Establishing a genuinely data-driven organization offers numerous advantages, such as enhancing ROI, strengthening strategy, and improving customer experiences. Leaders across various organizations are eager to foster a data-centric culture. However, the concept of a ‘data culture’ is often misunderstood.

When we refer to a data culture, we’re not just talking about the tools your organization uses or how many of them there are, but rather the organization’s attitude towards data and the value it holds. A data culture is essentially a decision-making culture—where data is pivotal in making informed decisions at every level of the company.

Despite the known importance of data in decision-making, many organizations find it challenging to cultivate a data-driven culture. Unfortunately, there is no quick fix for establishing a robust data culture.

It involves a significant shift in mindset and behavior, and these changes do not happen overnight. Implementing new processes, updating technology, and hiring and training staff requires substantial time, effort, and trust. However, with strategic planning and the right champions, it is achievable.

Building a data culture is a crucial component of advancing within digital consumer intelligence (DCI). It is one of the six key pillars outlined in the DCI Maturity Model, which we explore in our DCI Assessment to help organizations identify their strengths and areas for improvement.

In this guide, we will explore how organizations at different maturity levels interact with the data culture pillar and provide advice on how your organization can progress. We will also delve into the subject with insights from Joakim Nilsson, VP Solutions Consulting for Europe at Brandwatch, who has over 15 years of experience helping brands grow through insights. If you haven’t already, we recommend checking out our DCI Assessment to see where your organization stands.

Building a Data Culture: The Four Stages of Maturity

DCI Maturity can be measured across four key stages. We refer to the earliest stage as ‘Monitoring’ and the most advanced stage as ‘Embedded Digital Consumer Intelligence.’ Here’s what a data culture looks like at each stage of maturity:

1. Monitoring At this initial stage, there may be little to no culture of data usage. As Joakim Nilsson puts it, “decisions are made based on intuition or where the wind is blowing – think senior leaders making decisions based on personal opinions. While this isn’t an ideal data culture, it’s a starting point with significant room for improvement.”

   Organizations in this stage often rely on instinctive decision-making, driven from the top down without thorough scrutiny. A lack of trust and confidence in data is common, which can pose a major challenge, according to Nilsson.

2. Developing Intelligence Organizations at this stage have the foundational elements of a data culture in place but often struggle to act on insights. Nilsson explains, “There’s a clear intention to be data-driven, but the execution may not always match the ambition. This is the stage where we typically see investments in analytics tools and efforts to become more digitalized.”

   For example, consumer insights may be presented to leadership to influence decisions. However, the cultural shift towards data-driven decision-making must be driven from the top down, and senior leadership may not yet be fully championing the data culture.

   To reach a higher maturity level, senior leadership must actively promote data and analytics and empower their teams to do the same.

3. Digital Consumer Intelligence At this advanced stage, leadership champions are using consumer insights data to inform decisions, such as integrating data into recruitment processes or the company website. Analyzing data becomes second nature to key areas of the business.

   “In the ‘advanced’ category, we’re not just hearing about ambitious ideas; we’re hearing about what has been accomplished with data. There’s a common language and understanding of data across the organization, but it’s important to recognize that this culture of data may not be uniform across all business areas,” Nilsson notes.

   We asked Nilsson the critical question: How do organizations reach maturity? “Even at this stage, some teams may lag behind. Access to data is crucial—a culture of data isn’t functional unless everyone in the organization has easy access to data, information, and insights.”

   Another key point is avoiding data silos. Data scientists and their work should be visible across the organization to ensure data is used effectively.

4. Embedded Digital Consumer Intelligence In a digitally mature organization, a strong data culture is evident throughout the business. Here, consumer data is visible and valuable at every level, with experimentation and innovation encouraged. Data is not only used for major decisions but also for everyday tasks—for example, frontline staff using analytics to address customer queries multiple times a day.

   In such organizations, data is paramount and democratized. “Data is not just proficiently used; it’s seen as the organization’s most valuable asset. Employees are expected to be data-driven and justify their actions with data. There may even be a Chief Data Officer responsible for aligning data use with business value,” Nilsson explains.

How the Data Culture Pillar Connects with Other Pillars

The DCI Maturity Model includes five other pillars, and to mature in digital consumer intelligence, organizations must focus on all six areas. However, some pillars are more closely related to the data culture than others.

Insight expert Evelyn Castillo, Account Director for EMEA at Brandwatch, highlights the connection between the ‘Speed to Insight’ pillar and data culture. “Speed to insight means quickly understanding and acting on consumer feedback. This allows organizations to act swiftly and outpace competitors, but a strong data culture is necessary for rapid and widespread action that drives change.”

Similarly, ‘Insights Flow’—the process of delivering the right information to the right people across the organization—fits well with building a data culture. Nilsson explains, “Information flow and data culture go hand in hand. When everyone in the company understands the importance of information sharing, it becomes second nature.”

He adds, “Performance is consistently measured, and insights are shared to connect the dots and gain a holistic view of overall performance. This has multiple benefits. Ultimately, a data culture is a decision-making culture. Embracing data and insights within the company culture enables critical decision-making at all levels.”

5 Tips for Building a Healthy Data Culture in Your Organization

1. Start at the Top A data culture must be driven from the top. Commitment from the CEO and the board is essential and must go beyond mere lip service. When hiring—not just for senior roles—look for individuals skilled in data analysis and fluent in the language you want to establish across your organization.
2. Start Small Large, rushed initiatives are prone to failure. Begin by identifying the business problems you want to solve and determine what data can help. Take small, deliberate steps, focusing on quick wins that will build momentum for larger initiatives.
3. Give Your Program Its Own Brand Even if your insights program is small or in the pilot stage, give it a distinct name and visual identity. This helps your employees recognize it and makes it part of your organization’s language, along with the language around data.
4. Encourage Data Champions Ongoing training is vital. Allocate the necessary budget to ensure your teams know how to use insights tools and interpret data. Emphasize the importance of data in their work, and celebrate those who demonstrate the right behaviors and achieve success through data.
5. Encourage Experimentation A healthy data culture thrives on experimentation, which often includes failure. Don’t limit data access to a few teams—allow everyone to experiment with data, create proofs of concept, and be creative. Innovation flourishes in environments where people are encouraged to try new things and learn from their mistakes.

• Introduction
• Building a culture of data: The four stages of maturity
  • 2. Developing intelligence
  • 4. Embedded digital consumer intelligence
• How does the culture of data pillar relate to other pillars?
• 5 tips for building a healthy culture of data in your organization
• Assess your performance across the pillars of DCI

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