In the past decade, most organizations have undergone digital transformations. But digital transformation is never done; it’s a continual process to keep pace with technological change. Recent advances in AI have only accelerated the need for every company to transform its underlying IT infrastructure and operating model.
For data leaders faced with this continual evolution, this means building and evolving a modern data ecosystem that keeps data well-governed and enables teams to effectively use data. A foundational data ecosystem—with a focus on strong data management—is necessary for digital transformation, and especially one that seeks to leverage technologies like AI.
What does effective data management look like in the context of this transformation? It looks like creating a scalable ecosystem that enables teams across an organization to find, access, and use high-quality data. For engineering leaders like myself, here are a few tips for building a scalable, well-managed data ecosystem in the face of digital transformation.
Marty Andolino
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VP of Engineering, Enterprise Data Technology, Capital One.
Distributed data; centralized rules
A scalable data management strategy starts with a centralized data hub—a unified repository that consolidates data across the enterprise. Using a combination of proprietary and off-the-shelf tools, organizations can construct a “front door” to data, enabling secure, real-time access and sharing throughout the organization. This hub acts as the heartbeat of the enterprise data ecosystem, integrating distributed data sources into a standardized, well-governed environment.
From this central hub, data can be securely provisioned to authorized users, published to data lakes for machine learning initiatives, or streamed in real time. All these operations adhere to a unified set of rules, standards, and permissions, ensuring consistency and governance across the enterprise.
Address data governance up front
Before embarking on the journey of data centralization at scale, it is crucial for organizations to establish robust data governance policies. These frameworks define data management standards, ensure compliance with industry regulations, and include measures like tokenization, data quality assurance, schema registration, and case management tools. Strong data governance safeguards data integrity and enables secure data discovery and sharing across the organization.
Automating these governance capabilities ensures data remains well-managed throughout its lifecycle. For example, you can ensure that lineage is generated when data is manipulated in a data pipeline , or that data quality checks are run as part of the conditions of the data pipeline. You can also monitor data for timeliness and alert, monitor, and generate cases when issues arise.
Build data pipelines to and from the centralized hub
Once your centralized hub with embedded standards is in place, you can start building pipelines to and from the hub to enable federated data sharing within the organization. Federated sharing organizes data into units that can be recombined into various streams. For instance, marketing teams can combine transaction histories with demographic data to create highly targeted campaigns and personalized experiences.
These pipelines act as conduits, intelligently routing and combining relevant data to deliver contextualized insights tailored to specific teams. This cross-functional integration also breaks down silos, fostering collaboration and data-driven decision-making across departments such as sales , marketing , engineering, and data science.
Know when, where, and how good your data is
After you’ve got data moving throughout your ecosystem—reaching users through pipelines of federated data sharing—it’s critical to maintain full visibility into those data flows. As data moves through multiple systems, applications and processes, companies should implement robust data lineage and monitoring tools to track data provenance; understand where data is flowing, to whom, when, and for what purpose; and to ensure compliance with data governance policies.
When building a data-driven organization, fostering full transparency and trust in that data is paramount. Further, with data powering the business in so many different ways and places, lineage alone is not sufficient. Providing tools to ensure service level agreements and quality of service are met, data quality is acceptable for use, monitoring and alerting is employed when data is not up to standard, and a mechanism for correction is also critically important.
Don’t forget your “old” data and modernize what you can
While modern ecosystems excel at integrating new data, many companies also have legacy data collected over years or decades. Transforming this data into a format compatible with modern systems involves cleansing, aggregating, and standardizing it. But it’s worth it to undertake this task to ensure historical information is accessible and usable throughout your organization’s data ecosystem. Legacy data can provide a more comprehensive vision of trends over time, or analyze past performance and outcomes to help make informed decisions for the future.
In today’s era of digital disruption, data offers a competitive edge—whether by refining business strategies or fueling AI. A successful digital transformation requires leadership that champions a data-driven culture. This data-centric approach is fueling strategic decision-making today and paving the way for future AI initiatives—the next digital transformation shaping the companies of tomorrow.
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