In my previous blog I addressed the need for situational intelligence (SI) as an approach to decision-making that combines insights with relevant context to create the big picture we need to make the best possible decisions with the lowest risk. I concluded that blog by promising to explain why and how various technologies such as data access and fusion, analytics with machine learning, artificial intelligence and visual analytics come together to support situational intelligence.
Situational Intelligence itself is not a technology, nor can you use just one technology to create it. Rather, a situational intelligence approach requires a combination of integrated technologies. The main types of technologies are listed below. Seamlessly integrating these technologies creates actionable insights that are especially applicable for real-time operational decision-making.
- Live connections to data, both at rest and in motion, in a variety of formats and structures (including no structure at all). Access to multiple, disparate sources of data provides the context for new, deeper insights. Connecting directly to data creates great efficiency and savings savings because data access and preparation often consume as much as 80% of the effort of making data-driven decisions.
- Analytics, big data, and streaming foundational technologies (such as Spark, Hadoop, SAP HANA) that are inherently scalable and enable high-performance execution of analytics and processing of large datasets. These foundations are typically distributed and use in-memory processing so that complex software executes and generates answers and insights as quickly as possible. Streaming message brokers such as Kafka and Internet of Things (IoT) platforms are also necessary to manage streams of data that can be passed through streaming analytics for real-time insights and/or to be stored for inclusion in subsequent applications of advanced analytics that derive deeper insights.
- Advanced analytics and streaming analytics that derive insights from the data at rest and data in motion, respectively. Because situations inherently occur at specific times and locations, the ability to correlate spatial and temporal relationships increases the insights that can be derived. Similarly, the ability to correlate entity-to-entity relationships increases the insights by revealing actual and likely ripple effects. Altogether these analytics make it possible to identify the what, when, where, why and how of situations that happened or may happen. In addition, machine learning allows the analytics to adapt to your data and to your use cases.
- Visual analytics is essential to complete the transformation of data into actionable insights. Intuitive renderings of the relevant data and resultant insights derived by analytics helps users comprehend and acted on data at-a-glance. Output from visual analytics included in alerts via email and SMS is a powerful way of notifying people about critical matters and focusing their attention on acute situations and the decisions to be made.
In summary, situational intelligence is an approach that combines data and analytics, including visual analytics, to aid human decision-making. Insights from advanced analytics and streaming analytics are combined with relevant underlying data to create context so that decision-makers have a complete understanding of each situation and make decisions that lead to the best possible outcome.