6 Biggest Misconceptions About Data Scientists, Data Engineers and Business Analysts.

What exactly does each of these professionals do? They all work with data, and they all seem to “analyze” information so… what are the main differences among them and the biggest misconceptions about what they do for a living?

The many roles involved in big data have to overlap at a certain point. It’s inevitable, however, to know the differences between a data scientist, a data engineer and a business analyst. So let’s pinpoint what they bring to the table. At the end of the day, the all are part of a team that deals with big data.

  1. Data scientists are people who can come up with magic formulas to make data meaningful. Data scientists understand the process of analyzing and finding trends and patterns in data, but not all of them know which of these findings can help the business. It’s ideal but sometimes they need the help of other professionals, known as domain level experts or business analysts. The more competitive jobs for data scientists sometimes ask for so called data science unicorns (data science + domain level expertise), but in most cases business analysts help the data scientist obtain the information they need to get their jobs done.
  2. Data scientists are “numbers people”. To be sure, their primary role is to work on large datasets with machine learning algorithms and then create machine learning models. However, they should also be very visual people and know a thing or two about how to present their projects to specific audiences. Numbers per se may offer the whole picture of a trend or of a specific finding, however, telling a story with visual aids is much more effective at getting the message across.
  3. The role of the business analyst is synonymous with project administration. Most people know that this position includes taking notes, documenting project deliverables, arranging follow up meetings, etc. One of their primary functions however is to serve as a translator between technical staff and business managers. Good people and presentation skills are obviously important.
  4. Business analysts’ main role is to capture clients’ requirements and hand them out to the developer. Not quite. Clients sometimes ask for things that are of lesser value to them than other alternatives. The business analyst, by putting on the consultant hat, can help parse out these asks and convert them into deliverables that are valuable for them and the business.
  5. Data engineers are synonymous to programmers. They’re typically software engineers who can build a strong foundations for data scientists or analysts. Vast amounts of good data aren’t of any use if the data isn’t accessible. It’s important to be aware that if an engineer has not worked with the specific technology that your company has, but has good technical problem solving and programming skills, he will be invaluable even if he hasn’t dealt with much data-driven projects before. Savvy engineers love a good challenge.
  6. Big data is the only way to go and the first step for everything data-related. Not necessarily. In fact, it’s better to first think in terms of making your company more data driven and focus on getting the data you need to address the goals you have. These will be your first steps… and you don’t need big data or a data scientist to figure these things out. Start small and think big.

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