Analytics And Data Science Are Converging

For years, analytics and data science had always been siloed and existed apart from one another. Analysts mined insights from their organization’s data, while data scientists leveraged data to build algorithms, artificial intelligence and machine learning models. Analysts were concerned with broader business challenges, while data scientists focused on solving particular problems. An analyst generally didn’t require a highly technical background, while data scientists needed years of training and expertise.

However, things are changing, and the two disciplines are becoming one.

Analysts and data scientists share the same overarching goal: discovering new insights to help grow the business. Enterprises want more value from their data. By working closely together, analysts and data scientists can move more swiftly toward that goal.

By fusing analytics and data science together, enterprises gain a more comprehensive perspective of their organization. After all, gaining insights from data isn’t just about looking deeply at a single dataset but being able to incorporate various perspectives from many datasets. Consider a utility engaged in vegetation management. They would need to look at historical data showing where wildfires most likely are and model geospatial data to find where tree mortality is most severe. The former would have traditionally been done by analysts, while the latter would require data scientists. Now, the two can collaborate to solve the problem.

Collaboration is a huge part of this equation. It allows companies to drive new synergies. Analysts are typically engaged in BI efforts—trying to improve general business results—while data scientists typically are engaged in deeper, more specialized projects, like building AI and machine learning models. When analysts and data scientists combine their workflows, they can avoid duplicate work and benefit from one another’s learnings.

Sometimes, you need to query a traditional database for insights to answer a question, and sometimes, you need to run an AI algorithm against billions of rows of data. Often, what you need involves both and several other workflows.

The rise of rich data visualizations is a crucial component bridging analytics and data science. Visualizations allow analysts and data scientists to experiment and explore data in new ways. (It’s worth noting that GPU- and CPU-accelerated analytics platforms play a critical role in supporting these visualizations and enabling this shift.)

Data visualizations are especially good at bringing data scientists’ complicated observations to life. Visualizations are excellent at depicting and explaining algorithms common in machine learning, such as neural networks and decision trees. This allows users to understand these modules in ways that a written or verbal description could never convey.

Today, organizations are collecting ever-increasing quantities of data, and they’re getting it from a variety of highly distributed sources (i.e., sensors, IoT devices and satellites). With everything these organizations have invested in gathering and storing that data, they expect more value from it. The convergence of analytics and data science is making it easier to deliver that value while enabling enterprises to support advanced new use cases like vegetation management, 5G planning, supply chain logistics and fraud detection.

These developments allow data professionals to collaborate, share discoveries, run powerful algorithms and leverage interactive visualizations. This enables them to uncover hidden insights—the needles in the haystack—that even the best subject matter experts, analysts and data scientists could never have found on their own. Enterprises can solve even the most vexing challenges with better insights and unearth previously invisible opportunities.


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