For many companies, there are lots of data cowboys — and no caravan (unified data strategy) in sight. While the boom of data science knowledge and tools within organizations can be exciting, unruly data practices can hurt businesses in the long run. We share why every company needs clear data rules, plus tips about developing a winning data governance strategy.
It’s a wonderful new world of data insights and sleek projections that would make any board of directors lean back in their armchairs and nod in approval. With a few clicks, your CEO (who once needed one Xanax, three interns and 10 versions of an Excel sheet to check a purchase order) can now build their own color-coded report about the company’s future. And, yup, just as they suspected — the future’s looking peachy.
They’re not the only ones in your organization who are suddenly fluent in data and experimenting with analytics apps to make their teams look amazing. Simple, automated data management tools are unleashing vast analytic potential business leaders couldn’t have imagined even five years ago. We are in the midst of a data gold rush — and cowboys at every organizational level are teaching themselves how to mine data for priceless business nuggets.
But don’t let flashy applications lure you into a false sense of security. Your business intelligence is only as smart as the strategic decisions that inform it. Without clear rules and keen oversight, this wonderful new world can quickly return to the old Wild West of data management.
There was once a time when everyone had a spreadsheet — or even the dreaded SpreadMart — and no one had the right answers to business questions. Wise leaders will avoid the pitfalls of bad business analytics by not simply embracing new tools but by establishing a clear and strategic set of rules to govern them.
Note: If you don’t know what a SpreadMart is (or you’ve suppressed the memory), see the nightmare example below.
This image is an example of SpreadMart insanity. Each employee with 10 versions of the same budget — shifting rules, loose version control, general chaos. Yes, you may see SpreadMart-like practices come back in the form of flexible programs like DataBricks, Python, SQL, and you guessed it, ChatGPT. Still, our use of these programs doesn’t have to be unruly or decentralized like the spreadsheet collections of yesteryear. Reject unruly data practices. Choose data governance strategy.
What is Data Governance?
Data governance is not the same as data management, and every business leader should know the difference. Data governance strategy refers to the decisions determining how a business stores, shares and uses data assets (like customer preferences, purchase patterns, and so on). Ideally, executives lead data governance with guidance from IT professionals in close collaboration with the people and teams that use data systems.
Examples can include shared or centralized:
- Data sources and standards
- Compliance practices
- Systems for entering data
- Policies for data sharing
Data management happens when the professionals who design and maintain data systems implement those strategies. For a long time, business leaders have relied heavily on IT teams (data managers) to set their company’s data standards. These leaders may have incorrectly believed data governance strategy was beyond their understanding or control.
Bad Data Hurts Good Companies
Neither business leaders nor IT professionals should blindly depend on one another. Why? Because each field brings a wealth of knowledge that is unique, important and cannot function properly without the other. When data scientists build systems without executive (and end-user) input — or when executives make assumptions about the quality of their data — even the most advanced tools can become misleading. Likewise, leaders should not blindly trust their teams to handle company data carefully and consistently.
A colleague recently shared a cringeworthy example from their top-ranked healthcare college: During a strategic summit, it became clear that each department had used a different definition of “student” in a pricey, year-long data collection initiative. It would be problematic for any organization to have inconsistent definitions of their customer.
But in the health sciences — with its countless specialties, certifications and academic pathways for “students” to pursue — the error rendered the year’s data almost entirely unusable. To make matters worse, the discovery happened in front of the school’s board and clinical partners, undermining their reputation.
How do your teams define customer, sale, retention and other key terms? How are these terms used in data systems? If you don’t know, ask. Their answers may surprise you. Variability or bias in your teams’ assumptions could introduce flaws to the data systems that shape your decisions and impact your revenue. These are the hidden cracks that cause good businesses to crumble.
How to Protect Your Company’s Future With a Data Governance Strategy
Even if you know nothing (or very little) about data science, you need to play an active role in setting your company’s data standards. How you approach data governance strategy will depend on your company’s type, size, stakeholders and funding. Agile and entrepreneurial operating systems (EOS) are two popular change management models to possibly adopt as you lead.
However, just because a model is popular doesn’t mean it’s right for your company. Think about how the pace, ethos, demands and rewards of these models would resonate with your teams. A third-party vendor specializing in data governance strategy can help you design a tailored approach.
Regardless of process, your teams will need you to:
- Listen: You must establish a data governance strategy with (not for) the people who will have to live with its implications, or it won’t be sustainable. Ask your teams what they need from analytics systems and staff. Offer multiple platforms for people to weigh in — interviews, town halls, department meetings, anonymous surveys. Don’t stop listening once you’ve gathered baseline insights. Partner with stakeholders at every phase of data governance.
- Learn: The field of data science is little more than a decade old. That means everyone, even the experts, is still discovering its promise. It’s okay to be new to this. Read, explore, ask questions. Encourage others to learn too. Set aside funds for employees, including senior leadership, to undergo analytics training. If your leadership team is taught and made responsible for key analytics functions, they will be champions for optimizing and investing in data governance.
- Share: As you gather insights from listening exercises and continuing education, share them. Keep stakeholders informed through routine touchpoints, such as monthly staff meetings and newsletters featuring analytics success stories.
Listening and learning are important, but what do you do with your collected knowledge? Some companies might require a third-party vendor to help turn insights into action plans. Whether you DIY or work with outside help, this webinar offers tips on how to get started.
Just twenty years ago, a degree in data wasn’t a thing. Today, junior executives are graduating with diverse specializations in business intelligence. They speak data natively and won’t be constrained by any one tool your company might use. Give the cowboys a chance. Learn from them. Welcome their product demos. Offer them opportunities to learn from your knowledge and experience in return. Finally, partner with them to ensure your company can reach its full analytic potential.