Data analysts, data scientists and data engineers are three roles that are gaining prominence in the business industry. They developed due to the demand posed to companies by ‘big data’, that is large sets of data that emerged with the rapid advancement in internet technology. The problem is that the exact role of the experts in the three fields is greatly debated. This is partly due to the similarities between the three and partly due to organisations using the terms loosely.

What Do They Have In Common?

The three fields deal with data as such there is an overlap in the skills that are needed for the jobs. An important thing to remember when looking at the three is that while they may overlap in their generalities, each one will delve into a particular area.

In order to understand what the difference between the three jobs is, we need to appreciate what the experts in these three fields do.

The Data Analyst

Data analysts will use data to identify system efficiencies and their problem areas. They will also point out possible system improvements. They are required to have a basic understanding of programming, machine learning, data visualization, statistics and data wrangling.

Data analytics gained momentum in the 1980’s, with the launch of Microsoft Excel. They don’t use technical software like Phyton or R. Their job involves using SQL databases, Share point, Microsoft Excel and Microsoft Access to collect data and present complex ideas in a way that can be understood by the general public.

The Data Scientist

According to IBM’s Vice President of Big Data product, Anjul Bhambhi, a data scientist needs to be inquisitive, able to spot trends in the data in front of them. The field arose when businesses began to understand the competitive advantage in being able to efficiently utilize large sets of data.

Data scientists need a thorough understanding of computer science, math, statistics, modelling and an analytics. They will also have an advanced understanding of data visualization. This is because their job involves checking that the data is accurate, valid and significant. They will have to build a statistical model and use advanced programming software like Phyton, Scala and Closure to problem solve and deliver the impact of a business to the head of an organization.

The Data Engineer

Data analysts and data scientists are quite similar, in that they both collect and interpret data. A data engineer is on the other side of the technical spectrum. They are usually software engineers who use their expertise to deal with the data and machines. They are able to design, build and manage the data’s infrastructure; ensuring that the data flows to its destination so that it can be interpreted by the analysts and scientists. Data engineers work behind the scenes, using complex SQL and Hadoop-based technology to make sure that the systems run smoothly.

What’s The Difference?

When looking at the three jobs, it’s clear that they are complimentary. The subtle differences are in their depth of knowledge, their ability to understand and alter data and the software that they use to get their job done. To find out more about the services provided, check out the ActiveWizards.

Data analysts are quite similar to data scientists but the latter’s job is significantly more technical. They formulate the questions that are answered by data analysts, converting the data before them into a business strategy.

These are the main differences between the three jobs. As in all fields it’s not what they know, it’s what they do!