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BLOGData, Information, Intelligence

by Randall CraigFiled in: Blog, Make It Happen Tipsheet, Professional DevelopmentTagged as:

How many hours have you spent trolling through the internet, doing “research” on a particular company or industry? Many people spend too much time on research, and put off making a decision or acting on it.

The reason? Most people are averse to risk. We rationalize that with more information, there is a lower risk of failure. But how do we know when we’ve done enough research? Consider the Data-Information-Intelligence model: instead of making a quantum leap between Data Gathering and Action – we break it down into steps:

Data Gathering: Decide what you need to collect, then obtain it from your workplace, your colleagues, the government, the internet, library, or other sources. Don’t waste time in pre-data-gathering activities – such as “internet surfing”. Write down what you’re looking for on a piece of paper, then when you have found the data, move on.  This also applies at a systems level: Think of silos of data, sitting in disparate systems.

Information: As it is collected, organize the Data into logical groupings: Pros/Cons, History/Current status, By divisions, By Country, Exceptions, or whatever seems to make sense. Look to see if there are any patterns to the information that you have collected.  When systems (and databases) are connected, this makes it far easier.

Intelligence: Change from an information organizer to an intelligence analyst: What does the information mean? What are the implications? Are there any trends? If there are any gaping questions that are unanswered, ask if more data will help you make your decision. If yes, then get it; if not, then the task is complete (sort of).


Whenever you need to do research this week, use this model. And make sure that you actually move through intelligence – otherwise you’ll never get to Action.

Intelligence insight:  The use of machine learning and AI can make a significant difference in ferreting out intelligence, but beware of the problem of bias: if the AI is trained on too small a data subset, then any insights produced may be faulty.

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