I'm Luciana Padua, Analytics Translator
Master in Data Mining & Knowledge Discovery, Bachelor in Business
Why do you need a Translator?
Many companies have invested in Data Science but haven't been able to make the most of it due to lack of people understanding both business and technical language. Many data scientists are great in programming, but they do not know which data to input to their models or how to interpret the results. Many business people are not technical enough to understand Data Science and cannot translate their business needs into analytics problems.
Translators play a critical role in bridging the technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other frontline managers. In their role, translators help ensure that the deep insights generated through sophisticated analytics translate into impact at scale in an organization.
This where I come to play, to merge the two worlds, bridge the gap between business and analysts.
What makes me an "Analytics Translator"?
In 2012 Hardvard Business Review named Data Scientist as the sexiest job of the 21st century. More recently, however, companies have widened their aperture, recognizing that success with AI and analytics requires not just data scientists but entire cross-functional, agile teams that include data engineers, data architects, data-visualization experts, and — perhaps most important — translators, the new must-have role.
Translators must be experts in both their industry and their company to effectively identify the value of AI and Analytics in the business context.
In my 15 years of professional experience, I have worked for many different industries: CPG, Banking, Telco, Sporting Goods and more recently NGOs. From a logistics analyst to Director of Consumer Insights, I have had the opportunity to help companies to make data driven decisions in different areas of the company.
General Technical Fluency
Translators must possess strong acumen in quantitative analytics and structured problem solving. And while they don’t necessarily need to be able to build quantitative models, they do need to know what types of models are available and to what business problems they can be applied.
Data Science became a buzz word, now replaced by AI and ML, and there must be people in the company to identify when its complexity will add more value compared to traditional descriptive analytics.
Translators should be able to direct an analytics initiative from ideation through production and adoption and have an understanding of the life cycle of an analytics initiative and the common pitfalls.
In my career I have lead many projects. I gained most of my project management skills when working as a presales in a technology company. Proof of Concepts are the ideal mini analytics project to develop this kind of experience.
Translators need the enthusiasm, commitment, and business savvy to navigate the many technical, political, and organizational roadblocks that can emerge.
I would say that from all the skills needed, this my strongest one. I've been recognized many times as an analytics ambassador in the companies I've worked for. Leveraging my teaching and social skills, I have managed make the most reluctant teams embrace analytics.