But if you’re already familiar with the subject, you’ve probably noticed that the term data scientist encompasses a lot of different skills, competencies, and responsibilities. And just as sexy as data scientist, data engineer is in the top 10 of LinkedIn’s 2020 Emerging Jobs Report in the US. The big problem is that digital roles are changing with the increased adoption and advancements in technology, and titles can be inaccurate, as different companies and industries use different names for similar jobs.
And now you've realized that it's not a scientist or engineer who will save your BI and help you code number of philippines make the best business decisions, but rather a team of experts. So in this article adapted from MIT Management Sloan School , I bring you a suggestion for putting together your Data Analytics dream team .
Data analytics dream team
Data Analytics Dream Team
Data Engineer
Data engineers are a central part of a data analytics operation. Engineers collect and manage data, and manage data storage. Their work is seen as the foundation of the operation, as they take large amounts of unstructured data and prepare it for other people who make business decisions.
Data engineers tend to have backgrounds in software engineering or computer science, according to Michelle Li, director of the Master of Business Analytics program at MIT Sloan and former director of the Global Technology Group at UBS Investment Bank. “Data engineers are really the backbone,” she said. “If you’re building a house, they’re the structural engineers.”
While engineers maintain the data, data scientists figure out what to do with it. Both positions are the starting point for most companies with big data strategies.
Data scientists tend to be a bit more business-oriented, while data engineers focus more on infrastructure, scale and data quality, said Tim Valicenti, a 2018 graduate of the MIT Sloan MBA program and a senior analyst at McKinsey, a role that combines data scientist with classic management consultant. Data scientists also use artificial intelligence and machine learning to conduct analysis and gain insights.
Remember here, the skills we mentioned that make scientists really sexy:
MATHEMATICS – Of course, this skill must be inherent to any data scientist, especially when we talk about statistics and linear algebra.
COMPUTER SCIENCE – Essentially programming and infrastructure design.
STORYTELLING – The ability to create narratives around your work, integrating the results into a larger story. A data scientist must be able to ask the right questions. This skill is harder to assess than any specific skill, but it is essential.
BUSINESS UNDERSTANDING – This is a skill that we believe is very consistent with the current scenario. “Business thinking” is already a highly valued skill in different professions and is viewed favorably by the most demanding recruiters.
Data Translator
While data engineers and data scientists are established roles, several newer titles are taking analytics operations to the next level – such as Data Translators, who serve as a kind of bridge between data and traditional business operations, translating insights gained from analytics into actionable insights.
As companies invest more resources and become more dependent on data, some translators may also take on training roles and educate others on how to use data.
Data Ontologist
This title may not be as popular, but think of it as a knowledge engineer who works to embed intelligence into machines. While traditional analytics allows companies to analyze trends and past events, ontologists take a broader view, acting as a sort of brain of the company that takes the results of analytical findings and combines them with information from inside and outside the company to answer a question. They take the whole natural language questioning thing to the next level. In effect, they consume the results of advanced analytics in their knowledge graphs and ontologies and arrive at real answers to business questions.
And the leader?
Every team needs a leader, and so far, companies are taking a variety of approaches when it comes to who is in charge of data operations. Some companies are adding chief data officers, or even chief data analytics officers, to the C-suite. According to a 2020 NewVantage Partners survey of more than 70 executives from Fortune 1000 and other leading companies, about 57% of companies have appointed a chief data officer. However, only 28% of respondents said the role is established, and about 27% said there is no single point of responsibility for data.