The Data Scientist isn’t a new concept. The science of analyzing data has been around for a long time. For years, companies have been utilizing a data science team, consisting of a group of statisticians, technologists and business subject matter experts, to collectively solve problems and provide solutions. However, previously buried in the back room of the IT department, data scientists are now the hot ticket in the business world, with Indeed.com reporting that the growth rate for this profession has reached more than 4,000 percent. Data scientists can thank the paradigm shift to big data, which has made this role an imperative in today’s corporate organization. The demand for these individuals, who possess a deep understanding of advanced mathematics, system engineering, data engineering and domain expertise, has never been higher.
While it’s a flourishing time to be a data scientist, I predict business analysts will likely take the first hit and be forced to either adapt their skills or be left behind. A shift is occurring in which companies are no longer using business analysts to determine what the future of a business looks like – instead, they are turning to data scientists to use machine learning and data mining techniques to discover new product trends and patterns of customer behavior that create a more accurate picture of where various aspects of the business is going.
By human nature, business analysts often have preconceived notions about the factors that drive a particular business. Unfortunately, in today’s world, this has become a flaw and puts business analysts a step behind data scientists, who are able to leverage machine-learning algorithms to provide statistical-based predictions for real, data-driven and fact-based decision-making, ultimately making automated analytics the wave of the future. Data scientists have the multi-disciplinary skills to step in and do things that are simply out of scope for the business analyst – they know the algorithms of the statistician and the engineering of a database engineer, and have domain/subject matter expertise.
Why the Takeover
The way the business world operated 10 years ago is much different than the way it does today. Previously, the business analyst played a prominent role in the organization by evaluating the overall structure of a business – from sales and marketing to IT and the utilization of the workforce – and providing recommendations on how to improve the efficiency of a certain area of the company. As an example, take a look at what’s changed in the retail industry: In the past, if a brick-and-mortar store wanted to analyze customer behavior, they’d have to patrol the stores, stand at checkout counters, and try to form conclusions based on what they observed. By contrast today, most all buyer activity is done online, and every piece of customer behavior can be captured and analyzed.
Data scientists have the ability to analyze customers every single move. All customer actions are recorded – from every click that leads to the final purchase – and those click patterns allow data scientists to determine buyers versus non-buyers very early in the customer experience, rather than having to wait until the customer leaves ‘empty-handed.’ There is so much behavioral data available now and we know so much more, allowing businesses to react to customers in a completely different way than ever before. It is now possible to capture the behavior patterns of people who abandon shopping carts and people who compulsively buy things when they are about to check out and customize their experience accordingly. Now, targeted marketing/ad placement and product recommendations based on real-time behavior are becoming required competitive techniques. The data scientist even has the ability to understand the behavior patterns of the shopper and assess their experience in a way that a business analyst can’t.
The traditional business analyst relies on their experience and reactionary analysis while interpreting data. Their experience, however, is unscientific having been gained in environments that have been less digitally specific and far more general. The data scientist translates data using algorithms to obtain information that provides precise solutions to specific questions. These data-driven results are far more likely to reveal proactively exactly what one is seeking and actually influence customer behavior in real-time.
Data scientists will have a major impact on the way companies do business and understand their customers, making this role an integral part of shaping and growing the future of a company. Today, it’s the cutting-edge organizations that are utilizing data scientists, but it’s only a matter of time before they are seen within nearly every organization. So if you’re a business analyst, what does this mean? There is much to learn to stay relevant! Dedicated data science programs are developing in colleges across the world to address the data science shortage, educating young minds and helping to retool professionals to stay competitive.
Joe Caserta is the president and CEO of Caserta Concepts, a consulting and technology services firm that specializes in data warehousing, business intelligence and big data analytics.
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