What Does a Data Scientist Do That a Traditional Data Analytics Team Can't?

Kumaran PonnambalamIn this special guest feature, Kumaran Ponnambalam of Transera provides his views on the distinction between traditional data analysts and the newer designation of data scientist. Kumaran is Director of Data Science and Analytics at Transera where he is in charge of the company’s customer interaction advisor projects and leads the internal team of highly skilled data scientists. He is a seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications, and specializing in leading Big Data Science and Engineering efforts.

The collection and analysis of customer data is nothing new to the business world. Organizations large and small are utilizing Big Data collected from website visits, surveys, social media and a myriad of other sources in order to discover useful insights that they can apply to their business strategies to create more effective sales and marketing processes, greater operational efficiency and improved customer service.

Many companies rely on teams of data analysts to uncover actionable trends or sometimes, red flags, within their customer data, but increasingly so, organizations are realizing that transferring the knowledge gleaned from this information into an executable business strategy also requires a data scientist. For the layman, these two terms may seem like synonyms; especially given that both ultimately involve comparing and analyzing Big Data —  yet there are a few key features that distinguish their individual knowledge and skill sets, and which demonstrate the importance of data scientists to the overall business strategy.

Descriptive vs. Predictive Analytics

Perhaps the most notable distinction between data analytics teams and scientists is the type of analysis that they use in their work. On the one hand, data analysts use descriptive and exploratory methods of analysis, which involve the interpretation of data to reveal performance results and discover patterns that can be linked to trends and issues within the collected information. Typically, analysts will tend to focus on current and past data, which is then used to generate performance reports or to pinpoint solutions to the problems uncovered within the data.

Data scientists, on the other hand, channel predictive and prescriptive methods of analysis to predict emerging trends and provide a recommended set of actions aimed to optimize business results. Rather than recording and reporting, they are tasked with understanding what has happened, why, and what it suggests about what may happen in the future so that the company can be better prepared to maximize revenue and respond to customer demands. As one can imagine, making these predictions also requires a level of inference not seen by analysts in their more direct interpretation of data.

Structured vs. Unstructured Data

Just as their roles require different approaches towards analysis, the specific work performed by data scientists and analytics teams dictates that the two groups also use different forms of data. Data analysts, for instance, traditionally rely on structured or “clean” data in their analysis, which takes the shape of more numerical information like from website visits, customer satisfaction ratings, and other metrics from other measurable sources. Even though there can often be a great deal of data for analytics teams to sort through, it has the benefit of being comparatively easy to compile, store, and organize into neat data sets for measurement.

Unstructured or “dirty” data is in many ways the opposite of its more organized counterpart, and is what data scientists rely on for their analysis. Data of this type is made up of qualitative rather than quantitative information — descriptive words instead of measurable numbers —  and comes from more obscure sources such as emails, sentiment expressed in blogs or engagement across social media. Processing this information also involves the use of probability and statistical algorithms to translate what is learned into advanced applications for machine learning or even artificial intelligence, and these skills are often well beyond those of the average data analyst.

While a data analytics team may be essential to properly understand a company’s performance with regards to customer service or sales issues, few teams have the knowledge or expertise to translate these findings into a model to drive future business strategies. To truly get the most benefit from Big Data, organizations also need a data scientist who can collectively examine all of the available information, apply this data to highly sophisticated algorithms and ultimately determine which actions for the company to take. Together, data scientists and analysts work side by side to understand the company’s data and maximize business outcomes.

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Source: What Does a Data Scientist Do That a Traditional Data Analytics Team Can't?

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