In Part I and Part 2 of this series I addressed why situational intelligence is a natural and essential method of decision-making that is especially apropos for real-time business operations. Inherent in my argument is an altruistic belief that people make the best decisions and take the best actions with the information at hand. That is the crux of the matter – the information at hand and how accurate and actionable it is. What information is available to decision makers? Does it contain insights? Is it current? Is it clear or is interpretation and/or further analysis necessary before the information is actionable? Is it reliable? How comprehensive is the it? Correspondingly, how much uncertainty shrouds the information, the decision, the action and the outcome? What are the risks of making a bad decision (including no decision)?
Ideally the answers to the preceding rhetorical questions should all be encouraging. But how can these attributes of data and insights for decision-making be assured, especially when decisions are made by different people, when decisions needed are for unplanned situations, and when timeliness is important? Systematized decision-making aided by technology-generated intelligence is a way to assure that accurate insights are derived from data and actionable by decision makers. As discussed in the preceding blogs (and other blogs too), advanced analytics and visual analytics are essential building blocks for analytics that support operational decision-making. Data must be transformed into insights and intelligence. The insights must also be transformed so they are readily comprehended at-a-glance and are actionable.
Another key consideration is having a broad composition of data for analysis. The more data from relevant sources within the enterprise, from the IoT and from external sources, the more insights can be derived by analytics. Accessing external data enriches intra-enterprise data sources with relevant context that is useful when decision makers require supplemental information, such as when insights brought forward to decision makers is not immediately actionable. In such cases further discovery helps decision makers gain the needed understandings and confidence to make a decision. This is where additional data sources and the corresponding added context facilitates interactive data exploration so that decision makers can make timely and favorable decisions. Sources and types of external data include: weather, traffic, news, spot market prices and social media.
Having live connections to data sources ensures that decisions are made using the most up-to-date data, and also enables interactive exploration of underlying data to deeply understand and resolve complex multifaceted situations. A single system that maintains live connections to data sources yields another benefit – it helps organizations bridge their data silos and unify their data assets.
Here at the end of this blog series, situational intelligence now sounds easy, and somewhat obvious too – connect to relevant data sources, apply analytics, make the resulting insights and underlying data available to decision makers with intuitive visualizations so they can consistently make the best possible decision in any situation. If you use an off-the-shelf solution to implement situational intelligence, getting started is also relatively simple. Decide for yourself. What does your situation require?
If you have experiences, thoughts, opinions on this topic, please comment and share them.