Bridging Business Analytics and Data Science

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There really shouldn’t be a gap between business analytics and data science. To say that these two fields intersect would be an understatement. However, there are several reasons why these points of intersection are often underappreciated by those in either field, maintaining a gap between the two inherently related specializations. While bridging this yawning gap is not easy, it will allow business owners to access artificial intelligence (AI)-enabled tools for analytics and lead to better business insights. It would also require help from the world’s data scientists, who are currently struggling with the massive amounts of actionable business data produced on a daily basis. Which brings us to our first point.

The world needs more data scientists.

One of the keys to bridging this gap is scrapping the notion that data science is a niche area for a few select people. This notion hasn’t been true for years due to the simple fact that data is now everywhere. Every industry, business, or service with an online presence creates unique new datasets that can be analyzed for practical and actionable business insights.

Apart from the exponentially increasing amount of raw data that can be harvested today, the connectivity afforded by the internet age has also expanded the ways in which data science is taught. Leveraging the still growing global availability of high-speed internet, universities and e-learning academies are partnering with tech companies, such as IBM, to build data science programs across the globe.

Furthermore, the exponential growth of web-based business data has also caused a spike in the demand for different professions within the purview of data science. This is especially true now as the scope for studying the subject has widened and moved away from brick and mortar universities to become a legitimate and highly sought after online degree. Those who study online data science degrees have a career outlook that spans from companies and institutions to government agencies. This includes high-paying roles for actuaries and financial analysts who specialize in calculating business risks, as well as computer systems analysts who specialize in network efficiency and security. Apart from these data science jobs, there have been noted increases in the demand for operation research analysts, management analysts, pricing analysts, and information research analysts.

In short, while it’s true that data science was a niche area around a decade ago, this is no longer the case. This is bittersweet news for the field of data science, as a high demand translates to a massive shortage as well – one that’s overstretching data scientists who are expected to take on more and more business problems. But there’s more than one way to process data analytics. Scientists are just one side of the big data equation, and on the other side are user-friendly AI applications that can solve most business challenges that can create an impact right away.

AI will shape the future of industries.

Dealing with the sheer amount of data is another key step in bringing business analytics and data science together. And today, the most efficient way to do this is through the use of AI-enabled tools. With the Ople.AI Platform, our goal is to bridge the two and start a new breed of business analytics by providing AI to business users as well. 

Currently, AI is expected to mostly build models and produce predictions, leaving the next steps of decision making and investigating insights to data scientists. Today’s AI does not have to stop there, it can now cross that boundary and go into analytics/insights territory. For instance, unsupervised learning, which is a form of machine learning, can reveal the structure of the data, which helps data scientists with the arduous task of organizing and clustering massive amounts of accumulating business data.

For business owners, using AI-enabled analytics tools makes data science much more accessible. Even without internal specialists in their company, business owners with AI tools for analytics can more easily leverage their own unique sets of business data. This isn’t to say that they won’t need data scientists, as AI is not a replacement for data scientists. Rather, in the case of business analytics, AI can clear the lines of communication between data scientists and business owners.

For instance, the combination of AI-enabled learning and data clustering techniques can quickly and efficiently reveal how many of your customers or clients exhibit certain consumer habits. These insights into customer behavior can reveal patterns and details, which can then be used to make more accurate and detailed business decisions. This includes, but isn’t limited to, decisions related to targeted marketing, sales tactics, inventory management, and optimizing the user experience in all your platforms. Today, these AI-enabled tools are increasingly becoming more available, in platforms that are accessible to both data scientists and business owners.

These are the keys to eliminating the business analytics and data science gap. By democratizing the use of AI for data science across the world’s industries, business analytics can become more accessible to those who need it, delivering the true value of AI back to the business in a more practical way.

Article written by Nicola Foster

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