Breaking The Habit

From Charles Duhigg’s The Power of Habit we can draw some parallels to a frequent problem when dealing with Data Quality issues, and what we can learn from the science of habit formation. In the bottom line, the success of any Data Quality initiative goes down to changing or developing new institutional habits.

According to Duhigg, most of our choices are based on habits, and this is true for organizations to. But Duhigg also points out that habits can be changed and replaced by new habits.

The habit cycle can be described as a three-part loop:

• There must be a trigger or a cue, or the event that occurs and prompts the action.
• Then there is a routine, or the actions taken when the trigger occurs.
• Finally, there is a reward that happens because of the actions taken. In fact, it all comes down to the reward. It’s the reward that makes the habit.

All this also works for organizations and Duhigg identifies three triggers to create new habits:

Crises as opportunities

Moments of disruption are good chances to break old habits and introduce new ones. In those moments, people become both increasingly flexible and willing to rethink situations. There’s no need for big crises to make this change, sometimes with just some awareness of reality can obtained, for instance highlighting the true status of the quality of the data can trigger this sense of emergency.

Leadership is the role model

Habits are unique to each business and are based upon the leadership that drives company culture. Companies start their transformations from the top, so top management shouldn’t only be on top of this change, they should be in front of these changes, modelling the new habits and holding each other’s accountable for its success.

Make an emotional case for change

As stated, before habits rely on intrinsic or emotional rewards, and usually change processes are founded only on a rational case, and never call to the employee’s emotional core and yet this is the motor for real transformation.

Looking at this we can easily map some of the things commonly considered as critical success factors for Data Quality initiatives, such as strong sponsorship, management commitment, strategic alignment, or staff training, as the instruments to create a Data Quality culture based on new organizational habits.