While many companies currently have Business Intelligence (BI) solutions, there is a real need to lean into an entirely new generation of analytics to truly get the value from their data. The advanced data analytics software available to organizations today gives users more control over their usage by leveraging Artificial Intelligence (AI) to maximize the breadth of insight brought to light.

Here’s more on what features advanced analytics brings to the table and what enterprises need to know about getting the most from their data analytics endeavors.

Key Advanced Analytics Features

As Gartner defines, analytics must autonomously (or semi-autonomously) explore data using tools beyond the scope of traditional BI to be considered advanced. So, it’s useful to think of this tech in terms of a collection of cutting-edge features that transcend the limitations of legacy self-service BI.

AI data mining

Data mining involves scouring huge repositories of data, seeking out useful insights hidden within. In other words, it’s like mining for gold in a mountain of rock and dirt. AI algorithms can sift through huge quantities of data more quickly than human analysts can, which helps companies discover potentially useful patterns, relationships and occurrences humans can use to inform decisions. Side note: Rather than making analysts obsolete, AI data mining frees them from one of the more tedious and time-consuming manual aspects of their jobs so they can work on higher-level projects instead.

Machine learning

Machine learning goes hand in hand with AI data mining — allowing algorithms to learn from data findings/results so as to automatically improve relevancy and accuracy over time. This facet of advanced analytics eliminates the need for data scientists to manually train algorithms.

Predictive analytics

The predictive component of advanced analytics utilizes past and present data to understand performance changes over time to forecast what should happen in the future. The speed at which advanced analytics can crunch numbers, provide real-time performance analytics and forecast gives enterprises a better idea of present and future outcomes — particularly when you compare these capabilities with the limitations of the legacy BI outlined above.

 Natural Language Generation (NLG) 

Instead of just getting an alert that product X has gone up by 30%, you’ll get an alert it’s gone up by 30% because a marketing promotion is more effective than usual and seeing 50% more conversion than other campaigns.

Natural language processing (NLP)

In part, NLP allows users to query data by typing or speaking naturally, and ensures data visualization models come back understandable to human users. Not only does this feature make search-based analytics tools easier for people to use up front, but also aids with the interpretation of results. The ability to provide an intuitive search experience, as well as understandable results, plays a major role in motivating people to adopt analytics tools — and in helping them beneficially understand and act upon their findings.
Advanced analytics address the limitations of traditional BI and then some, with features like AI-driven data mining, machine learning, predictive capabilities, interactive graphs, and natural language processing. However, advanced analytics also needs to avoid falling into the same trap that befell BI – AA needs to be accessible and usable by business people, otherwise, the same bottlenecks that broke BI will break AA.