What is data culture? A guide for data-driven organizations

Core tenets of a data culture

A data culture relies on the following core tenets, with each reinforcing the ideal role data should play:

• Data democratization. Every employee should have easy access to relevant, role-appropriate data.
• Data-driven decision-making. Data culture emphasizes making decisions based on evidence and reason, meaning data needs to be at the foundation of any business decision.
• Data governance. The organization must ensure that data is properly managed, allowing employees to access the information they need. This includes implementing policies and access controls to ensure that role-appropriate data is shared.
• Data literacy. Employees at all levels need to have the skills to read, understand and use data effectively.
• Data trust. Policies and standards must be in place to ensure data quality. To make full use of a data culture, an organization must be confident in its data's accuracy.

Data culture vs. data strategy: What's the difference?

A data strategy is a set of long-term objectives for the use of data in an organization, including any policies and practices that support those objectives. To be successful, a data strategy must cover all uses of data in an organization. It can't consist only of technical processes for data management and analytics. These strategies also need to be comprehensive, with broad involvement and support, to ensure that data is managed well and used effectively. Data strategies will also look different for each organization.

While a data strategy acts as a formal and long-term plan for how an organization uses its data, a data culture is more of a mindset for how employees within an organization use that data on a day-to-day basis. In short, a data strategy is a laid-out plan, while a data culture is a principle to follow.

Barriers to building a data culture and how to overcome them

Building a data culture can come with challenges. Some common hurdles an organization might face when attempting to build a data culture include the following:

• Adoption. Teams might resist making the change, as it involves altering their daily workflow and how they might have traditionally made business decisions.
• Data literacy. If employees don't have the knowledge on how to understand and interpret relevant data, then they won't be able to fully use the data culture when making decisions.
• Data quality. Inconsistent, inaccurate or out-of-date data can reduce employee trust and significantly affect the quality of decisions made under the process.
• Security. Balancing data accessibility and security can be a challenging task. Clear policies and access controls should be implemented to prevent employees from accessing sensitive data they don't need to complete their work.

Benefits of an integrated data culture

Organizations that can create a strong sense of a data culture might likely see the following results:

• Data-based decision making. Decisions made by employees will be guided by accurate data as a foundation.
• Efficiency. Providing employees with access to data they might not have otherwise had can help improve operational processes.
• Employee support. When employees have access to data, they are better positioned to contribute and help make business decisions.
• Risk management. Data governance practices that accompany a data culture mean an increased potential to identify data-based risks.

How to build a strong data culture across the enterprise

There are 10 steps chief data officers (CDOs) can take to build an enterprise data culture in their organization. Each step might require different strategies or approaches and can affect the business to varying degrees. Some steps are more difficult to execute than others, but each plays a role in building a successful data-driven organization.

1. Align analytics and business strategies

The first step to building a successful data culture is to link the data and analytics strategy to the organization's business and operational strategies. Organizations struggle with understanding the economic value of data and how to extract that value. Leadership should communicate about how the vision, mission and goals of the data and analytics strategy support the business strategy. Demonstrate how data and analytics can create new sources of customer, product, service and operational value.

2. Foster a culture of collaboration and experimentation

Create a data and analytics culture that fosters collaboration, experimentation and the sharing of learning around value creation. Many organizations lack a formal data and analytics literacy foundation to drive awareness and empowerment among employees. Educate stakeholders on how data and analytics can create value. Engage business stakeholders by integrating their requirements, desired outcomes and key performance indicators (KPIs) into analytics models. Reward data and analytics advocates and understand the role of failure as a learning and growth mechanism.

3. Use data and analytics for innovation

Data and analytics are a valuable resource to create new products, services and business models. The lack of stakeholder empowerment and organizational agility, which enable organizations to explore and identify new business opportunities, can hinder the benefits of innovation. Leadership should adopt a collaborative, value-centric methodology that uses design thinking, rapid prototyping, minimum viable product development and experimentation to explore new business opportunities. Use data and analytics to access, blend and analyze internal and external data sources with AI to create new customer, product and market revenue streams.

4. Develop a use case roadmap

Develop a use case roadmap that outlines the short-term and long-term goals, milestones and deliverables. The roadmap is only successful if stakeholders and data teams work together to identify the desired outcomes. Create a cross-functional steering committee, working groups and feedback mechanisms. Align the roadmap with the organization's strategic priorities and data and analytics capabilities. Monitor and adjust this roadmap as needed.

5. Cultivate talent

A common challenge that organizations encounter when becoming more data-centric is a lack of data skills among employees. Nurture and align data and analytics talent and skills with organizational needs. Recruit, train and retain data and analytics professionals. Create a data and analytics career path and learning culture that motivates and rewards employees. Establish communities of interest to accelerate skills development.

6. Form an advisory council

Establish a business advisory council chartered with the prioritization and funding of AI and data-based business initiatives. The council can coordinate AI and data asset management across the organization and empower business units. It can also decide how to prioritize data and analytics initiatives based on the value impact and feasibility. Show ROI and value creation from data and analytics projects. The advisory council won't succeed as an IT initiative; senior business management must co-lead the council for it to succeed.

7. Establish a data management framework

A data management framework ensures data quality, security, privacy and ethics compliance. Ensure business stakeholders understand the roles data management and data governance play in delivering relevant and meaningful outcomes. Implement data and analytics governance policies to ensure data quality, security, privacy and ethics. Monitor and audit data and analytics activities and outcomes.

8. Use proven technologies

Too many technological advancements can distract CDOs from their focus on value creation. They must build a data and analytics foundation that uses the most appropriate and proven technologies. Ensure alignment and integration of data and analytics technologies with business processes. Adopt a data architecture that enables data agility, sharing and scalability. Use AI to generate business insights and recommendations from the data.

9. Improve performance with external providers

Partnering with external data and analytics providers can enhance the organization's data and analytics capabilities. Leadership should assess the current internal data and analytics capabilities to identify gaps or areas that need improvement. Identify external data and analytics providers that offer complementary data and analytics capabilities. Manage partnerships to ensure quality, security, privacy and ethical application of the data and analytics assets.

10. Identify, monitor and report KPIs

To ensure cultural measures align with business measures and achieve the desired outcomes, leadership should define KPIs or other metrics to measure cultural transformation effectiveness. Hold collaborative workshops to identify, validate, value and prioritize cultural metrics. Map metrics to business strategies to ensure organizational buy-in and alignment. Report progress and issues transparently and frequently.

Integrating these steps can help organizations empower their employees in a data-driven, value-centric culture. Explore more tips for creating a data-driven culture.

Editor's note: This was originally written by Bill Schmarzo and published in 2024 under the title "Use these 10 steps to successfully build your data culture." It was expanded and edited in 2025 to improve the reader's experience.