Over the past decade, companies have developed a data-driven culture, whether that means monetizing data or using it to power technology initiatives such as AI, analytics, and data science. We have been working hard to build this.
It seems like a lot of work. A 2023 Harvard His Business Review (HBR) white paper states that “many companies struggle to implement data strategies that create business value.” The white paper cites a 2022 NewVantage Partners study that found that only 26.5% of Fortune 1000 data executives surveyed said they were successful in building a data-driven organization. ing.
In the 2024 edition of an annual study by a consulting firm now called Wavestone, the figure was 48.1%. But Christina Egea, vice president of enterprise data products at Capital One, who commissioned the HBR white paper, said there is a disconnect between enterprises' data aspirations and the data realities reflected in her Wavestone report. He pointed out that there are contrasts.
“There's a huge chasm. Everyone thinks data is critical to developing good business strategies, but no one thinks they're doing it well,” she says. I did.
Egea offers 5 tips for building a data culture, based on whitepaper and Capital One's experience building a data ecosystem aimed at making data accessible across financial services companies Did.
1. Establish messaging around a data-driven culture
Egea cited a lack of corporate involvement in data strategy as the biggest challenge in building a data-driven organization. Data specialists alone cannot bring together a data-driven company; senior business leaders must support data efforts.
To do that, Egea noted that organizations need top-down messaging that emphasizes the importance of data. Messages need to emphasize that data is at the heart of the company and that employees are rewarded for meeting customers' data needs, he added.
“A data strategy cannot be successful without a business strategy,” Egea said. “We need to embed data at the core of how we approach business.”
2. Building a data ecosystem
Investing in the underlying data ecosystem is another key element of your data strategy. The ecosystem provides a marketplace where data scientists, analysts, engineers, and other employees can use an organization's data.
Egea said Capital One's data ecosystem begins at the base layer, where data is created and published. The company has traditionally created data through batch processing, but is now increasingly using real-time streaming applications. Stream processing can quickly analyze, transform, and send data to an application or data repository.
Christina EguiaVice President of Enterprise Data Products, Capital One
The next layer is the storage layer. Here, Capital One first makes the data available in raw form and then transforms that data to fit more specific use cases, he said. Finally, the access tier allows customers to make data available from the storage tier or streaming applications.
Other important ecosystem considerations include data cataloging and metadata that inform users of what data is available and where it is located, and governance policies to govern data access and use, Egea said. states.
3. Treat data as a product
Egea said there is often a disconnect between the business teams that need the data and the teams responsible for creating the data. However, treating data as a product provides a workaround that fosters collaboration between data creators and their internal customers. Based on the product philosophy, data creators assess end-user needs and work backwards from that understanding to make data available in ways that are useful to users, he added.
The HBR whitepaper reiterates this approach, stating that the product approach is “focused on internal customers.”
Egea's team has cultivated a backwards approach to data products at Capital One. He said the team's focus is on building data standards and tools across the enterprise, not on determining how and for what purposes the data should be used.
“A really important aspect of how we think about our product is that no one in the company can define the data intent for any data across the company,” she said. “Instead, you need to ensure that ownership remains within the business area that is responsible for that data.”
4. Provide self-service tools and training
Egea said top-down communication helps instill a data culture, as does a bottom-up approach that provides self-service tools to access and use data.
“It's our expectation that everyone in the company can leverage data, but we need to give them access to the best tools,” she said.
Data users also need access to training to use these tools effectively, Egea said. He added that internal forums, where users can learn best practices and share their own methods, also promote data literacy among employees.
Self-service can democratize access to data, but it faces several obstacles. A 2023 Capital One-Forrester Research study found that there are several factors preventing organizations from pursuing a self-service data strategy. Potential obstacles include cultural considerations such as lack of easy-to-use tools, funding issues, and insufficient collaboration.
5. Encourage executives to dig deeper into their data
Convincing top executives to promote and build a data culture is not easy. This is especially true when it can be tempting to ignore data management and focus solely on high-profile AI and machine learning projects.
Egea suggests encouraging company leaders to spend a day in the lives of their data users. Business owners who work with data will soon learn how easy it is to find and use data, and whether it is consistent, she noted. Data tours like this help leaders decide where to start with their data efforts. For example, an organization may have great data but lack useful tools or adequate storage, she said.
“It's really important to walk the mile in your team's shoes and really understand the challenges that are there,” Egea said.