Data is the new oil. All of us agree that big data is, well, a big business. Worldwide, businesses are struggling to deal with the mountains of data they have at their fingertips and derive actionable insights from that data. Businesses have come to realize that more than just gathering data, it is important to know what to do with that data.

To make use of this data and optimize the business outcomes and to leverage it to meet the business goals, it requires specialized skills and expertise. Businesses do understand that they need data professionals and they are increasingly looking for data scientists to help them make sense of this data. However, there is a huge mismatch between the demand and supply.

According to Gartner, in the USA alone, there would be a shortage of 100,000 data scientists by 2020

How can this gap be quickly filled? I feel that the role of data scientist has a lot of similarities with the role of business analyst. Let us see if and how business analyst can climb up the ladder and be in demand as data scientists –

Business Analyst Vs Data Scientist

A company relies on its business analysts to gain business insights by interpreting and analyzing data and predicting trends-related aspects which help in making critical business decisions. Business analysts also focus on end-to-end automation to eliminate manual intervention and optimizing business process flows which can increase the productivity and turnaround time for an efficient and successful end result. They also recommend systems changes needed to optimize an organization’s overall execution.

Data scientists, on the other hand, specialize and purely rely on data which is further broken down to simpler facts and figures by using tools such as statistical calculations, big data technology, and subject matter expertise. They use data comparison algorithms and methodologies to identify and determine potential competitors or resolve day-to-day business issues.

Business analysts often work on preconceived notions or judgments related to the factors that help drive the businesses. Data scientists, whereas; have had an edge over business analysts, as they leverage data related algorithms which provide accuracy and also use mathematical, statistical, and fact-based predictions.

As organizations are proactively defining new initiatives and campaigns to evaluate the existing strategy on how big data can help to transform their businesses, the role of business analyst is slowly but certainly widening into a major role.

Learning The Tricks of the Trade

Business analysts have some definite advantages if they decide to become data scientists. Business analysts often have domain expertise and industry knowledge which is extremely useful for data analysis. They are, in their role, familiar with data analysis. Apart from this, they also have the ability to translate complex information into a more understandable form.

In order to start the transition from a business analyst role, the first step is getting well versed with technology and programming. For instance – starting from the basic understanding of the Structured Query Language (SQL) and later moving to more advanced big data technologies like NoSQL, MPP databases, and Hadoop. The next logical step is gaining knowledge in algorithms such as recommendation engines, K Means Clustering, Linear and Logistic regression, Time series analysis, text analysis, decision trees, and NLP.

In order to effectively implement big data techniques, there are a variety of tools such as Pentaho business analytics, Talend Open studio, Tableau desktop and server, Mahout, and Splunk, to name a few. A mastery over these tools will definitely provide a cutting edge when it comes to building the skills sets for a data scientist role.

Apart from the technical skills, data scientists need to be expert at math and statistics. So it is a good idea to learn statistics or brush up the knowledge. It is also important to understand machine learning – what it means, how it works and the real world applications of that.

In my opinion, the huge demand for data scientists is a boon for business analysts. The role of a data scientist can be a natural transition if business analysts start to delve deeper into the data and bridge the data relationship across several systems within an organization.

The traditional business analyst relies on mere experience and know-how of the business whereas; data-driven decisions are proving to be more accurate and precise. Business analysts have a scope to ride the wave of the big data transformation and stay relevant. Besides, the use of analytic tools has made it simpler for business analysts to perform the duties of a data scientist.

In any case, organizations are now on the lookout for business analysts equipped with the intelligence of knowing the right analytic tools, big data technology, and machine learning rather than simply relying on business analysts to predict the future of a business.  So if you are a business analyst then you have a lot to learn to stay relevant but the good news is, there are various data science programs which can help you retool to stay competitive.

At Inteliment, we are actively looking for Data Scientists and Business Analysts with a data science mindset. If you think you fit the bill – drop me a message.