Written by GoodData Author |
What’s the problem with traditional BI tools? For one, they’re typically unable to make sense of the increasing amounts of data that are being gathered, which slows down business processes and causes companies to lose money on their investment. Instead, companies should be focusing their investment into big data analytics if they want to capitalize on the value that business-critical insights offer. I spoke more about this in a recent interview with Analytics Insights, where I shared my perspective on capabilities for big data analytics today and in the future.
Currently, a key differentiator for the analytics product market is the ability to use visualizations to turn data into a story with real business context. AI is coming into play for visualizations as well, where it helps tackle the unstructured big data that visualizations are unable to describe on their own. Incorporating machine learning or AI also helps close the loop by making predictions or suggesting actions that make it easier for users to make high-impact business decisions. In addition, these platforms are evolving to handle ever-larger volumes of data and deliver insights through a customizable, intuitively designed user interface —while traditional BI tools turn that data into, at best, a series of dashboards and reports.
In the future, we should see the role of AI within analytics expand both with visualizations and into new areas. While AI is already used for reviewing content, one future use case will be for reviewing alternative content sources, like videos and media. This will have interesting implications for sites wanting to monitor the behavior of bots or other bad actors who attempt to evade detection by using text-free posts.
We also touched on the fact that many companies realize the full benefit that analytics can offer. Analytics is often seen as a cost that delivers no—or at least minimal—return on investment. It’s easy to understand why when you consider the experience that traditional BI tools have offered, but big data analytics delivers tangible value by integrating data into each decision and enabling users to make better decisions overall. When personalized, actionable, and relevant insights are presented to the user, it also speeds up the decision process, so companies can react more quickly to threats like fraud or other risks before they become serious problems. Though the returns may not be immediately apparent, a thoughtfully designed analytics platform can save a company millions in the long run. In fact, a recent report by Nucleus Research found that GoodData’s customers received an average of $4 for every $1 spent on the platform—proof that big data analytics is well worth the cost when the insights are contextual and easy-to-understand.
Written by GoodData Author |