Want a Raise? Transition from BI to Data Science

What’s the difference between a job in Business Intelligence (BI) and Data Science? Around $45,000 per year, according to CyberCoders salary data, in each of the top 10 metropolitan markets for data professionals.

In smaller cities like San Diego, this equates to a 55% higher starting salary for Data Scientists, who can fetch over $150,000 annually after a few years on the job in larger coastal markets. The salary for an experienced BI professionals tops out at $136,000 in Silicon Valley, and ranges from $95,000-$110,000 in the usual tech hub cities.

Jobs in BI and Big Data utilize similar tools and skill sets to achieve to meet the same general business goals. There is still some overlap and ambiguity about the specific functions unique to the respective professions, as to be expected in this relatively new line of work. CyberCoders' data about top skills for Data Science jobs will be used to ferret out the key differences between the two.

In any case, corporate obsession with data-driven decision-making is good news for both BI and Data Science specialists.

Data Science is BI with a higher vision

BI professionals develop programming skills and the proper business mindset after a few years on the job. There are certain qualities distinct to Data Science that set it apart from BI, however.

Data Science usually works at a larger scale, analyzing multiple data sets located on different networks, and thus requiring knowledge of different tools and programming. Having elite chops in mathematics and logic is critical. The real mark of a Data Scientist, though, is a creative understanding of how data insights drive ROI for a business, and the ability to turn those insights into action.

BI fundamentals provide groundwork

Folks entering BI usually begin with either a functional (“business sense”) understanding of data, or technical proficiency with the programming tools used to collect, analyze, and store the data. According to CyberCoders data, the technical skills are in higher demand, so let’s look at those first.   

Topping the list is, obviously, Structured Query Language (SQL), the coding standard for relational database systems. Microsoft-specific software SQL Server Reporting Services (SSRS) and SQL Server Integration Services (SSIS) are the top two and three skills that companies want for BI positions.

Data warehousing, specifically Exact, Transform, and Load (ETL) management, and  demonstrable experience with Tableau desktop analytics applications are also core requirements for the job.

Learn the programming to transition into Data Science

Hadoop and Hadoop-related programming is the key to breaking into Data Science. This open source Apache software framework is the dominant tool for developing programs designed to process data sets from multiple database servers, which, more than anything else, is the essence of how Big Data is understood.

Since Hadoop is a Java-based program, Java is second to Hadoop in employers’ most desired skill; knowing MapReduce, which is the programming paradigm for Hadoop’s primary function, is critical for crossing into the Data Science realm as well. Someone with a BI background might know data warehousing, but should probably know Apache Hive for doing it with Hadoop.

Instead of SQL, NoSQL is used for analyzing multiple horizontally-connected (cloud-based) databases, so BI professionals will need to add that to their arsenal.

Data analysis tools utilize general-purpose programming languages Python or R, and for the most part, engineers in different verticals use one or the other.

Final Thoughts

Obtaining the right programming experience will open the door to a career in Data Science. Advancement in the profession will require adding creativity to the mix in how data drives business growth. If you have the right kind of mind, the sky is the limit in this constantly evolving industry.