Building a Data-Driven Culture: Four Key Elements

Thanks to the deafening buzz around data science and AI, enterprise leaders no longer doubt the transformative potential of this powerful duo. Yet, a striking statistic reveals a bigger leadership challenge: More than 57% of companies struggle to build a data-driven culture, according to Wavestone research. This indicates that in many cases, leaders believe in the power of data and are investing in AI, but their organizations still aren’t getting the real benefits.

Indeed, for many leaders, the challenge is not buying advanced analytics tools or building accurate technical solutions. The real hurdle is subtle yet much more important: fostering an environment within an organization where individuals instinctively turn to data anytime they must make a decision. This is the real meaning of being data driven or creating a data culture.

Building a Data-Driven Culture: Questions to Ask

Why is building a data-driven culture incredibly hard? Because it calls for a behavioral change across the organization. This work is neither easy nor quick. To better appreciate the scope of this challenge, let’s do a brief thought exercise. Take a moment to reflect on these questions:

How involved are your leaders in championing and directly following through on data-driven initiatives?

Do you know whether your internal stakeholders are all equipped and empowered to use data for all kinds of decisions, strategic or tactical?

Does your work environment make it easy for people to come together, collaborate with data, and support one another when they’re making decisions based on the insights?

Does everyone in the organization truly understand the benefits of using data, and are success stories regularly shared internally to inspire people to action?

If your answers to these questions are “I’m not sure” or “maybe,” you’re not alone. Most leaders assume in good faith that their organizations are on the right path. But they struggle when asked for concrete examples or data-backed evidence to support these gut-feeling assumptions.

The leaders’ dilemma becomes even more clear when you consider that the elements at the core of the four questions above — leadership intervention, data empowerment, collaboration, and value realization — are inherently qualitative. Most organizational metrics or operational KPIs don’t capture them today. Thus, leaders end up investing in and backing data initiatives despite having little visibility into their likely outcomes.

This is also why these leaders may be caught unawares when their organizations fail to become data driven, as evidenced by surveys such as the one noted above.

This article spotlights four areas leaders must focus on to build a data-driven culture, using real-world examples of how to make this journey fun, engaging, and relatable for employees.

How to Shape a Data-Driven Culture: Four Pieces

To build a truly data-driven culture, leaders must look beyond the traditional technical focus of AI journeys. The following four elements can bring about a behavioral change across organizations, putting data at the heart of decisions.

1. Leadership Intervention

Organizational leaders must get actively involved in their strategic data and AI initiatives for the organization’s data-driven culture to take root. Since decision-making with data calls for a shift in the way people do their jobs, this needs active change management.

Unfortunately, executive-level owners often stop at funding the data initiatives and delegate the entire execution away.

Leaders can intervene by clearly stating why the company needs data and AI in the first place. They must own the outcomes of the initiatives and periodically check in to ensure that everyone considers data a part of their job — and not just the IT department’s or the data team’s responsibility.

Finally, leaders must walk the talk by actively and visibly using data and AI solutions — as part of their work, in meetings, and for organizational reviews. And they should foster an environment of curiosity to encourage employees to question processes, propose innovations, and take calculated risks to change the status quo.

Concept in Action: Rewarding Failures for ‘At Least Having Tried’

When DBS Bank embarked on its digitization journey, CEO Piyush Gupta made it a priority to build a culture that rewarded risk-taking and valued learning from failures. To spur innovation, he wanted to create safe spaces for employees to experiment, even at the risk of failure.

In a podcast, Gupta recounted a pivotal moment during the shift, when an experiment failed and there was regulatory pressure to punish the person responsible. But he intervened and pushed back, saying, “Not only am I not going to punish the person, I’m going to give them an award for at least having tried.”

Such an example of a leader walking the talk sends a powerful message throughout the organization. This kind of leadership behavior shows people that they can take calculated risks and it’s acceptable to fail as long as they try, learn, and adapt. By embracing a growth mindset and focusing on psychological safety, Gupta cultivated a culture where DBS employees feel empowered to innovate and take ownership.

2. Data Empowerment

To build a data-driven culture, leaders must take everyone along. Organizations need to empower all individuals by giving them not just access to data but also the ability to use it effectively. To get an organization ready, this empowerment must occur at three levels: data readiness, analytical readiness, and infrastructure readiness.

Data readiness is the foundation that ensures that good-quality data is readily available to the right person at the right time. Investing in platforms and governance policies to streamline access is the first essential step. Once individuals have data, they then need skills to use it to inform their decisions. This is not just about training people on tools but also helping them think critically, interpret data, and develop a deeper understanding to derive actionable insights. Finally, AI projects call for the right hardware and software to ensure seamless operations — whether at the organizational or individual level.

Concept in Action: Upskilling by Competing in DeepRacer Events

Imagine coding a fully autonomous car to compete in a racing challenge. Eighty thousand people from around the world signed up to do just that with a one-eighteenth scaled-down version of a car in the AWS DeepRacer learning program. In 2019, JPMorgan Chase embraced this challenge to help its employees upskill in an enjoyable way.

