Data Culture: What it is and why it matters

What is Data Culture?

If culture is the ideas and customs of a group of people, then data culture is when a group thinks, talks and acts in data.

Companies and organisations with a strong data culture don’t just accumulate data, but value, gather, understand and apply data effectively throughout the organisation. And, critically, they make decisions based on data. Not only when it applies to small adjustments in user journeys or sales messaging, but especially big-picture decisions in the company strategy.

But what does a data culture organisation look like?

Firstly, data culture can’t exist without a Chief Data Officer. The CDO has to lead data analytics initiatives, ensure high-quality data flows through the organisation and, perhaps most importantly, make sure that the CEO understands the value of data. It will be hard for a company to grow a data culture if the top executive doesn’t get it.

It’s up to the CDO to create an understanding that data is not just for the data team. They need to get both executives as well as individual contributors on the same page. Data needs to be understood to make the job easier, not harder.

A company with a thriving data culture will also have a solid strategy around data gathering, cleaning and analysis. This can cover finance, sales and marketing, IT, inventory management, supply chain, data-gathering physical assets and much more. Systems will be procured or developed with a data-handling capability in mind.

At the team level, empowered by access to data sets and analysis tools, team members will be trained to gather, analyse and interpret data and communicate findings to inform decision-making.

Why does Data Culture matter?

By promoting data-driven decision-making, data culture makes an organisation more competitive. Companies make smarter decisions, build stronger customer relationships, innovate more effectively and operate with greater efficiency.

Benefits of Data Culture vs non-Data Culture

The advantages of data-driven decision-making are well established. A McKinsey survey found that a data-driven organisation achieves better financial performance, such as above-market growth and EBITDA increases in the range of 15 to 25 percent. Likewise, Forrester found that data-driven firms are 2.8x more likely to report double-digit year-over-year growth versus firms at the beginner stage. In the tussle between basing big decisions on gut feeling and instinct versus data, data has won.

But not every firm that invests in data and analytics realises big performance gains. Many data and analytics projects run aground, their sails failing to gather wind.

And the primary cause of failure and the biggest roadblock to the success of a CDO is not technical, but cultural. The lack of a data culture results in an apathy to data-driven decision-making up and down a corporate hierarchy. This eventually leads to poorly defined problems, misallocated investment and misaligned incentives.

That’s why the starting point for any CDO should be asking “Does my organisation have a data culture, and if so, what stage are we at?”

But it’s not just about the parts of a business working on cutting-edge data projects. Harvard Business Review found that 87% of leaders expect improved business success when frontline workers are empowered to make important decisions in the moment. Ibrahim Gokcen, Chief Data and Analytics Officer at Aon, found that data culture allowed employees to focus on more “human” domains, such as innovation, collaboration, and communication.

The State of Data Literacy 2023 report found that data training resulted in improvement in the quality (79% reported gains) and speed (77%) of decision-making, including over half citing substantial improvements. Improved decision-making had positive downstream effects on innovation, customer experience and employee retention.

And the stronger the data culture present at a company, the better the performance gains.

The Role of Data Literacy in Data Culture

McKinsey surveyed data analytics leaders to learn about their data journeys. On the subject of adopting a data culture, the report found data culture requires investment in data skills and training.

The motivating factor being referred to here is data literacy.

Data literacy is the ability to communicate through data. It fertilises the soil in which data culture can grow. It allows data culture to proliferate across and through a company. Data literacy reverses the friction that comes with attempts to affect behaviour change. Data literate staff are encouraged and empowered to ask for more data and to put it to use.

It’s clear that organisations require data literacy to succeed in an economy in which data is key to success.

Here are a few behaviours and outcomes that data literacy enables:

1. Data literacy leads to a better understanding of data, empowering individuals to interpret data, identify patterns, and draw meaningful conclusions. Without this ability, data becomes a vast, untapped resource.
2. A data culture fosters communication and collaboration. Data literacy allows individuals to communicate data insights effectively to colleagues from different backgrounds, creating a shared understanding.
3. Data literacy allows team members to independently conduct data analysis relevant to their role. The series of small optimisations across projects and teams compounds across the business, leading to big improvements in operational performance.

The goal is not to make data scientists out of every team member within an organisation. The goal of data literacy is to reach a basic level of competence, comfort and familiarity with data. That means understanding core concepts like data types, collection methods, storage and quality. Next it focuses on grasping basic data analysis like cleaning, organising and manipulating data. And finally, it’s helps being able to create visualisations to communicate data insights effectively.

The fastest way to reach data literacy is through a data literacy programme.

A successful data literacy programme should not just focus on the education piece itself. Specific groundwork needs to be done for a successful uptake and outcomes of the education. We will look at how Data Literacy Academy uses five stages of implementation, from initial assessment through to embedding and optimising. A change management model guides employees through the behaviour change needed to make data literacy sticky.

Building a Data Culture: Steps, Strategies and Challenges

Establishing a data culture in an organisation requires people to change the way they behave. And that is not a straightforwards task. A whole host of psychological, habitual, intellectual and incentive-based hurdles need to be cleared.

