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As Federal agencies continue AI-enabled data journeys, providing customers with improved access to enterprise data for maximized value demands timely innovation — smart solutions with flexible, scalable, and connected capabilities to unlock the potential of mission data.
“For some time now, we have felt there is more power within our customers’ data,” said Eric Moore, Senior Vice President and Chief Technology Officer of Leidos’ Digital Modernization Sector. While this feeling has fueled innovation in data management and products, government data still holds untapped potential.
“Bringing disparate data sources together was essential, but it hasn’t achieved all the desired results. We needed more analytics capabilities to make sense of so much unstructured data,” Moore explained.
Data management and AI
Data ConnectiV as the accelerant
3. Self-service data infrastructure-as-a- platform: data product owners use a reusable, shared platform to accelerate data product development, data discovery, and data access.
4. Federated computational governance: data product owners form a governing function that defines common practices and standards to ensure interoperability including data semantics and syntax, as well as standards pertaining to security, encryption, sovereignty, and respective regulations and compliance requirements.
2. Data-as-a-product: data product owners use a consistent, secure, discoverable, addressable, trustworthy, and interoperable framework consisting of code, data and metadata, and infrastructure where code is executed, and data is stored.
Domain-oriented, decentralized data ownership: data product owners remain responsible for objective measures of data quality, timeliness, and other Service Level Agreements (SLAs) and Service Level Objectives (SLOs).
Leidos is an Amazon Web Services (AWS) Premier Tier Consulting Partner and has achieved Government Consulting, Healthcare Consulting, Level 1 MSSP Consulting, Migration Consulting, Public Safety Consulting and Security Consulting competencies. In 2024, Leidos announced it has signed a Strategic Collaboration Agreement (SCA) with AWS to accelerate innovation for new and existing customers in both public sector and select commercial markets. Learn more about how Leidos and AWS are accelerating the data mesh revolution with Data ConnectIV at leidos.com/DataConnectiV.
Data ConnectiV — accelerating the power of data
Based on their respective positions working with government enterprise IT, Leidos and AWS have collaborated to develop a solution that provides their customers with better access to, and value from, enterprise data. The collaborative solution is called Data ConnectiV and is an innovative data accelerant that takes agencies beyond centralized, enterprise-shared data lakes to a mesh-enabled environment – a solution designed to enable customers to leverage AI and AI-ready data to unlock the potential of mission data and create new data value streams.
As technology leaders work to maximize the value of government data, they must contend with a combination of technical, organizational, and budgetary hurdles to success, all of which boil down to the issue of technical debt residing in siloed systems and legacy system sprawl.
Data siloes, a long-time problem in government, were somewhat reduced by data lakes, but Moore said even some data lake solutions are becoming dated. Data locked within different lakes has simply created newer forms of siloes and
have not solved the need for enterprise connectivity and
access that is required by today’s AI-powered applications.
Enterprise customers must navigate diverse data ecosystems, technologies, policies, and regulations with various types of technical debt, creating a renewed trend toward data mesh.
No single product can solve every need nor address every scenario, which is why a mesh can connect across organizations, define responsibilities, and enable coordination across separate domain teams and their data products.
Rather than a singular product, a data mesh comprises proven best practices, tools, resources, and operating procedures, all woven together into a cohesive structure to support innovative, secure, and resilient data management. As a complement to centralized data lakes, a data mesh ensures that teams creating and capturing data are also making it accessible and usable.
“The approach becomes, how do we attack problems in a renewed way – to try bringing all the data together but deal
with the fact that there are legacy systems, there is siloed data, and data in different formats, of different quality with different sets of metadata,” Moore said. “We can allow each of those
data owners to start addressing their problem in their domain
and allow the problem to be eaten one bite at a time.”
Beyond the technical challenges, revolutionizing data management often requires a cultural shift that can be difficult
for data owners accustomed to having complete control over their data. Navigating ownership concerns and organizational complexities around them represents the human side of modernization challenges.
“You can have all modern systems and platforms and still
not have data integration, and still not be able to apply full
AI analytics and capabilities across the enterprise, if you don't deal with some of those cultural and organization elements,” Moore said.
