Data Checkers? Or Data Chess? What's your Data Strategy?

The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking. — Albert Einstein

It has been said the greatest chess players, the grand masters of chess, rarely consider more than three moves or so ahead. As a move in chess can be highly unpredictable, the grand masters tend to play in the moment.

The same can be said for many good checkers players (English draughts), in a game perhaps not as forgiving as chess. The rules for checkers may seem easier than chess, but recovering from a mistake can be harder, if not fatal.

Both chess and checkers require some sort a plan or strategy, answering the question, "What's my next move?"

Many organizations are asking that same question today, especially in the face of volatility and uncertainty.

If assumptions are uncertain. If the uncertainty is large. If the affect is large, then it is an important uncertainty. —Gerben Wierda, Enterprise Architect

One area that can be helpful to organizations planning their next moves is in having a formal data strategy.

Why a formal data strategy?

One good reason to codify a data strategy is to rein in the collection, utilization, and spread of marginal or even useless data and the development of bad data habits, in turn leading to increasingly complex systems, more and more tech tools and "solutions," and higher and higher data access and storage costs.

Every business has a need for speed. One term you might be hearing more of in the future is "technical debt," the cyber and data equivalent of the expression, "If you don't have time to do it right, when will you ever have time to do it over?" A data strategy should help avoid the accumulation of technical debt in the form of data inaccuracies. An inadequate data strategy, says the credit-reporting company, Experian, contributes to bad data.

More organizations are moving to increased automation. If your data isn't complete or lacks consistency, how can any processes be successfully automated or improved? Having a solid data strategy should cut costs or increase return-on-investment (ROI) in efforts to automate or implement machine learning (ML) or data analytics.

Finally, improving the data and information value chain (data chain), through a data strategy for the people who are collecting, using, producing, or analyzing data, will only serve to make their work that much easier.

The end goal of your data strategy is not only making the lives of your workforce easier, but for the organization, to build in a capacity for robustness under uncertainty. Whether we like it or not, we are certainly living in turbulent times.

Lumber is up. Lumber is down. Then it's up again. Inflation is transitory. Inflation is here for a while. The economy is growing. The economy is slowing. These are all macro data points that may sooner or later work their way through your own data collection systems.

Some Data Strategy Considerations

It's about the people.

Most data, about 2/3rds of it is not analyzed. But that is not necessarily a bad thing. According to Lori Sherer, a partner in Bain's Advanced Analytics, "It's not about the data. It's really about the people who have to trust these tools." Look for solutions with the end users in mind, both internally or externally.

Create a "data council"

A data council should be comprised of members from each area of the organization. The data council can both help ensure better data quality and fine-tune broad data governance to ensure the right data is captured and used appropriately.

Don't over do it.

About 90% of the value of data comes from a small percentage of data points, according to Christian J. Ward, Chief Data Officer, EVP at Yext. Perhaps only 20 out of a 1,000 are useful. Ward likes to run and has a Garmin watch to monitor his performance. Partly out of curiosity, Ward was astonished to download 396 pages of data, in 10 point format, from one run. Find the useful data points. That could be people asking questions, or customers wanting to know more. More data will not mean better decision making. Conduct a data management maturity assessment to find out if you have the data you need.

A data strategy is the foundation of a digital strategy

Many organizations, according to data strategy consultant Donna Burbank, not only want to be a data-driven company, but a data company, where data is the product and information can be monetarized. This means a digital transformation to a new business model in which data becomes the business.

What will be your next move? ?■