Too many legacy systems. It’s something we hear from government agencies all the time. And you’ve heard this complaint too, no doubt. But the risk-averse nature of government organizations combined with a fear of disruption makes them reluctant to take on a wholesale modernization effort.
What if they don’t have to, though? What if there’s a better way?
[ Get the guide, Modernization in State and Local Governments, to learn how the path to modern technology is evolving. ]
The problem of siloed data.
Legacy systems mean siloed data, and nowhere is this problem more pronounced than in state government IT solutions. Legacy systems are typically designed with limited interoperability, leading to data fragmentation across different departments or functions within an organization.
In state government IT solutions, siloed data gives rise to several critical challenges:
Limited data visibility and accessibility hinders efficient coordination of effort and information sharing. This lack of cohesion often leads to inefficiencies, delays, and difficulties in resolving issues promptly.
Reliance on disparate sources that do not synchronize contributes to inaccuracies and inconsistencies in information, which leads to errors in reporting, flawed decision-making, and potentially impacted citizen services.
Inefficiencies in data management caused by data silos result in increased operational costs and hinder the overall speed and effectiveness of state agencies.
Breaking down these data silos is crucial for organizations to harness the full potential of their data. What’s more, without strong data connectivity, organizations can’t benefit from automated workflows that need data access to run without human intervention.
Government organizations need to transition to more modern and interconnected solutions to foster agility, efficiency, and data-driven decision-making.
So you know there’s a substantial cost to leaving your data in silos. But ripping and replacing all your legacy systems might feel totally unrealistic. If that’s your situation, data fabric is going to become your best friend.
[ Learn more about what next-generation state government applications look like and why COTS is not the answer. ]
The solution? Data fabric.
Unify and extend your legacy systems with a data fabric.
Legacy systems at state governments are not going away anytime soon. But it’s now possible to access your data across legacy systems using a data fabric—defined by Gartner as “a design concept that serves as an integrated layer (fabric) of data and connecting processes.”
A data fabric connects data across systems to create a centralized view without needing to move data from where it currently lives. This means that applications that use a data fabric work seamlessly with your legacy systems and allow you to build new capabilities on top of existing data structures. Using such next-generation applications lets you modernize parts of your business without needing to rip and replace existing systems. Instead, it lets you keep your existing systems and upgrade or replace them over time.
Appian’s patented data fabric unifies data sources across your enterprise without requiring you to migrate data, unless you want to. It enables automated workflow elements to access consistent, accurate, and compliant data from anywhere, at any point in a process. In this way, the Appian Platform seamlessly bridges the gap between old and new systems. By connecting all the systems in your environment, Appian empowers your organization to build flexible, integrated applications that extend the life of your systems.
This power to connect disparate data sets means that data is no longer hidden in silos, giving you a complete view of your data, allowing your organization to become truly data-driven. A data fabric is the best data management option for organizations that want to build real-time applications with a 360-degree view of their operations.
Told you there was a better way.
Read the Data Fabric Guide for Public Sector Leaders and start unifying your organization with connected data.
