Value-focused definition of Actionable Intelligence
Actionable intelligence is relevant, timely, accurate, and contextually relevant information that can be acted upon to produce a desired outcome or result, that provides an organization a competitive advantage over its competitors.
Some features of actionable intelligence include:
- Completeness: The information provided must paint the complete picture of the environment and contributing factors, thus allowing for people to make an informed decision. This includes the need for all relevant and related points of data to be contextually present for a given analysis or application.
- Speed & Timeliness: information must be delivered in a timely manner consistently so that it can be used to make informed decisions before the opportunity to act has passed.
- Relevance: The information presented must be relevant to the situation at hand and provide insights that are useful in achieving a specific goal and providing a competitive advantage.
- Accuracy: Actionable intelligence must be accurate and reliable, based on high-quality data sources and rigorous analysis.
- Accessibility: information that has been made easily accessible consistently by authorized users. It involves ensuring that data is available in a timely, secure, and convenient manner, and that users have the necessary tools and permissions to access, retrieve, and manipulate the data as needed.
- Context: Actionable intelligence must be presented in the appropriate context, with a clear understanding of the broader context in which the information is being used.
These features comprise the actionability of the information presented. Actionable intelligence must provide clear recommendations for action, supported by evidence and data, so that decision-makers can act with confidence.
Value-focused definition of Insights
Insights provide new or valuable perspectives from the data analysis process. it is based on information that has the capacity to gain an accurate and deep intuitive understanding of a person or thing.
There is a multitude of types of insights that can be used to garner a better view point of an outcome or position. However, we’re going to dive into the four that I’ve found most commonly used:
- Descriptive insights focus on past performance and analyzes current status. It provides a snapshot of current conditions. As an example, a descriptive insight might be that sales of a particular product have increased over the past year.
- Diagnostic insights provide genesis information and describes why things happened. Understanding where and how an opportunity came to be, can be help in better understanding next steps. this type involves identifying patterns or trends in the data and exploring the underlying causes of those patterns. As an example, a diagnostic insight might be that sales of a particular product have increased over the past year because of a successful marketing campaign.
- Predictive insights focus on the future state and the forward looking analysis. This type is great for generating predictions. This type of insight is generally an output of things like propensity modeling projections and forecasting. As an example, a predictive insight might be that sales of a particular product will continue to increase over the next year based on current trends.
- Prescriptive insights center around providing recommendations for what actions to take in order to achieve a desired outcome. As an example, a prescriptive insight might be to invest in a particular marketing strategy in order to increase sales of a specific product.
Both insights and intelligence are usually developed at the upper layers of the OLAP systems and processes. They are usually expressed in some form of a report or dashboard and based on the data layers directly underneath them. in the generic logical design below I have outlined in green the area where the data products that are the foundation for intelligence and insights are usually stored.
