Machine Learning Times
Machine Learning Times
AI Success Depends On How You Choose This One Number
 Originally published in Forbes, March 25, 2024. To do...
Elon Musk Predicts Artificial General Intelligence In 2 Years. Here’s Why That’s Hype
 Originally published in Forbes, April 10, 2024 When OpenAI’s...
Survey: Machine Learning Projects Still Routinely Fail to Deploy
 Originally published in KDnuggets. Eric Siegel highlights the chronic...
Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...

10 years ago
Data Scientist: Evolution of the Business Analyst


The business/data analyst role is evolving into a new role due in part to the new technology of big data. The data scientist role has emerged because of the increase in breadth and depth of data being examined. In addition to computer science and analytical skills, a data scientist has a strong business sense and the ability to communicate findings to both business and IT teams. They review data from multiple sources, looking at it from various angles, searching to discover insight into the data which will provide a competitive advantage or help resolve a business issue.

Data scientists of today don’t just crunch numbers; they view the universe as one large data set and work to decipher relationships in the data.

Although business analysts and data scientists are both data focused roles, they differ in that a business analyst analyzes data and assesses requirements from a business perspective, designs and recommends systems and processes related to an organization’s overall system. On the other hand, a data scientist is more focused on the relationship of the data in an organization’s database. They use their skillset to compare data to competitors in the industry. They perform statistical analysis on data and provide insights based on that analysis. Business analysts and data analysts must be proficient in modeling, but while business analysts model company infrastructure, data analyst model business data structures.

According to a Gartner estimate, there will be approximately 4.4 million new IT jobs in the next couple of years. The issue is that there are only enough qualified people to fill about a third of those jobs. Currently there are no university programs offering degrees in data science. Also, companies struggle with where the role fits in the organization, how data scientists can add the most value, and how their performance should be measured.

So, how does a business analyst become a data scientist?

If you have a background in SQL, you are on your way. An easy first step is to begin using Hive, which provides access to large datasets on Hadoop. The next step would be to become knowledgeable in algorithms such as recommendation engines, decision trees, and NLP and become familiar with current implementations of tools such as Mahout, WEKA, or Python’s Scikit-learn. EMC offers training and certification which assists business analysts and other professionals in transitioning to the world of data science. Zipfian Academy, a San Francisco-based facility offers a 12-week training program that will teach its students what they need to know to be proficient data scientists. The University of California at Berkeley launched its Master of Information and Data Science program, which school officials called the nation’s first online master’s degree program for data scientists.

The great thing about being a business analyst is working as a liaison between the business and technical developers. BSAs get to work hand-in-hand with the business clients to gather/define requirements of a system or process to improve productivity while at the same time working with the technical team to design and implement the system/process. Understanding both the business and technology allows the BSA to suggest technology-sound process improvements to the client. I find the role of data scientist very interesting and for some business analysts, I believe, a natural transition. Delving deeper into the data and understanding the relationship of the data across systems in a company and in relation to competitors allows the analyst to draw insights and provide more informed recommendation. Similar to the partnership that has developed over the years between a business analyst and technical developer, I see a similar partnership evolving between a business analyst and a data scientist.

By: Dawn Coppola,
Originally published at

Leave a Reply