Data-Driven Companies Perform Better by Almost Every Metric — Here’s How To Become One of Them

Preena Johansen, Trailblazer and Tableau Ambassador, and Wendy Batchelder, Chief Data Officer at Salesforce, joined us at World Tour Sydney to share how successful business leaders are harnessing data to lower costs and boost efficiencies.

Behind the data

Most execs say data is critical to decision-making, but a new global survey of 10,000 business leaders reveals a different story.

• Sixty-nine percent of leaders are not using data for important decisions like pricing
• Only 27% of them use data to inform strategy when entering new markets
• Seventy-nine percent don’t use data to inform their diversity and inclusion policies
• Only 22% use data to help guide their climate targets.

The case for change — why creating a data culture is essential

A data culture can help businesses tackle complex challenges and put everyone in a position to ask the most helpful questions and uncover the most useful responses. 

A data culture eliminates guesswork and ensures you can tap into mission-critical insights that will help you identify trends and opportunities. Building an accessible data culture is the only way to unearth these buried insights. 

• Companies that embrace this approach reallocate talent and capital four times faster than their peers
• Eighty-eight percent of Australian leaders say that data is pivotal in decision-making.
• Meanwhile, 90% of Australian leaders say that a single view of data amongst all meeting participants drives quality conversations

Data analysis identifies actionable trends

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

And all the better for companies using Tableau in their tech stack — these Trailblazers report 29% faster time to insight and faster delivery of business driving reports, and an average 26% decrease in time required to analyse information. 

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, organisations can make efficient and informed decisions that feed into ROI. 

Strategic work keeps employees engaged

When data analyses guide routine decisions, employees spend less time on basic tasks that add no value, and more time focusing on strategic work. Indeed, for sales teams, for example, data and automation work hand-in-hand to cut down on non-selling tasks. 

The results, as Preena Johansen, Trailblazer and Tableau Ambassador points out, are higher performing, more agile and more engaged teams who can provide a better experience for the customer. And given 85% of decision-makers agree there is a direct link between employee experience and customer experience, empowering employees to work strategically with data is a win-win. 

What’s the difference between being data-driven and data-informed?

In a data-driven organisation, all or most employees can find and analyse data, extrapolate what it means, build a dashboard, and use data to decide the next steps. Employees don’t rely on people to do this — automated dashboards do the hard work for them, so they can focus on the analysis. Being data-informed means making decisions based on a mix of data, internal research, personal experience, and insights. Data-informed organisations may or may not possess the data skills of data-driven ones. 

Where to start when building a data culture?

Creating a data culture, says Wendy Beda, chief Data Officer at Salesforce, “requires a change in attitude from not just the data team or those that are using data, but really the entire executive team and their willingness to be able to engage with data on a day-to-day basis.”

CEOs face countless decisions about where to start when building a data culture. To overcome decision paralysis and start building a community of data champions, Wendy suggests starting small. Find something that has garnered interest or a group who are motivated to make some progress in the data space then build on that. 

Her other recommendation? Bring your colleagues along for the journey.  

“If you have some interest in that space already and there’s someone you can easily bring with you to learn more and help propagate that message across your organisation, do it.” A small use case can go a long way to convincing the sceptics that data can help the organisation accomplish goals faster and with more impact.

With that in mind, here are some suggestions for building an organisation-wide data culture. 

• Choose the right team members
• Equip your team with the right training and technology
• Test your assumptions on a small scale and iterate
• Prioritise data culture’s human element

Step 1: Choose the right team members

Create a working group that includes diverse colleagues from across the organisation. These team members should bring a collaborative mindset, differentiated skills and abilities, and distinct organisational 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

Education and coaching are critical here. As Preena says, training people to understand how to work with data is an important part of democratising that data so anyone in the business can make data-based decisions. “Training can be anything from learning technical skills on platforms like Tableau to data communication and data ethics,” she says.

With easy-to-use technology and free learning platforms like Trailhead, you can connect team members and enable them to unlock hidden insights. 

Step 3: Test your assumptions on a small scale and iterate

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.

Step 4: Prioritise data culture’s human element

Only human eyes can determine if bias has influenced the conclusions so avoid indirect discrimination by not taking data at face value. 

For example, postcodes: at face value, they are nothing more than a location indicator. But when you consider postcodes often correspond with race or socioeconomic indicators — and lenders and insurers consider postcodes in loan applications — human reviewers must step in to ensure decisions made based on this data point are fair and free of bias.

“The biggest thing is understanding how data translates into real-world scenarios,” says Preena. “You need to make sure the people working with the data aren’t putting their own bias on it.”

How The Mix thrives on a data-rich diet

Take family-owned company The Mix, the leading independent distributor for Thermomix. The company has leveraged increased data accessibility and visibility to simplify processes including transactions, onboarding and customer experience. 

Using Customer 360 analytics, The Mix can identify consumer trends and market opportunities. The results? Data-informed decisions, reduced consultant attrition, increased sales, and a 50% reduction in time preparing key reports.

Take the next step to build your data culture

Look, this is hard stuff. It will take time, trial and error, and a culture shift. These things don’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.

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