“We can take a rocket to space, make that land back on a platform in the middle of the ocean, but we still can’t say these two spreadsheets match,” Juan Sequeda recalls a speaker at Enterprise Data World saying.

But it doesn’t have to be that way. The technology is already here, and organizations are throwing big money at all kinds of data tools. But to no avail, because they still have a lingering issue: their businesses are not data-driven.

This data problem manifests itself in everyday work everywhere you look. People continue to email spreadsheets. Gut feeling and intuition still reign. The highest paid person’s opinion (HiPPO) prevails. 

Can’t buy your way out

Investing in the right tools is important. They need to enable your team to solve business problems collaboratively to multiply the value of their insights. But you can’t buy your way out of your data problem.

Instead, direct your attention to your people. Who do you email to ask for clarifications when working through projects? How do you verify your data? What you’ll quickly realize is that every team and every person in your organization has a stake in data.

Technology augments data work, but doesn’t replace it

Let’s say you work for a retailer. It’s the end of the quarter. You need to get a deal signed with a major manufacturer in the next 48 hours. They’d be the largest partner your business has ever worked with, promising to lower costs significantly as the business grows. This is a make-or-break deal for your business as it struggles to expand revenue, and your job is on the line if it falls through.

You’re meeting the manufacturer’s Chief Financial Officer in two hours, the last stakeholder to give approval before the contract is signed. A producer of phone cases, they are adamant about only working with products that fulfill at least 100,000 orders per year. It isn’t worth the shipping and administrative costs for them to partner with smaller businesses.

You have data. A central database connects, cleans, and presents your Amazon, Shopify, and website sales data in one place. An internal subject matter expert has assured you that there’s been well over 100,000 orders fulfilled last year.

Since your colleague has given you their word, you don’t look at the numbers ahead of time. You check the numbers a few minutes before the meeting starts because you want to impress your prospect by giving an exact number. But you checked the data, and are mortified to see that you only have 70,859 orders in that time period.

Now that you’re in the meeting, do you tell the CFO you meet the 100,000 threshold or not? Maybe your colleague made a mistake in their calculation. Whatever the case, the most important deal for your team and your career is now in jeopardy.

What went wrong? Technically, the technology worked: it gave you the data you needed. And it was indeed the canonical source of truth. But it didn’t give you the context necessary to understand it. For your colleague, orders meant the number of items sold. For you, orders meant the number of transactions. That’s a huge difference.

This isn’t simply a matter of miscommunication. The data wasn’t clear and you couldn’t trust it. Multiply that feeling across your entire organization, across all the datasets you have. Your data assets failed to deliver value, but it’s not the data’s fault.

The human touch

Machine learning, artificial intelligence, and other technologies cannot yet address subjective interpretations necessary to build the knowledge that your organization needs to function. The earlier retail story shows that you can have all the right technologies, but outcomes can still turn out wrong.

Promoting data literacy means everyone understands data better, and asks the right questions. They can make better business decisions by being well-informed. Data moves from being a difficult asset to get and use in your work, to a central, seamless part of your workflow.

Push more of your teams to become more data-driven. Give them the training and experiences they need, and select technology to augment and enable them. This cultural shift to collective data empowerment will make your organization more resilient to unexpected disruptions.

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