I often use the term “analytics” to refer to a broad set of capabilities, deliberately broader than business intelligence. In this Perspective, I’d like to share what decision-makers should consider as they evaluate the range of analytics requirements for their organization.
The analytics continuum comprises the many types of analyses organizations perform, including reporting, visualization, planning, real-time processes, natural language processing, artificial intelligence and machine learning. Our recent Analytics and Data Benchmark Research shows that reports and dashboards are still the most common type of analytics deployed today. This research also shows that, over time, organizations expect to be using AI/ML and natural language processing nearly as frequently as reports and dashboards.
One of the most common benefits reported by participants in our research is that analytics improves communication and knowledge sharing within an organization. That said, today’s organizations must be able to do much more than just share knowledge, but also act on the information that is gathered and analyzed.
Sometimes, acting on information is a matter of employing real-time analyses to respond in the moment, before an opportunity is lost. Both business-to-business and business-to-consumer customers have come to expect real-time responsiveness in their customer experiences. More than one-fifth (22%) of organizations are analyzing data in real time, and more than one-half (56%) are using or evaluating event streaming technologies. Analyzing data in real time, though, requires a different approach than analyzing historical data. As I’ve written previously, visualization may be useful in some situations, but it is not a very effective technique for real-time analyses that require automation to take the required action and generate a timely response.
The automation required in many real-time scenarios depends on the use of AI/ML models to recommend the appropriate response based on information streaming into the organization. Our research shows that nearly 4 in 10 (38%) organizations are automating responses to event data. Many use cases can benefit from AI/ML analyses to determine the best action to take. Today’s powerful computing infrastructures allow for analysis of huge amounts of data using sophisticated algorithms, processing faster than ever before. These techniques help organizations predict behaviors or outcomes and use that information to automatically respond – for instance, with the best offer or recommendation given the current situation.
Even when responses are not automated, AI/ML-assisted processes can help identify correlations that might not be discovered otherwise, such as finding a process that results in better customer segmentation for sales and marketing activities. AI/ML also drives the use of natural language processing, which makes analytics accessible to a wider audience within the organization. Beyond that, AI/ML enables the personalization that extends the analytics continuum, not just by type of analysis or type of data, but also by the role and habits of the individual. Across all of these different use cases, we assert that by 2025, 9 in ten analytics processes will be enhanced by artificial intelligence and machine learning to streamline operations and increase the value that can be derived from data.
Still other actions might involve thorough evaluation of alternatives using driver-based planning capabilities in order to project and then compare the outcomes in different scenarios. While planning and predicting are both forward looking, they are not the same thing as I described in this Perspective. AI/ML gets more attention, but I believe that planning is just as important. Organizations can’t fully evaluate different actions without modeling the outcomes of those actions. My fear is that many organizations are still relying on spreadsheets for this part of the analytics process.
In the end, the value of analytics is realized not from the analysis, but from the actions of the organization. To maximize value, the analytics continuum needs to connect back to business applications, to be connected to and orchestrated with operational applications in order to capture and implement the results of the analyses. I’m encouraged to see more analytics vendors beginning to address this need in their platforms.
“Analytics” is a loaded term. It has many meanings and many components. Hopefully this explanation of the term will help you understand the breadth of analytics available in the market today and help you match those capabilities to the requirements of your organization.