This blog contains categories and posts for situational intelligence and for analytics. Outside of this blog, these terms sometimes are used interchangeably, so I thought it would be worthwhile to describe the relationship between analytics and situational intelligence. Generally speaking, analytics is a component of a situational intelligence solution.
Generically, analytics is a broadly used term that describes a type of computational software. More specifically, the term analytics describes algorithms, models and an entire category of software applications. Specific types of analytics generate outputs and results that range from insightful details about past events to recommended responses to predicted future events.
Because we typically use analytics to obtain a detailed understanding of past events and to predict future events, the output of the analytics must be readily understandable and acted upon. This is one of the reasons why analytics algorithms and models are generally embedded within an application program that makes the output available and actionable to users and to other systems.
Algorithms and models operate on data, so analytics must somehow have access to systems and sources of data (generally in predetermined formats). This is another reason that analytics is embedded within an application program – to seamlessly integrate data access with analytical capabilities.
Analytics can be delivered in several different forms: as native algorithms (e.g., as an R package), as specific models and point solutions, and as a salient component of “intelligence” solutions, such as situational intelligence solutions.
Situational intelligence is the latest generation of intelligence solutions that accesses data from many systems and sources then, depending upon the use case and solution, correlates, analyses and presents the results of analytics in contextually relevant and intuitively actionable ways.
As an example, consider a situational intelligence solution that generates an optimal work schedule based on rules, constraints and changing conditions. While the solution may use stochastic optimization, an analytical method, to generate the optimized output, the complexity of this particular analytical method is hidden from end-users who receive output familiar and actionable to them – schedules and work orders.
Embedding analytics within intuitive and easy–to-use application software removes barriers to use and consumption. Conversely, this approach also extends and operationalizes analytics throughout the organization by delivering information to people that is easily consumed and comprehended (at-a-glance) when and where they need it to drive and affirm decisions and actions. This approach spreads the power of analytics beyond the IT “glass house” and into the hands of the people taking action to achieve organizational goals.
Seamlessly integrating analytics into situational intelligence applications that elegantly handles the data input and output makes analytics accessible to more people to drive more favorable outcomes. This method of embedding analytics is among the best ways to democratize and operationalize analytics, and it also clarifies the relationship between analytics and situational intelligence.