The program allowed participants to compete with local and global peers from within the company and with external challengers, including people at other businesses and universities. Employees could compete in racing tournaments by coding autonomous vehicles in the cloud and racing them on physical tracks. JPMorgan Chase started competing locally in Chicago and London and eventually went global to about 20 technology centers.

This program provided access to data and infrastructure and made upskilling on analytical topics like reinforcement learning experiential. The program encouraged employees to collaborate on projects to spur innovation and cost optimization. Employees without an engineering background or coding or machine learning experience joined the bandwagon. In 2021, seven of the 40 finalists in the DeepRacer championship finals were from JPMorgan Chase.

3. Collaboration

Collaboration is a key prerequisite for innovation within a data-driven organization. When AI projects are driven by technology teams, it can be a recipe for failure. Business and technology teams must come together to conceive and execute ideas and deliver outcomes from AI. But as any veteran leader will tell you, cultivating cross-functional partnerships is easier said than done.

Believe it or not, one of the barriers that keeps the teams apart is language. No, it’s not their skills with spoken or written language but rather the ability to understand, craft, and communicate information. The moment someone uses jargon or oversimplifies their message, their intent can be lost.

That’s why data literacy is an important skill for everyone within an organization. Enhancing data literacy across teams eases communication challenges, thereby empowering team members to share insights and engage in collaborative, informed data-driven discussions.

Concept in Action: Making Data Everyone’s Job Through a Data Ambassador Program

Gulf Bank aspired to foster data camaraderie among its 1,800 employees. As authors Thomas C. Redman and Thomas H. Davenport noted in a 2023 MIT SMR article, the bank’s leadership team launched an ambitious data ambassador program in which it assembled a network of connectors — data leaders who would help colleagues across teams see the value of data science and communicate using a shared language.

The initiative was first introduced during meetings with the bank’s management committee, where the importance of data quality was emphasized. Despite the leaders’ initial reservations, the idea of nominating data ambassadors took hold. The bank committed to world-class training for these ambassadors, along with media recognition and branding efforts to highlight their roles.

This initiative transformed initial skepticism into enthusiasm, opening up numerous opportunities for personal and professional growth among the staff. As data ambassadors began to take prominent roles within the company, they not only improved data-handling practices but also fostered a wider environment of continuous learning and curiosity. This program highlights how creating ambassador roles can help facilitate collaboration and empower employees to move closer to the goal of becoming data-driven.

4. Value Realization

A data-driven culture is all about achieving measurable business goals. To truly realize the value of data initiatives, it’s imperative to clearly identify the targeted outcomes for each project upfront. Leaders need to define KPIs that will best capture the success scenarios of these initiatives and then ensure that all key stakeholders buy into these target outcomes and how they will be measured.

However, the data needed to compute such novel KPIs might not be readily available. If this is the case, leaders must put processes in place to start collecting necessary data even before the projects are greenlighted. This ensures that sufficient data is available before and after an initiative goes live.

Finally, just as important as tracking success is celebrating it. Recognizing and rewarding the achievements of key stakeholders involved not only motivates team members but also inspires the entire organization to experiment with data-driven innovation. This is crucial for sustaining the momentum with data and AI.

Concept in Action: Avoiding Carrier Penalties Through Intelligent Warehouse Scheduling

A cold chain logistics company was struggling with inefficiencies in scheduling carrier appointments — a challenge that led to higher costs and customer dissatisfaction.

To address the inefficiencies, the company introduced an intelligent appointment-scheduling system based on machine learning. This data-driven solution eased carrier appointment scheduling by analyzing historical data. By evaluating internal factors, such as order complexity, warehouse load, and expected carrier delays, and external factors, such as weather and seasonality, the system crunched the numbers to generate actionable recommendations for better scheduling.

The initiative was prioritized and company leadership clearly defined its targeted outcomes: a reduction in turnaround times and cost savings in avoided penalties from missed service-level agreements. The system was deployed across 26 warehouses and managed about 650 appointments at each of them daily.

The system helped reduce turnaround time by 16%, saving the company $1.2 million in penalties annually. Additionally, it boosted customer satisfaction. Leaders celebrated this success story across the company by recognizing the team in internal newsletters and town hall meetings. The marketing team converted the story into an engaging video case study that was shared on social media and through webinars. This initiative helped the organization build the momentum for a data-driven culture and finalize a road map for embarking on several data and AI initiatives over the next few years.

Engaging Employees in a Data-Driven Culture

Engaging in a data-driven cultural transformation is more like running a marathon than a sprint. While there could be leading indicators of success, measurable business goals often take a while to manifest. Leaders must remain steadfast in their support, ensuring that investments in technology, upskilling, and organizationwide collaboration are not sporadic but rather a part of a long-term strategy.

Measuring success and celebrating key milestones not only acknowledges the hard work and innovation of teams but also galvanizes the entire company. The real-world examples above demonstrate how this entire journey encompassing the four elements can be made fun, engaging, and collaborative as your organization moves down the path to becoming data driven.