Every organization has something to learn. Every organization has data. There is always something to learn from data. Therefore, every organization has something to learn from its data.

A painful problem? Organizations that DO NOT learn from their data.
A more painful problem? Organizations that DO learn from their data but DO NOT build those insights into strategy.

A most painful problem? Organizations that DO learn from their data and DO build those insights into strategy but DO NOT feed the strategy back into the data structuring, collection, and integration processes.

The idea here is that the collection and creation of data has become central to most managerial, informational, and strategic practices in today’s world. Organizations must understand how each data element is to be used in order to optimize the information and insights gained along the way. Organizations must also know what to do with the insights once those insights have been made. Building them into strategy is critical – as long as they are built into the right strategies. In particular, it is imperative for that information to feed back into the original source of the information: the data structure itself. How can new data be created (and old data be refined) to provide new insights and analyses moving forward?

The process needs to be cyclical. Organizations must turn historically linear processes into innovative cyclical ones. Cyclical processes are self-fueling and renewable, whereas linear processes are expensive and always run out of gas. It is that self-sustaining nature of cycles that enables perpetual growth for individuals, teams, departments, companies, and industries.

So how can strategy feed back into the data structure and collection systems?

• Create new data.
• Refresh old data.
• Determine the value of each data element based on where, how, when, and why it is used.
• Compare internal data to external data sources and data standards.
• Ride the DIS Cycle backwards to see how data can supplement new, desired insights.
• Question your data. Love your data. Hate your data. Ask why it works. Ask why it doesn’t.
• Build quality control and oversight processes to ensure data is used properly.
• Insert data into your everyday workflow. Build a dependency on your data.
• Quantify elements of your marketing, product development, customer support, and managerial strategies.