Reducing data sprawl and increasing efficiency

The data sprawl challenge: Complex ecosystems, increased risks, and costs

The digitization of work in recent years has caused a surge in the volume and complexity of data, and over time, the data ecosystem begins to exhibit sprawl. This increases data risks and costs while jeopardizing innovation and agility. Data sprawl leads to more complex ecosystems, increased data risks, and increased operating costs for maintaining legacy data environments. While data sprawl can exhibit itself in many ways, it is most often defined in one of two ways.

Data sprawl: The proliferation of data and non-strategic assets generally leads to inaccurate, inconsistent, and low-quality data being leveraged to make business decisions. This coincides with a higher maintenance cost and increased complexity of the data environment.
Technical sprawl: The proliferation of data capabilities and tools across an enterprise generally leads to inoperability between business units and homegrown solutions, and adds to the complexity of supporting business priorities.

Migrating to an integrated platform

Migrating to an integrated platform presents a tremendous opportunity for organizations to take a fresh look at their data ecosystem, evaluate how to construct a platform that simplifies their data ecosystem, and implement leading practices for data provisioning. The platform can provide a common business user experience, as well as reduce cost and technical debt. It can also provide enhanced capability through new tooling and vendor capabilities, drive the standardization of capabilities across business teams, provide elasticity and scalability ready to meet new business demands, and drive cost reduction through active monitoring and reduction of federated technical assets.

The need for a sound data architecture

To capitalize on the benefits and promise of a new platform, an enterprise data architecture must be in place to reasonably ensure business, technology, and data objectives are in alignment. A well-built data architecture connects your data strategy to the business needs, defines relevant services and capabilities, and simplifies and classifies your ecosystem of data assets.

And a critical part of establishing a data architecture is articulating business goals, objectives, and processes that will drive the need for different data and technical capabilities for an organization. Business strategy and goals will drive the requirements and inputs into an organization’s enterprise data strategy, data risk appetite, and data programs. Similarly, business architecture will also help define requirements for technology architecture components such as digital transformation, data storage, and advanced analytics tooling.

The time to act is now

A migration to an integrated platform can be a multiyear journey and may take years to present a significant return on investment. However, it is a golden opportunity to build a cohesive data and technical architecture to meet business needs. Successful implementation of your organization’s data architecture can be achieved through the build-out of enterprise data products that support the provisioning of data from multiple domains across many lines of business. Download our report to learn more.

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