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Data Culture

How To Build a Data Culture: What Leaders Need To Know

If institutional knowledge and gut feel guide your decisions, your company risks getting left behind. Here’s what to do instead.

woman looking at a board with graphs and tables: data culture
Learn how to overcome analysis paralysis and make smarter decisions faster. [Westend61/Getty Images]

Your company accumulates mass quantities of data every day. You collect information from marketing, sales, service, and more. There are mission-critical insights locked inside. The question is, what trends are you missing? Can you surface them to make smarter, more strategic decisions? And if so, how?

Building a Data Culture is the only way to unearth these buried insights. That’s a formidable task for the 83% of CEOs who want their organizations to be more data-driven. Yet companies that embrace this approach to decision-making are more successful. In fact, leading data-driven companies to reallocate talent and capital four times faster than their peers. And, the 58% of companies that make decisions based on data are more likely to beat revenue targets than those that don’t.

So, what’s next for leaders ready to build a Data Culture? See how CEOs can overcome challenges and empower their employees to make smarter decisions faster.

CEOs face countless decisions about where to start when building a Data Culture. Overcome analysis paralysis by starting small with a use case that proves the value of your new Data Culture. Promote the payoff with skeptics: McKinsey research shows data-driven companies accomplish goals faster and that their initiatives contribute at least 20% to earnings before income taxes.

Here’s why this works:

Data analysis surfaces patterns that >unearth value and enable companies to take advantage of market opportunities faster. That drives growth, nurtures innovation, and strengthens differentiation from competitors.

Companies that still rely on institutional knowledge and gut feelings to guide decision- making are leaving money on the table.

Artificial intelligence and machine learning take the guesswork out of decision-making

Companies that still rely on institutional knowledge and gut feelings to guide decision-making are leaving money on the table. With artificial intelligence and machine learning, employees make the right decisions quickly and confidently.

Strategic work keeps employees engaged

When data analyses guide routine decisions, employees spend less time on basic tasks and more time focusing on strategic work. That keeps them engaged and productive. That’s why 84% of data-leading organizations have observed an increase in employee retention.

Empower the right team to score a financial win

The best way to build a community of data champions is to demonstrate how data-driven decision-making grows revenue and streamlines operations. Don’t choose an analytics use case just because it might produce an interesting outcome. Instead, opt for a project that will yield a financial win and that you can later scale for maximum impact.

Here’s how to start:

Step 1: Choose the right team members

Start a working group that includes diverse colleagues from across the organization. These team members should bring a collaborative mindset, differentiated skills and abilities, and distinct organizational perspectives. Make sure you include executives, line managers, data engineers, developers, and machine learning architects.

Step 2: Equip your team with the right training and technology

With accessible technology, you can connect team members and enable them to unlock hidden insights. Don’t assume your team members have the skills or tools to get started on their own. Instead, give them comprehensive training so they can learn how to make data-driven decisions from anywhere (without having to be an actual data scientist).

Step 3: Start small

Test your assumptions on a small scale and iterate. You’ll know you’ve hit a winner when your colleagues can measure the value of your project on their bottom line.

Here’s how this worked at one financial services company. After a simple clustering analysis evaluated subclasses of data across sales territories, right-sizing the coverage led to $1 million in incremental revenue the following year. That win was enough to build enthusiasm for data-driven decision-making across the company.

Nobody can just drop all your data in and the right answer comes out. It’s that human insight that helps you make that jump from that raw data to conclusions.

Mark Nelson, Tableau CEO

Step 4: Prioritize data culture’s human element

Ensure team members review raw data analyses to understand downstream utilization. Only human eyes can determine if bias has influenced the conclusions.

Avoid bias by proxy by not taking data at face value. Consider ZIP codes: at face value, they are nothing more than a location indicator. But when you consider ZIP codes often correspond with race — and lenders and insurers consider ZIP codes in loan applications — human reviewers must step in to ensure decisions are fair and free of bias.

“Nobody can just drop all your data in and the right answer comes out,” said Mark Nelson, Tableau CEO. “It’s that human insight that helps you jump from raw data to conclusions.”

Take the next step to build your data culture

The process of iterating and scaling new data strategies means success won’t happen overnight. However, leading companies encourage experimentation because they believe not being data-driven is the bigger institutional hazard. And inaction is the biggest risk of all.


Candra McRae is a lead solutions engineer at Tableau Software. She is also a results-oriented analytics professional passionate about making an impact and empowering others with data. Candra has a proven track record of effectively leveraging a broad technical skill set, strong business acumen, innovative thinking, and leadership to maximize business performance and solve complex problems.

More by Candra

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