Actionable Intelligence

Dashboard versus Cockpit
A dashboard shows the status and rate of change of metrics.
A cockpit allows the business leader to input what-if scenarios on top of the dashboard metrics and see how various levers can impact future results.

I tasked my entire team with gathering comments, feedback, and information from various parts of the organization. I put up a thermometer in the office, like the ones you see in front of fund-raising organizations. It was big, and at the top it had a goal of $1 million. I said to the team in one of our meetings, “Everyone needs to bring in the monetized benefits of intelligence.”

If you look 100 times for correlations between two variables, you risk finding, purely by chance, about five bogus correlations that appear statistically significant—even though there is no actual meaningful connection between the variables. Absent careful supervision, the magnitudes of big data can greatly amplify such errors.

The challenge now is: How can we make people trust the data again?
The simple answer would be: Create a data foundation for the data in the company that is complete and accurate at all times.

Notes Donald Farmer, VP, product management at QlikView: “People don't trust data. People trust other people and their opinion of the data.”
It is easy to understand why. In a large corporation with more than 600 systems, lots of data, and no data quality dashboards, the strongest opinions of a few people reign supreme.

What companies need to do instead is create a data dictionary that maps data names and definitions throughout the enterprise, and they need to validate the information. Documentation and validation are the solid rock foundation needed for actionable intelligence.

You simply cannot answer every important question. There will be a limit to what you can and cannot do, especially in terms of budget, manpower, and expertise.”
Question to Consider: What opportunities could you be seizing if you had forward-looking actionable intelligence capabilities right now?

I asked him how long it would normally take him to find this information, and he answered that it would normally take three to four days—and that's if he had all of the correct information.

During business discovery you should be asking questions like:
Do I have the critical business information I need to make decisions?
Are the data management teams maintaining the data well?
To review historical versus present and future, is my data aligned between my old systems and any new systems?
Are my people trained and capable of using the intelligence to make better decisions?
Is there a feedback loop that allows us to show we are using intelligence the right way?

Competing on Analytics: The New Science of Winning.