Lewis Wynne-Jones, the Vice President of Product at ThinkData Works, recently highlighted the importance of a staging platform in data strategies. A staging platform acts as a mission control center for data where it is verified, discovered, accessed, managed, and governed before being put into production. This platform sits between storage and analytics and helps data flow smoothly within an organization, bringing engineering and business teams together to make data more valuable.
While the concept of a staging platform is not new, many organizations already have some form of software deployed to make data more usable and reliable. The Wavestone Data and AI Leadership Executive Survey showed that a significant percentage of organizations have increased their investments in data management software. However, despite high investments in technology, businesses struggle to innovate and become data-driven. Fewer than half of organizations currently manage data as a business asset or have a data-driven culture, indicating a disconnect between technology investments and business outcomes.
Many organizations tend to blame their culture for the lack of innovation when data management tools do not deliver expected results. However, the issue lies in how technology is bought and deployed within organizations. While data catalogs, AI copilots, and other tools solve specific issues, they do not address the larger problem of integrating capabilities across the data architecture. Organizations need an integrated suite of tools in a staging platform that can verify, access, manage, and govern data effectively, ensuring that the tools work well together to drive business value.
Building a data platform that considers end-to-end processes from storage to analysis is crucial in addressing the challenges of data management. Instead of treating each business process as a separate problem, organizations should focus on integrating capabilities that work seamlessly together. Data catalogs need to integrate with storage, provide governance controls, and directly integrate with analytics tools to be effective in driving data-driven decision-making. Without a holistic approach to data management, organizations risk being stuck in an endless cycle of buying solutions that do not deliver tangible results.
As the appetite for data-driven transformation and AI continues to grow, organizations must reevaluate how they buy and implement data management technology. Simply buying the latest software in the data management hype cycle without a comprehensive strategy will not lead to sustainable results. The challenge lies in integrating capabilities that work together seamlessly, rather than relying on individual tools to solve specific issues. By building a staging platform that integrates different tools and technologies effectively, organizations can overcome the barriers to becoming truly data-driven and achieve business success.
