In our digitally obsessed economy, it’s become all too prevalent for knowledge workers to assume that data problems are best solved with newer and better tech. In fact, the most powerful solutions for maximizing business value are right under the noses of management – in the form of improving data literacy skills. 

In the service of changing the culture around data literacy, academic and consultant Peter Aiken offered an invaluable 12-step program to audiences at last year’s Enterprise Data World Digital conference. Aiken’s recourse to the language of recovery culture may seem merely tongue-in-cheek, but it conveys the reality that data problems are in the end a product of bad habits at the personal, corporate, and even cultural level. 

Step One: Admit You Have a Data Literacy Problem

Just as at the personal level, the first roadblock to implementing meaningful change within an organization lies in denying there’s a problem, and this denial is rooted in issues of responsibility and blame. In a business context, data managers may have difficulty admitting there are enterprise-wide data issues because they feel it reflects poorly on their leadership abilities, which is not necessarily the case. More important than passing the buck, however, is initiating a candid and realistic program for change within your company.

Step Two: Embrace Data for What It Is – Both Good and Bad

Once a data problem has been acknowledged, the next step is to begin doing the necessary assessments for the first articulation of a data strategy. One of Aiken’s most crucial lessons is that problems in data organization must be managed as a strategy, not a project. To return to the recovery analogy, long-term best practices aren’t overhauled once and for all by removing symptoms, but by perpetually revising company-wide habits.

“You will no longer need a data program when you no longer need your HR group or your finance group,” Aiken quipped. “After all, nobody says, ‘I think everybody’s gonna behave, so we don’t need any more lawyers anymore.’”

Envisioning data organization as a never-ending project may require a paradigm shift within many businesses, but to do so will reduce untold costs due to hidden inefficiencies in the system. 

Step Three: Create a Data Manifesto – and Commit to It at a Company Level

Once the systematic problems in data organization have been brought to light, it’s essential to begin to change the way the company behaves by changing how it thinks in clear, jargon-free steps. Creating a company doctrine that clearly states new values can be helpful in this: a current tenet of “following a plan” might be replaced with “responding to change,” for example. To succeed with such corporate sea changes, it’s vital not only for your CEO to openly embrace new paradigms but for the full company board to also be fully committed.

Step Four: Take an Inventory of Your Company’s Data Strengths  

Here, the process shifts away from identifying problems and begins the business of identifying and cataloging the company’s data assets. Setting this in motion may be a revelation for some companies, as the process can both reveal assets that may not be well known enterprise-wide and reframe aspects that may not have been recognized as positives. Some of these, such as individual or departmental talent, may not seem directly data-driven, so Aiken suggests that in compiling an inventory, having the support of a seasoned board can eliminate potential blind spots.

“CEOs may come and go,” said Aiken, “but if you’ve got a board that interacts with other organizations, they are much more likely to have encountered good organizational data practices.”

Again, because data organization is ongoing, this inventory may need to be reassessed at appropriate intervals to consistently unlock value.

Step Five: Look Clearly at Your Past Data Debts ­– and Celebrate Their Demise

The flipside of taking stock of your company’s assets is to create a sort of forensic picture of bad data practices in the past. This part of the recovery process entails making positive affirmations of the ways in which your company has made inroads in decreasing data debt with better practices, though presenting dollar amounts to the savings can be motivational enough.

Again, because an effective program must permeate into the soil of corporate culture, Aiken suggests going beyond positive memos and instead commemorating significant milestones of improvement with rewarding company events.

“I’ve been to many organizations where they will celebrate,” Aiken assured. “Because when you do that, it demonstrates that you’re not doing it anymore. ‘Yes, we used to do things badly this way, and this was the cost of doing them badly.’” 

Step Six: Initiate Deep Cleaning and Replace Bad Data Practices with Good Ones

Often, once the culprits of data debt and general inefficiency have been tracked down, many data managers are tempted to fix the problem by investing in the latest IT innovations. However, this can easily lead to an uncannily similar repetition of the same dysfunction, as technology is only useful when in skilled hands – and fed with clean, reliable data. At this phase, the importance of spreading data literacy down the rungs of the organization becomes vital, said Aiken:

“If you want to assess your capabilities against some objective standards and report on that progress, it requires the organization to be more literate all the way around, and to allow folks to go out and cede various resources by improving knowledge worker data literacy.”

Step Seven: Take Advantage of Increased Worker Literacy by Crowdsourcing

Your company may be filled with experts and seasoned businesspeople, but some problems are challenging in novel ways that are best tackled by teams, not individuals – and this mindset may go against the grain of business culture.

“Most organizations have been saying, ‘Data is everybody’s responsibility,’” joked Aiken. “Well, it hasn’t worked out so well, has it? ‘Everybody’s responsibility’ usually ends up meaning it’s somebody else’s responsibility!”

To truly make data a collective endeavor, encourage team-based solutions through facilitating regularly scheduled data meetups. This will help build a synergetic focus on shared goals so that the most passionate of knowledge workers can access the best the company has to offer. “It’s very much a team sport,” said Aiken.

Step Eight: Make a List of Data Beneficiaries

Moving the program increasingly along from a “me” to an “us” trajectory, this step is a critical element in improving data stewardship, in which you and your team inventory who will best benefit from your data. At the same time, you should be tracking the life cycles of your data to document its sourcing and subsequent destinations to cement the business case for subsequent improvements.

Step Nine: Make Direct Amends by Remediating Past Mistakes

Demonstrate to your team the ways in which increased data organization has already increased self-funding to sell long-term data literacy as a solid return on investment. There are always myriad items of potential value waiting to be maximized through cleansing, combining, and otherwise optimizing, and executing such remediations again and again over time will not only win the confidence of the company but will continue to hone your data team’s skill at doing so.

Step 10: Increase Personal Accountability Through Teaching Better Data Practices

All too often, data-driven companies turn to new tech as a matter of reflex rather than seeking solutions among the skills of existing staff – human resources, in the most profound sense.

“Most of our knowledge workers have been underserved by the academic community by teaching you that the only answer to every data problem is a brand-new relational database,” said Aiken. “If it’s the only thing we teach them, why would they expect to learn anything else?”

Instead of purchasing new platforms, upgrades, and devices, data managers can save untold resources by shifting the culture towards practices and assessments with an understanding that data is a programmatic activity that can outlast the lifespan of trendy tech. 

Step 11: Improve Organizational Capabilities Continuously

Once these previous steps begin to be metabolized and reinforced at the individual level, you should expand the reach of the best of these practices by focusing on community-based efforts that will engage each and every knowledge worker through ongoing reinforcements. By institutionalizing a self-renewing data strategy cycle in this way, your company will become more reliant on the “data ears” of its workers rather than any individual app or program.  

Step 12: Evangelize

Finally, once you’ve personally witnessed the power of increasing data literacy at every level of your organization, begin spreading the word beyond your company by helping other people understand the degree to which data illiteracy contributes to pernicious data debt. Multiple obstacles exist within knowledge worker culture, from a dearth of leaders in the data literacy culture to resistance within the workforce itself, but when you can point to significant gains in corporate goals and bottom lines, you change the culture one knowledge worker at a time.

Want to learn more about DATAVERSITY’s upcoming events? Check out our current lineup of online and face-to-face conferences here.

Here is the video of the Enterprise Data World presentation:

Image used under license from Shutterstock.com

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