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How data scientists differ from BI data analysts?  A data scientist and BI analyst can take Big Data analytics and data warehousing programs to the next level. They can help in deciphering what the data is actually telling a company. They are also able to segregate relevant data from irrelevant data. A data scientist and a BI analyst will hopefully be able to leverage the enterprise data warehouse, currently in place, to take a deep dive into the data. Organizations must, therefore, know about the difference between data scientists and data analysts.

• By Naveen Joshi • 1:23 am

As a discipline, Business Analytics has been around for 30 years, starting with the launch of MS Excel in 1985. Before this, data analytics was only limited to manual calculations using trial and error methods. There are two major trends that have contributed to the foundation of the Data Science phenomenon.  First, the use of technology in various walks of life, particularly the use of Internet, led to an unprecedented data boom; the kind of information that is available to organizations now is nearly infinite. Secondly, new technologies made analyzing and interpreting such vast amounts of data possible and companies can now use all this data in decision-making. Before understanding about the difference between data scientists and data analysts, let’s take a look at how the role of data scientist was born.

Advent of the Data Scientist

Enterprises saw the availability of large volumes of data as a source of competitive advantage;  companies who could utilize this data effectively would take better decisions and would be ahead of the growth curve. To make sense out of such data, there arose a need for a new skill set that  included the skill to draw customer/user insights, business acumen, analytical abilities, programming skills, statistical skills, machine learning skills, data visualization, and much more. This led to the emergence of the role of a data scientist.

Difference between Data Scientists and Data Analysts

 

Data Scientist BI Data Analyst
A data scientist is likely to have a strong business sense and the ability to effectively communicate data-driven conclusions to business stakeholders. A data scientist will not just address business problems, they’ll also pick the right problems that have the most value to the organization. A BI analyst’s job is to find patterns and trends in an organization’s historical data. While BI is largely based on exploration of past trends, data science consists of finding predictors and the significance behind those trends. Thus, the main goal of a BI analyst is to evaluate the impact of certain events on a business’ bottom line or compare a company’s performance to that of other companies in the same marketplace.

 



As we now know about what the BI data analyst and data scientist roles are all about, let’s take a closer look at the key differences between the two:

  • Typically, a data scientist is expected to formulate questions that can help businesses in solving their problems, while a BI data analyst is given questions by the business team that they must solve.
  • Both roles are expected to write queries, work with engineering teams to source the right data, and focus on deriving information from data. However, in most cases, a BI data analyst is not expected to build statistical models; rather a BI data analyst typically works on simpler structured SQL or similar databases or with other BI tools/packages.
  • The role of data scientist calls for strong data visualization skills and they must have the ability to convert data into a business story. A BI data analyst is normally not expected to be adept at business and in advanced data visualization.

Companies must know how to distinguish between these two roles and the areas where a data scientist and a business analyst can add value.

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