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Introducing the Analytics Translator: A Key Partner in Data Projects

Data Scientists and Engineers have always played a crucial role in data projects, using their expertise to unlock data and build models that turn vast amounts of data into actionable insights. However, as more companies explore the potential of AI, another role has emerged as essential to the industry: Analytics Translators. But who are these professionals, what exactly do they do, and what skills do they need?

What is an Analytics Translator?

Analytics translators are experts in translating complex data analytics into easy-to-understand information for decision-makers. In many organizations, individuals outside of the data field may not fully understand how to leverage data to create value for their specific business needs. This can lead to missed opportunities in translating data insights into actionable goals. This is where Analytics Translators play a vital role in bridging the gap between technical experts and business decision-makers. They recognize valuable use cases, giving organizations a comparative advantage. By ensuring that data use cases are prioritized based on feasibility and value, an analytics translator can help to focus on creating business value and increase the success rate of AI projects.

Thus, Analytics translators bridge the gap between data scientists, business stakeholders, and executives. Their main goal is to turn data into clear recommendations or instructions for decision-makers. They are able to communicate key insights in the form of recommendations to other experts within the company, such as marketers, frontline managers, and supply chain professionals.

Moreover, analytics translators are responsible for ensuring that data use cases remain focused on the intended purpose and value of a business problem. According to Gartner, 85% of AI projects fail , among others because people lose sight of the bigger picture when working on a project. Analytics translators help maintain a focus on creating business value, which lowers the failure rate. They prioritize use cases on feasibility and value, and promote an agile approach that encourages failing fast.

The Skillset of an Analytics Translator

Working as an analytics translator requires extensive knowledge of quantitative analytics and data science. While it’s not necessary for them to build data models, they should be able to analyze data and apply this data to business problems.

Additionally, Analytics translators also need excellent project management skills, as they are often involved in multiple projects across various departments within an organization. Communication skills are essential in order to understand the issues within each department and properly explain how their findings can help solve these issues, bridging the communication gap between the data and business professionals.

The Future of Data Science

Analytics translators are key to helping data scientists and business stakeholders gather the right data and translate it into valuable insights. By prioritizing data use cases on feasibility and value, and promoting an agile approach, they help lower the failure rate of AI projects. As more companies explore the potential of AI, the role of analytics translators will become increasingly important. In order to have a clear data strategy and focus on business value, Analytics Translators are essential players in your data projects.