In analytics, companies are increasingly using artificial intelligence (AI) to guide users through the data maze and give them recommendations for action.
In the digitized age, analytics tools that promise exciting insights into an ever-increasing flood of data are booming. Because there is hardly any lack of data quantity, the new central challenges for these tools are to provide an overview of the constantly growing data stocks. In addition, they should independently gain valuable insights for the user and provide him with intelligent action derivations.
Reach your goal faster with natural speech processing
The intuitive navigation of users through complex data using artificial intelligence has become one of the most popular AI applications in the analytics field. For example, Google Analytics uses the algorithmic processing of natural language. The question of how many products have been removed from the shopping basket in your own online shop within a certain period of time can be easily asked and answered in the Ask Intelligence feature of Google Analytics.
Google’s AI algorithm translates the user’s question into the relevant metrics and dimensions and displays the appropriate result directly. New and irregular users of the tool can transfer their own KPI into fast answers with Google Analytics without major configuration effort.
A better interpretation of data using AI
While this feature always provides descriptive answers to one’s own questions, the automated interpretation of data contexts has become a second major field of application of artificial intelligence in analytics tools.
Using Google Analytics data, the tool Pave-AI not only tries to describe developments but also to interpret their causes. For example, the platform assumes increased marketing efficiency as the number of users decreases and sales increase at the same time.
This shows that fully automatic AI tools have hardly had a chance against their human competitors – the marketing analysts: The tools can hardly go beyond simple plausibility assumptions. The relationships in the data are too complex.
Assisted interpretation using AI algorithms, such as those integrated with the Quick Insights feature in the Microsoft Power BI business intelligence tool, works much more reliably.
For this purpose, user-selected anomalies in certain tables or the entire data set of machine learning algorithms are analyzed in more detail and interpretation suggestions are submitted, which can be immediately evaluated and visualized by the user.
This has the advantage that Microsoft Power BI time-savingly examines patterns and irregularities, some of which are not yet covered by its own reporting. Thus it can offer an added value for the own exploration and interpretation of the data.
Acting when the algorithm recognizes it
AI in today’s analytics practice is mostly an exploratory data analysis operating in the background. At best, it shows critical patterns and thus supports one’s own actions.
A good example of this is the BI service Mix Panel, which uses machine learning algorithms to continuously analyze its own reports and, for example, identify statistical anomalies and translate them into action derivations. In this way, the tool can, for example, detect a drastic change (positive or negative) in newsletter subscriptions at short notice, which goes beyond coincidence or seasonal effects, and send notifications of potential for action.
While most tools provide such alerts, Mixpanel also provides underlying correlation analyses that – as with Microsoft Quick Insight – help to quickly identify the cause of a drastic change.
Used correctly, artificial intelligence in analytics already helps to navigate smarter through one’s own data sets, interpret them more effectively and ultimately act faster when necessary through interaction with the algorithms. However, they cannot yet replace human interpretation of the data.