We have progressed a long way around technologies like artificial intelligence, data science, and analytics. With newer innovations coming up in this space, there is a huge surge in the demand for data science job roles. While there are a lot of employable candidates in India who are trainable to fit the changing technological demand, there is a lot of confusion about the right skill required to fit the job. There is often an apprehension that only knowing technical things like models and algorithms makes one a data scientist — but how true is that?
Technical Vs. Business Skills
Before we dig into this, it is important to understand the typical roles a data scientist needs to perform. A data scientist is a person who is at the core of solving problems and understanding business. They are also immensely curious and passionate about data. We recently wrote about how it is also important for data scientists to be storytellers to find meaningful insights from the data, look at the newer trends, and propose the course of action for the next problem.
Data science projects are wholesome responsibilities involving significant contributions from business analysts, data engineers, visualization experts, consultants, project managers, and more. This indicates that the data science job role is much more than just knowing technical know-how. It is not just restricted to people with a technical background but also needs people with a core business understanding.
At a recent event, Sohini Mehta, Global Service Delivery Head of Analytics at Wipro, said that there are so many automated machine learning platforms that most jobs have become easy and quite simple to perform. It no longer needs just technical expertise; a lot of business knowledge also comes into play.
Moreover, the journey to being a data scientist involves having worked as a data analyst, honing pr and skills, dealing with large volumes of data, working with it in real time, executing tactical mousing deals, and more. This experience then transforms into complicated tasks such as analyzing structured and unstructured data, identifying trends and patterns, and making business decisions. A data scientist has data maturity and business understanding gained over the years.
“Every data scientist must place enormous importance on learning business knowledge related to the problem they are solving. Every newly hired data scientist in the organization should avoid building any models for the first several weeks – use that time to develop deep business knowledge and master the “meta-data” – data about data. Putting priority on business knowledge in your initial days at an organization will help your technical skills find a smooth runway to land or take off in the future,” said Ashish Singru, Senior Director & Head, Global Businsof Analytics Center, eBay, in a conversa,tion with AIM.
Why Is It Important For Data Scientists To Have A Business Acumen?
While Python coding, Hadoop, SQL database, Spark, and data visualization are important technical skill sets that data scientists must have, the need for them to have business acumen cannot be overlooked. You need to have a strong understanding of the industry you are working in and deeply know what the business problem is and how to solve it.
Communication is another key requirement for successful long-term data scientists. In addition to running algorithms, data scientists must prepare progress reports, presentations, and interactive dashboards and communicate the findings in a way that stakeholders understand.
Experts agree that good scientists must be business-savvy and inquisitive to understand the problem and identify which data would be more relevant. They should also understand how to analyze business risks, improve processes, and put the best foot forward for the business to run.
“Data Science is less of science and more of art. Results or products designed using advanced analytical techniques will be effective and implementable only when the end objective has been clearly defined. The model will be as efficient and effective to achieve the said objective as the model. The modeler’s understanding of business & and processes processes and challenges. Technical expertise is essential to being a data scientist; however, it only makes the aspiring data scientist a modeler. ‘Business understanding’ enables that modeler to become a complete Data Scientist,” says Saurabh Awasthi, a Data Scientist at Mondelez International.
He further added that for being a data set, it is essential to test the proposed result for on-results implementations. “A fancy and glamorous solution might produce amazing results in Excel or Tableau but can be disastrous in the real world. To ensure that analytics or data science can produce results, that is the CEO’s vision, it is important for a data scientist to be as close to business as possible,” he said.
Who Wins?
Technical and business skills cannot be separated in data science. The ability of data scientists to explain complex algorithms to common people makes them the most talked about job. Therefore, finding a great data scientist is a challenge, as they have to be the best of both worlds. A data scientist should be able to handle data processing, create useful models, intuitively understand business problems, understand the nuances of data and how the model works, and communicate it to the rest of the world.
