A proactive intelligence system provides effective lead times to emerging trends. It will fit new information and incidents into wider outlooks, reducing the frequency of surprise for decision makers.
A reactive intelligence system develops when analysts wait for trends to emerge out of reporting. The analysis can be good, but it takes time to see new threads, and it can be difficult to put new information into context, especially when the pressure is on. Additionally, information gathered as part of reactive systems (regardless of automated or person driven methods) can be largely irrelevant to informing decisions. Analysts react to the few gold nuggets that turn up, and the bulk of reporting goes unused. This culture often develops in large intelligence organisations, as well as small ones that are newly established.
A reactive intelligence system can still have sparks of individual brilliance – smart analysts and collectors can navigate crises – but surprises happen more frequently, and the reaction time for an organisation to act is reduced.
How do you transform a reactive system to a proactive one?
There are four building blocks to a proactive intelligence system.
Firstly, scenario generation. Having an overall picture of the analytical subject and understanding where it is heading over the medium to long term provides a framework to put new information and inevitable incidents into context. It also allows for the development of indicators that can be used to drive information gathering, rather than being reactive to information coming in.
The second key building block is a collection and requirements management (CRM) function. CRM is the link between analytical and information gathering teams. Reactive intelligence systems primarily result from disconnected or ad hoc relationships between these two groups (with information gathering coming in many different forms itself). Differing management lines is often the excuse for this shortfall. But different reporting lines is not the same as a prohibition on communication. CRM is the formal combination of processes, tools, and people. CRM processes outline a regular set of accountabilities covering who is responsible for developing the questions to be answered, who will gather the information, how it will be communicated, and how frequently the plan will be reviewed and refreshed. CRM does not need bespoke technical solutions, basic commercial project management tools or even Microsoft Excel and Word can perform the necessary tasks (even with basic automation).
Third is a thorough understanding of the goals of the organisation. The quality of an intelligence product rests as much on the product being relevant to policy and decision making as it does the analytical findings. Understanding is not regurgitating organisational goals, but decoding how they translate into action, and how the intelligence team uniquely supports the achievement of those goals alongside other work teams.
Finally, intelligence leadership. Professional intelligence leaders translate organisational goals into easily understood priorities for their team. They co-ordinate laterally across teams to ensure there are strong formal links with the other components of the system. And they ensure their team produces situational understanding and far-reaching predictions that drive information gathering. Intelligence is one of the most complex leadership environments, but as put best by US business and political figure, Erskine Bowles, “leadership is the key to 99 percent of all successful efforts.”
Reactive intelligence systems react to their environment, with information gathering occurring detached from analysis. Surprises occur for decision makers because analysis identifies trends in reaction to reporting. Crises will still happen with the most efficient intelligence system. But a well led proactive intelligence system, based on scenario generation to drive information gathering, and coordinated through CRM, will reduce the frequency of surprises and provide greater relevance to decision makers.
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