Therefore, data culture depends on an effective change management programme. 

An effective change management programme ensures a structured and controlled transition with minimal disruption to operations. Morale stays high and encourages adoption within your company.

A five-step process will take a company from a standing start to data literacy:

1. Planning and preparation
2. Baseline Assessment
3. Enterprise Awareness Campaign
4. Enterprise Deployment
5. Embed and Enhance

Notably, the actual training and upskilling stage only comes in at stage four. That’s because laying the groundwork is a prerequisite for the success of a behaviour change initiative. Without first deciding on goals and project scope, understanding the organisation and its existing culture around and usage of data, there is a high chance of low adoption and misalignment from the start. 

Embedded in the five steps is an approach to people-focused change, called ADKAR.

ADKAR stands for:

• Awareness: Awareness of the need for change
• Desire: Desire to participate and support the change
• Knowledge: Knowledge and learning on how to change
• Ability: Ability to implement desired skills and behaviours
• Reinforcement: Reinforcement to sustain change

The ADKAR model is a useful model for affecting organisational change. Combined with the five-step process it can help to embed data culture into the organisation.

Challenges of Adopting a Data Culture

Beyond change management, you need to tackle various challenges to ensure the success of data culture adoption.

At Data Literacy Academy, we focus on digging into perceptions and preconceptions of data first, before getting into hard skills later.

A homogenous approach to data education should be avoided. It’s better to focus initial energies on those departments that sit closest to data – which are often Finance, Sales or Marketing.

The depth and extent of training will and should vary across the company. Failing to agree on the required level of proficiency for different roles is a common stumbling block.

Businesses can mistake training on technical tools for data education. It’s important to focus on what data literacy is and exclude what it isn’t. While skill in common data-handling programs like Excel, Power BI and Tableau certainly plays a role in data literacy, intellectual understanding of data is the priority. Someone with strong data skills but weak technical skills will outperform the opposite.

Another challenge is fostering an environment in which your newly data-literate employees can explore their skills without undue fear of being punished for making a mistake. This is an example of how company culture can make or break the “Reinforcement” part of the ADKAR model.

Culture change also needs to take into account differences in learning preferences. Some prefer a theoretical understanding, some like to learn hands-on and some respond to exam-based courses. Failure to account for these differences can impact the project’s success.

Case Studies: Successful Data Culture Transformations

Gymshark

British sportswear company Gymshark enjoyed attention-grabbing and explosive growth through the 2010s and outgrew its existing infrastructure several times over. Now a billion-dollar company, Gymshark undertook a restructuring to move away from the gut-feeling approach that guided founder Ben Francis’ early decisions to a data-driven decision engine to guide the company’s continued expansion.

Gymshark, assisted by data analytics partner Fifty-Five UK, onboarded Google Analytics 4 to gain greater customer insights and upskilled its wider staff.

The questions the programme sought to answer included:

• Who are Gymshark’s users?
• Are they long-term customers or visiting for the first time?
• Do they engage with the website, app, or both?

Gymshark was able to identify and rectify a wide range of problem areas, such as abandoned carts for first-time users, difference in usage between app and desktop users and problems with the wishlist user journey.

NatWest

British retail bank NatWest needed to provide data insights to meet a wide spectrum of climate analytics use cases across lending, investment and compliance. To achieve that, NatWest wanted to build a new IT culture based around cloud solutions.

Head of ESG cloud solutions Kaushik Ghosh Dastidar explains: “At the time, cloud adoption was quite minimal at NatWest. We knew that we could do a lot more by thinking differently and trying new things in the cloud, but there was no established pattern — so we’d need to show the benefits of our approach every step of the way.”

By establishing a cloud culture, NatWest proved the value of new cloud use cases and provided a precedent for other teams to move systems to the cloud, reducing costs and carbon emissions.

L&G Group

Indian construction conglomerate L&G aimed to leverage the power of data-producing computing technologies to significantly improve core operations that utilise workers, machines, and materials to save costs, to improve productivity, and to reduce execution time.

However, it recognised that overcoming cultural issues and resistance would be a major part of the project and that the substantial investment in technology would be wasted without data culture. Management and employees alike needed to engage with the new data-producing technologies and each team would be expected to improve their performance based on analyses of the data provided by the technologies.

L&G started from a low base of data literacy, with less than 20% of its L&T workforce using electronic devices on any construction job site. By appointing digital officers in each business unit in addition to digital champions on all levels, L&G has reaped the benefits of its investment in technology.

Conclusion

Data literacy is a fundamental ability of people and companies in today’s data-driven world. It allows effective communication, collaboration and decision-making based on data. To attain data literacy, businesses must implement a thorough data literacy programme which tackles cultural, organisational and technical hurdles. This involves raising awareness, building enthusiasm, imparting expertise, growing ability and reinforcing change.

By fostering a data-literate staff, companies can unlock the full potential of their data and drive innovation, operational efficiency and competitive advantage. Data literacy is not merely about comprehending data, but also about leveraging it to make better decisions and drive positive outcomes.