A logical data fabric lays across an agency’s diverse sources and provides seamless, unified access to data -- whether internal or external, on-premises or cloud-based.
State, local, and educational agencies face particular challenges accessing information quickly and reliably across multiple heterogeneous systems. Determining the appropriate monthly assistance for an individual or family for example, requires agencies to consider the eligibility requirements for multiple, federally funded welfare programs, each with its own independent delivery system. As a result, states often struggle to get the data they need to ensure that accurate and appropriate funds are delivered to those in need and that fraud is reduced.
Artificial intelligence-based applications can help states meet those needs, but such applications still require access to the large amounts of data resident in disparate systems. If the required data is stored in silos, it becomes time-consuming, costly and complex to integrate. This aggregation will hinder AI-based applications’ ability to offer an accurate picture of welfare eligibility, current or prospective spend and limit fraud detection.
In addition to human services agencies, other state and local agencies also require quick and reliable access to data stored across functional silos. To accurately determine current and prospective revenues, spend, budgets and possible fraud for example, state auditors, controllers, and treasurers need to integrate data from multiple state systems with speed and precision.
Similarly, law enforcement agencies must seamlessly access multiple state and county data for a well-integrated criminal justice information system that can help identify criminals upon contact with police, locate crime “hot spots” and allocate resources most efficiently. And state transportation departments need to aggregate both spatial and structured data to maintain road status and maintenance requirements, forecast rolling stock usage and maintenance and allocate weather-dependent resources by gathering spatial data from cameras to run analytics on the best routing for snowplows, for example.
When brought together seamlessly, securely and in real time, data is valuable in each of these use cases. However, integrating data between vastly different on-premises and cloud-based systems, tends to be prohibitively costly, time-consuming and complex, often requiring significant technical expertise.
Modern data integration strategies
Recently, Gartner, Forrester and other analysts have begun touting the benefits of logical, rather than physical, data integration strategies. Instead of physically moving large volumes of data from different databases and cloud systems into a large central data warehouse, they advocate logically connecting to the data as needed, leaving the source data in its original locations.
Analysts credit this approach with providing numerous benefits: It enables real-time access to data, rather than scheduled access supplied to users via batch-oriented delivery; it avoids the cost and complexity of warehousing the data; and it accommodates a large variety of legacy and modern systems, without significant modifications.
State and local agencies are now applying logical data integration and data management solutions, such as a logical data fabric powered by data virtualization to streamline data access. A logical data fabric lays across an agency’s diverse data sources – whether internal or external, on-premises or cloud-based, from one cloud provider or another, legacy or modern – and provides seamless, unified access across all of these sources. This is all accomplished without data consumers needing to know the details about where or how the data is stored.
Data virtualization brings the “logic” to a logical data fabric because it creates views of the needed data and delivers it in real time to any device connected to the logical data fabric, using standard protocols. IT managers can also limit or cut off access to data; audit who has had access to which data for how long; ensure access to data based on clearance levels (e.g. for law enforcement); cancel access to data as necessary; and provide an audit report for all the above.
Logical data fabric for social services agencies
With a logical data fabric powered by data virtualization, social services agencies can set up data feeds from any desired combination of data sources directly to AI applications, without having to construct a data warehouse or cloud-based data lake. This enables them to aggregate data from across multiple, heterogeneous systems, securely, in real time, to assist in date-specific eligibility determination for individuals and families.
Because logical data fabric does not require data replication, it offers states better time-to-value when evaluating their human service populations and greater accuracy in determining eligibility. It also allows them to easily overcome the data privacy issues that are often encountered in social services delivery by reducing the number of copies of personally identifiable information. With a logical data fabric, states can better serve their populations based on need and ensure budgets are allocated precisely, regardless of whether the required data is on-premises or in the cloud.
Logical data fabric and cloud expenses
Due to the architecture of data virtualization, logical data fabric also enables any state or local agency to better manage cloud costs, including even the significant monthly or quarterly input/output charges. Data virtualization minimizes I/O costs by significantly reducing the volume of data traveling across the network. For example, data virtualization employs numerous query optimization techniques to ensure that only the results of a query are delivered rather than the entire datasets required to make the calculation.
By reducing cloud I/O charges and making cloud data more freely available to agencies, data virtualization streamlines the complexity of managing multicloud environments.
Once multicloud management becomes cost effective at scale, agencies can avoid vendor lock in and make the greatest use of their data, wherever it resides. In multicloud environments, a logical data fabric powered by data virtualization tames the chaos by enabling agencies to simultaneously leverage the best features of each provider on behalf of their diverse user base.
Logical data fabric powered by data virtualization is proving to be exceptionally powerful across any state or local government use case where data is distributed. It becomes even more valuable to agencies when this data is fed securely, at scale and in real time to the right application.