If we only have traditional, non$-$learning computing available to us$,$ we might be able to take a stab by creating a database showing us which customers spend the most money. But what if a new customer appears who spends $$100$ in their first transaction with us$?$ Are they more valuable that a customer who has spent $$10$ a month for the past year? To understand that we need a lot more data, such as the average customer's lifetime value, and perhaps personal data about the customer themselves such as their age, spending habits or income level would also be useful!

Interpreting, understanding, and drawing insights from all of those datasets is a far more complicated task. AI is useful here because it can attempt to interpret all of the data together and come up with predictions about what the potential lifetime value of a customer may be based on everything we know $$ whether or not we understand the connections ourselves. An important element of this is that it doesn't necessarily come up with "right" or "wrong" answers $$ it provides a range of probabilities and then refines its results depending on how accurate those predictions turn out to be$.$

Rich new ways to explore and interpret data

Data visualization is the "final mile" of the analytics process before we take action based on our findings. Traditionally, communication between machines and humans is carried out by visualization, taking the form of graphs, charts, and dashboards that highlight key findings and help us to determine what the data is suggesting needs to be done.

The problem here has been that not all people are great at spotting a potentially valuable insight hidden in a pile of statistics. As it becomes increasingly important that everyone within an organization is empowered to act on data$-$driven insight, new ways of communicating these findings are constantly evolving.

One area where important breakthroughs have been made is the use of human language. Analytics tools that allow us to ask questions of data and to receive answers in clear, human language will greatly increase access to data and improve overall data capabilities in the organization. This field of technology is known as natural language processing $($NLP$).$

Another is new technologies that allow us to get a better visual overview and understanding of data by fully immersing ourselves within it$.$ Extended reality $($XR$)$ a term that includes virtual reality $($VR$)$ and augmented reality $($AR$)$ will clearly be seen to drive innovation here. VR can be used to create new kinds of visualizations that allow us to impart richer meaning from data, while AR can show us directly how the results of data analytics impact the world in real$-$time. For example, a mechanic trying to diagnose a problem with a car may be able to look at the engine wearing AR glasses and be given predictions on what components are likely to be problematic and may need replacing. In the near future, we should expect to see new ways of visualizing or communicating data, widening accessibility to analytics and insights.

Hybrid cloud and the edge

Cloud computing is another technology trend that has had a massive impact on the way Big Data analytics are carried out. The ability to access vast data stores and act on real$-$time information without needing expensive on$-$premises infrastructure has fuelled the boom in apps and startups offering data$-$driven services on$-$demand. But relying entirely on public cloud providers is not the best model for every business, and when you trust your entire data operations to third parties, there are inevitably concerns around security and governance.

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