Treating data as assets

Data can be built into products and services, using more and better data improves business decisions, and data is the input for game-changing opportunities such as artificial intelligence. Thanks to social media and the Internet of Things, the sheer volume and variety of available data is growing by leaps and bounds. 

Still, most people find the concept 'manage data assets' slippery. It sounds like a good idea (and it is!), but what does it really mean? And what must companies and managers do differently? My goal in this article is to provide a simple answer to the first question and a partial answer to the second, focusing on the steps that companies and all managers can, and should, take in the next few months. 

To answer the first question, it is useful to study how companies manage things they have traditionally viewed as assets and to transfer those insights to data. Companies manage physical assets, financial instruments, and (to a lesser degree) people, as assets. And one observes the following: 

• Companies take care of these things: They maintain plant and equipment, they lock up the petty cash, and they invest in their people. 
• They put these things to work: They use their physical assets and employ their people to make products they can sell at profit. Public sector organizations put their assets to work to advance their missions. 
• They advance management systems best suited to those things. This area is a bit more involved. For now, I will simply note that physical plant and people are different sorts of assets and companies adopt different styles of management accordingly. 

I propose that something qualifies as an asset if those responsible meet three criteria: 

1. They take care of it, 
2. They put it to work, and 
3. They manage it appropriately. 

This approach is simple and powerful. These criteria apply to companies, government agencies, non-profits, and people. 

It is easy enough to adapt these criteria to data. Readers may also wish to evaluate whether their organizations meet them. For data, taking care is largely about quality and security. Specifically, companies must invest to: 

a. Ensure they have the data needed to conduct operations, manage the business, and plan their futures. In particular, they must make sure these data are “fit for use.”
b. Keep their data secure from the prying eyes of others. 

It appears to me that many companies do a reasonable job securing their data. Quality is another matter - most data simply do not meet basic quality standards. This poor quality is extremely costly: An IBM estimate puts the tab at USD 3.1 trillion /per year in the United States. A typical company’s share is an astonishing 15 to 25 percent of revenue. 

There are many ways to put data to work. The spirit of this criterion is that a company has explicitly thought through its options, developed a plan, and is working that plan. But most companies readily admit they do not derive a fraction of the value their data offer. 

Finally, data have many properties unlike other assets. Perhaps the most tantalizing is that data can be copied and shared at very low cost - a virtually unlimited number of people can use the same data for many and varied purposes. You simply cannot share a physical asset, dollar, or an employee in the same way. This property illustrates data’s enormous potential! But organizational silos and technical issues get in the way and most data are not shared. This exemplifies how most companies fail the manage data appropriately criterion. 

Today, most companies fail all three criteria - after all, these criteria are deliberately tough and the notion that data are assets is relatively new. So, companies and all managers must take near-term steps that will both help them gain experience and demonstrate the enormous benefits that managing data professionally and pro-actively bring. 

For companies, the first step is to get their data teams out of their information technology departments. There is a natural tendency to reason, “data are in the computer, therefore they must be tech’s responsibility.” By this logic, since people work in buildings, they should be managed by Facilities Management. Data and information technologies are different sorts of assets that require different management systems. When managed by IT, data are given second-class treatment, exactly counter to their desired status as assets. More than any single factor, improperly assigned responsibilities hold companies back when it comes to treating data as an asset. 

If data are assets on par with capital and people, it stands to reason that the “Top Data Job” will be on the same level as the Chief Financial Officer and Head of Human Resources. This won’t happen for some time, so for now companies should take the first step by finding a better spot for their data teams. 

In the rest of this article, I turn my attention to individual managers. They have outsize roles to play, though the vast majority do not give data much thought. This is unfortunate because one of the fascinating properties of data is that they are also 'meta-assets', informing the maintenance of physical equipment, the deployment of capital, and programs to increase employee satisfaction. No one can do his or her job without data and for this reason alone, people, at every level, in every department are well-advised to treat data as assets. 

Where to begin? I recommend three first steps, all of which can be completed in four to six months. First, pick a small set of data for your initial focus. Managers complain about a veritable tsunami of data, coming at them from all over. But the practical reality is that most data is never used for anything. Only a small fraction are “absolutely essential,” while some qualify as “pretty important,” and more as “nice to have.” 

To narrow your focus, pick a small number of your most important tasks and then consider the data you need to do that work. For example, if your job involves maintaining solar arrays, consider the data needed to do that job well. 

The next step is to baseline the quality of the selected data. One good way to do so is using the Friday Afternoon Measurement (FAM), so called because it is simple enough to conduct on a Friday afternoon. To do so, assemble 10-15 critical data attributes for the most recent 100 instances of the data selected above - essentially 100 data records. To maintain solar arrays, such attributes may include: PV module type, nominal power, peak power according to the flash test, and module temperature. Then, with a small team, work through each record, marking obvious errors. Lastly, count up the total of error-free records. This number, which can range from 0 to 100, represents the percent of completely correct data, the data quality (DQ) Score. 

Most managers, quite naturally, expect their data to be pretty good and FAM provides a real shocker! In the most comprehensive study of actual data quality levels, the average score was DQ = 53% and many scores were lower. Only 3% met basic quality standards. A wake-up call if there ever was one! 

The third step is to make an improvement. FAM also provides error rates for each attribute. One usually finds that two or three attributes account for 80% of the errors (the Pareto principle in action). Pick one of those attributes, find out where that data is created, find the root cause, and eliminate it. 

Plenty of managers have taken these steps and made big improvements. In so doing, they have begun to improve their team’s performance and position themselves for far brighter futures! Even better, these simple exercises (narrowing the focus, FAM, and improvement) provide a tantalizing glimpse of the enormous benefits that are there for those who implement them across entire departments, business units and the enterprise. 

About the author

Thomas C. Redman Ph.D. “the Data Doc,” President of Data Quality Solutions, helps start-ups and multinationals; senior executives, Chief Data Officers, and leaders buried deep in their organizations, chart their courses to data-driven futures, with special emphasis on quality and analytics. Tom’s most important article is “Data’s Credibility Problem” (Harvard Business Review, December 2013) He has a Ph.D. in Statistics and two patents.

Text ©2018 Thomas C. Redman, Ph.D. Used with permission.