A data mesh like Data ConnectiV also helps solve these human complexities by bringing the requisite level of modernization while allowing people to both maintain a degree of control
over their data and easily grant appropriate access to it
when necessary.
Why now is the time for a
DATA MESH
ACCELERANT
Added to the challenge of making sense of huge quantities of data, AI-powered applications are also changing data solution needs around three key areas, according to Shannon Judd, Director, Federal Partner Organization at Amazon Web Services (AWS):
Data quality and volume requirements: AI systems require massive amounts of high-quality and structured data to perform most effectively, leading to a need for more sophisticated data collection, cleaning, and validation processes.
Infrastructure and integration specifications: Organizations looking to optimize AI integration require seamless connectivity between data sources, and cloud-based solutions are growing ever more essential to handling AI workloads.
Security and governance needs: AI applications are creating new data privacy and security considerations that require more robust data governance frameworks to support increasingly complex requirements.
Before federal agencies can successfully adopt an AI-ready enterprise data posture to advance their missions, they must contend with barriers like inconsistent data formats, poor quality data from legacy systems, a lack of metadata standards, and issues around data validation.
“AI hasn't just changed how we use data, but it is revolutionizing the entire data ecosystem. Organizations must now think holistically about their data strategy, considering not just storage and processing, but also quality, accessibility, governance,” Judd said. “This shift has moved data from being a business resource to becoming a critical strategic asset.”
AI is poised to change the data landscape, and while data lakes and other solutions for unifying the data made some improvements, AI has increased the need for connectivity and access.
5. Data value: data consumers gain access to reliable data managed through a consistent framework to enable greater mission value from modern analytics and actionable insights.
“AI hasn't just changed how we use data, but it is revolutionizing the entire data ecosystem."
Shannon Judd, Director, Federal Partner Organization, Amazon Web Services (AWS)
Partnering to accelerate mission data values
Through Data ConnectiV, Leidos and AWS are leveraging their expertise as mission-focused industry leaders in government IT and cloud services to help agencies accelerate at scale through proven best practices and an incremental, low-risk path forward. This approach to data mesh is purpose-built to take data from creation and capture to insight delivery, even amid dynamic operational challenges.
Current trends and data needs are increasingly shifting data management toward cloud-native architectures, and this results-driven collaboration combines Leidos’ decades of knowledge about the government space and AWS’ advanced cloud offerings and persistent investments in innovation and invention. Commercial cloud capabilities are evolving every day, and government technology leaders now expect similar levels of sophistication in emerging, government-focused solutions across all data sensitivity levels and environments.
“A key to success for any mesh capability is a clear understanding of the sensitivities and environments that these data-centric, mission systems must operate in, combined with parity across commercial cloud capabilities and sibling government cloud capabilities at various classification levels. Our deep customer understanding combined with AWS’ cloud parity is core to a powerful partnership,” Moore said.
As the roman numeral V in the Data ConnectiV name represents, the five feature elements to the mesh offering:
The result is a flexible, scalable, and repeatable accelerant for delivering data mesh capabilities as a pragmatic part of enterprise IT modernization. For the government, flexibility is essential. As budgets and even data volumes fluctuate based on current events, technology leaders need the assurance of rapid scalability.
“Customers are attracted to the flexibility of the cloud, and Data ConnectiV allows them to effectively manage growing data volumes, while at the same time being able to access advanced analytics without being bogged down with complex, and costly infrastructures,” Judd said.
If AI applications and solutions continue to evolve at their current speed, the landscape may look very different even six months from now. The pace of innovation requires data solutions that not only work now but are also capable of adapting to fit future needs — whatever they may be.
A solution like Data ConnectiV allows individual teams and departments to maintain ownership while providing controlled access and reducing the time typically wasted on bureaucracy around data access. Ultimately, success is not just about the speed at which the technology itself can be implemented and scaled, but also the speed of adoption.
“We now have a solution designed to enable the scalability of access — you don’t have to worry about who is going to get access or where it’s going to go, because our customers retain control,” Moore said. “Making it easier to navigate internal, organizational challenges is, inherent in the system we have built and should accelerate our ability to bring analytics and AI capabilities to our customers.”
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