With the amount and variety of data increasing in the cloud, almost every business decision can (and should) be rooted in data. Data-driven decision making can have a significant impact across various business functions. But in the cloud, businesses often face the question, “How do we effectively discover, access, trust, and analyze mass data sets?”

With the help of business intelligence dashboards and easy-to-understand data visualizations, businesses are more efficiently putting data to use. However, one obstacle continues to stand out in the quest to maximize data value: siloed access creating bottlenecks and slowing down the business’ ability to make the most of its data.

So, how can businesses truly maximize the value of their data? By empowering the team.

Putting power in the hands of those closest to the data

Democratizing data ensures that non-technical users outside of a centralized data team can easily find, access, trust, and use data at scale. This approach seeks to eliminate the barriers that restrict data availability to a select group of business analysts and scientists. By putting power in the hands of those closest to the data, data democratization empowers employees, irrespective of their technical capabilities, to get value from data.

To get to this state, organizations could implement a data mesh framework for a decentralized, domain-oriented data architecture. In data mesh, the data is owned by specific teams who are experts in their respective business areas. This is made possible by central governance policies and self-service tooling. Teams are able to trust the data quality while having control over their data and the ability to quickly glean relevant business insights. Domain-oriented ownership also fosters a sense of accountability that leads to better data stewardship across the organization.

To operationalize data mesh, organizations must:

Move to a federated model: Federation is a decentralized approach to data management. The federated model gives power to individual teams within an organization to manage their own data. This model can break down silos that may result in a centralized data architecture. Data is now handled by the domain teams that generate and use the data, empowering teams to take full ownership of their data and feel a sense of accountability.

Implement central policies: Taking a decentralized approach should not compromise on data governance. Organizations should enforce common standards across various teams and data stakeholders. Central policies ensure that while data is more easily accessible, it will remain aligned with organizational standards, internal policies, and ethical considerations. These central policies can be effectively managed when built into tooling, which can help each domain team feel confident that the required governance and controls have been accounted for through the self-service experience.

Use self-service tooling: Self-service tooling ensures that users with varying levels of technical expertise can find, access, trust, and use data. These tools empower users to derive insights from data without needing to rely on data engineers or experts, promoting a more data-driven culture within the organization.

Data mesh represents a paradigm shift in how we manage and value data. A departure from centralized data management, data mesh is designed for scale and brings data closer to the source, treating data as a product with clear ownership and accountability. With each domain handling its own data, there is no longer a single point of failure. Instead, the result is a resilient, distributed system that can scale with the business. This makes the data mesh an ideal framework for large enterprises that manage vast amounts of data in the cloud and want to empower users to get value from data.

Culture plays a pivotal role in increasing data access

Process and technology alone are not enough – organizations must also focus on people and culture in order to democratize data. This culture should emphasize shared power, open information, and an appetite for learning. It will foster collaboration, innovation, and continuous improvement by encouraging all associates to contribute with their own ideas and talents. To democratize data, an organizational culture must:

Encourage information sharing: Information should be seen as a shared resource rather than a closely guarded asset. This perspective aligns perfectly with the goal of data democratization. The idea is to eliminate data silos and create an environment where non-technical users can quickly find, access, and use data.

Promote learning and innovation: Users are encouraged to experiment and learn from failures, which is essential for utilizing and interpreting data effectively. Learning should not be limited to technical skills – it should also include data literacy skills that will empower individuals, like understanding data sources, their uses, and implications.

Empower all users: The culture seeks to break down traditional hierarchies and allow for decentralized decision-making, which can lead to more innovation and an engaged workforce. The idea behind data democratization is to make data accessible so that all users can make informed decisions, and this would not be possible without an organizational culture rooted in this value.

Focus on building trust: Organizations can foster trust amongst users by demonstrating the value of information sharing and respecting user autonomy. A culture built on trust is crucial to data democratization because users will need to trust that the data they use has the appropriate governance applied through self-service tooling, and the organization must trust that its users will be good data stewards.

Putting it into practice

Capital One modernized its data ecosystem on the cloud, adopting Snowflake as its data cloud. But to truly realize the benefits of managing data in the cloud, the company had to reimagine how it could empower both technical and non-technical users within the business to access and use data while remaining well-governed.

Capital One sought to build a scalable architecture that would empower a large number of users to find, access, and use data in the cloud. By operationalizing data mesh, it was able to further federate data management with central policies built into self-service tooling for users.

However, one of the first things Capital One learned was that with that great power comes great responsibility. Unexpected costs may rise when lines of business manage their own compute requests and configuration. Capital One built tools to solve their data management challenges at scale, including Capital One Slingshot, a data management solution that helps Snowflake users improve performance and empower teams with features that provide enhanced visibility and optimization.

Another best practice that is baked into the day-to-day at Capital One is the idea of continuous improvement. Organizations must acknowledge that workloads and user behavior is constantly changing, evaluate these changes, and provide recommendations for remediation. Self-service tooling can automate this, but it is still essential to maintain this focus.

Fundamental steps to maximizing data value

To fully leverage the value of data, organizations must shift how they view, manage, and use data. To recap, organizations should follow these fundamental steps to maximizing data value and empowering teams:

  1. Foster an open organizational culture: Culture is the backbone of any transformative initiative, and data transformation is no exception. Encourage openness, collaboration, and experimentation. Empower teams to make data-driven decisions, and ensure they have the tools, skills, and autonomy to do so. This creates an environment where innovation thrives and accelerates the journey towards data democratization.
  2. Consider the architectural framework: A centralized data approach may struggle to scale and adapt to evolving business needs in the cloud. Consider operationalizing data mesh, where data is treated as a product and ownership is federated to domain experts. This not only decentralizes data management, promoting agility and scalability, but also enhances data quality and relevance by placing ownership in the hands of those who know the data best.
  3. Leverage powerful self-service tooling: Tools like Slingshot can manage and automate critical data management needs, providing a user-friendly experience that users can trust in. Leveraging self-service tooling can greatly enhance the efficiency and effectiveness of data operations, ensuring that data remains well-governed as it moves through the organization.
  4. Focus on continuous improvement: Data is constantly changing, and an approach to managing it should be as well. Establishing a rhythm of continuous learning, feedback, and improvement helps organizations stay attuned to the evolving data landscape. This practice instills a culture of growth and adaptability within an organization.

The path to maximizing data value is a continuous journey. By nurturing organizational culture, distributing data ownership, and focusing on improvement, businesses can equip teams to make data-driven business decisions.