounts of data into insights.
More recently, however, companies have widened their aperture, recognizing that success with AI and analytics requires not just data scientists but also entire cross-functional, agile teams that include data engineers, data architects, data-visualization experts, and — perhaps most important — translators.
They help ensure that organizations achieve the real impact from their analytics initiatives (which has the added benefit of keeping data scientists fulfilled and more likely to stay on, easing executives’ stress over sourcing that talent).
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 and 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 then goes to data scientists and professionals to further clarify and produce crucial reports and data with which management teams can make strategic and operational decisions.
To understand more about what translators are, it’s important to first understand what they aren’t. Translators are neither data architects nor data engineers. They’re not even dedicated analytics professionals, and they don’t have deep technical expertise in programming or modelling.
Instead, translators play a critical role in bridging the technical expertise of data engineers and data scientists with the operational expertise in marketing, supply chain, manufacturing, risk, and other front-line managers. In their role, translators help ensure that the deep insights generated through sophisticated analytics translate into impact at scale in an organization.
Data professionals get all the supplies within an organization and, if the enterprise wishes to increase staff, the cost of these highly skilled professionals can be prohibitive. These resources play a minor role and workers spend too much time on projects that are:
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, the business user may miss day-to-day deadlines or information 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 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 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.
When identifying possible candidates to perform the Analytics Translator role, the organization should look for several skills and abilities:
When identifying possible candidates to perform the Analytics Translator role, the organization should look for several skills and abilities:
If this role is 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 become 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.
The wide range of responsibilities — leader, communicator, project manager, an industry expert — inherent in the translator role makes the following skills essential.
Domain knowledge is by far the most important skill for any translator. Translators must be experts in both their industry and their company to identify effectively the value of AI and analytics in the business context. They must understand the key operational metrics of the business and their impact on profit and loss, revenue, customer retention, and so on. Knowledge of common use cases (e.g., predictive maintenance, supply chain management, inventory management, personalized marketing, churn prediction, etc.) in their domain is important.
Apart from domain knowledge, translators must have a strong acumen in quantitative analytics and structured problem-solving. They often have a formal STEM (science, technology, engineering, and mathematics) background or self-taught knowledge in a STEM field. And while they need not be able to build quantitative models, they need to know what types of models are available (e.g., deep learning versus logistic regression) and to what business problems they relate. Translators must also be able to interpret model results and identify potential model errors.
A mastery of process-management skills is a must. Translators should be able to direct an analytics initiative from ideation through production and adoption and understand the life cycle of an analytics initiative and the common pitfalls.
Besides these “teachable” skill sets, translators also should have an entrepreneurial mindset. They need the enthusiasm, commitment, and business savvy to navigate the many technical, political, and organizational roadblocks that can emerge. This is often less teachable—or at least less straightforwardly so—and the availability of entrepreneurial individuals can depend in part on the organization’s culture.
A mastery of process-management skills is a must. Translators should be able to direct an analytics initiative from ideation through production and adoption and understand the life cycle of an analytics initiative and the common pitfalls.
Besides these “teachable” skill sets, translators also should have an entrepreneurial mindset. They need the enthusiasm, commitment, and business savvy to navigate the many technical, political, and organizational roadblocks that can emerge. This is often less teachable—or at least less straightforwardly so—and the availability of entrepreneurial individuals can depend in part on the organization’s culture.
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