Before You Build Your Own Data Management System
By Tiffany Edmonds
AI has changed what it takes to build software.
Tasks that once required significant development experience can now be accelerated with AI. Developers can use AI to help generate code, create interfaces, troubleshoot problems, structure databases, and develop applications much faster than before. For organizations looking at the cost of an established data management system, that can make building something internally seem much more realistic. And in some cases, it may be.
But building an application is only one part of creating a dependable data management system. Once staff rely on that system every day, the organization also has to maintain, secure, support, update, and improve it over time. For Tribal programs, courts, probation departments, social service agencies, public-sector organizations, and other teams managing operational information, those responsibilities can quickly become substantial.
AI Has Lowered the Barrier to Building Software, but Building is Only the Beginning
The question of whether to build your own data management system is not new. Organizations have always had the option to develop something internally if they had the technical resources to do it. AI simply makes that option more accessible.
A developer can now move from an idea to a working prototype much faster. Smaller teams can accomplish work that previously required considerably more development time. Organizations may also be able to create tools around very specific internal processes.
Those are real advantages. The challenge comes after the first version is built.
Once staff begin depending on a system to manage people, cases, services, documents, applications, eligibility, payments, court activity, program data, reporting, or other information, someone has to take responsibility for everything behind it. That goes well beyond the original code.
Who Owns the System After It Is Built?
One of the biggest questions organizations should ask is not simply: Can we build this?
It is: Who is going to own it five years from now?
An internally developed data management system needs someone to manage updates, troubleshoot problems, respond to users, make changes, maintain documentation, test new functionality, address security concerns, and ensure the system continues working as the organization changes. If the employee or contractor who originally developed the system leaves, someone else needs to understand what was built and why. If a reporting requirement changes, someone has to update the system. If staff identify a workflow that could be improved, someone has to design and test that change. If a technical dependency changes or a vulnerability is identified, someone has to respond.
Security itself also extends well beyond the initial build. The National Institute of Standards and Technology’s Secure Software Development Framework (SSDF) outlines secure development as an ongoing process that includes preparing the organization, protecting software, producing secure software, and responding to vulnerabilities. AI can help with many of those responsibilities, but it does not eliminate them.
What a Strong Data Management System Should Look Like
A good data management system has to do more than work well in a demo. It should support the way an organization actually operates day to day, including the exceptions, changing requirements, different user roles, and reporting needs that come with real programs.
That means the system should be built to handle things like role-based access, historical data, changing workflows, organized documents, consistent data entry, and different views of information for different types of users. Front-line staff may need quick access to individual records and day-to-day tasks, while leadership may be more focused on trends, outcomes, and reporting.
It should also be able to evolve. Staff roles change. Programs add services. Reporting requirements shift. New processes are introduced. A data management system should be able to adapt without requiring the organization to rebuild major parts of it every time something changes.
Reporting is a good example. A strong system should not simply store information and hope it can be pulled together later. Data should be structured in a way that makes reporting reliable and useful from the start. That includes maintaining clear relationships between records, supporting consistent data entry, and making it easier for staff to retrieve the information they need for federal, state, Tribal, grant, or internal reporting.
Support should be part of that picture as well. When a workflow changes, a report needs to be updated, or staff have questions, there should be a clear process for addressing it. A system becomes much more valuable when organizations are not left to figure out every technical issue on their own.
The same principles apply when AI is introduced. AI can help summarize, analyze, or surface information, but it still depends on the quality and organization of the data underneath it. We have written before about why organizations should focus on data quality before AI. The better organized the underlying data is, the more useful tools built on top of it can become.
That is the kind of foundation organizations should be looking for: a system that is organized, adaptable, supportable, and built to keep working as the organization grows and changes.
Experience Is Hard to Build From Scratch
An established data management platform also comes with something that cannot be generated as quickly as code: experience.
When a new organization implements RiteTrack, it is not the first time our team has encountered complicated workflows, reporting requirements, role-based access, historical data migration, multiple departments, or program-specific processes. RiteTrack has been developed and refined over decades of working with Tribal programs, justice organizations, human services agencies, and other public-sector programs. That experience influences more than the features available in the software. It also helps our team understand the questions that need to be asked during implementation, where organizations may need additional flexibility, and how different pieces of information need to work together.
Some organizations use RiteTrack primarily for case management. Others use it more broadly for data management, program tracking, reporting, document management, eligibility, services, financial information, court activity, or other operational processes. Instead of starting with a blank database and deciding how every part of the system should work, organizations can start with an established platform and configure it around their own processes.
The Support Team Is Part of the System
Software comparisons tend to focus heavily on functionality, but functionality is only part of what an organization is choosing. With RiteTrack, customers also have a team behind the platform.
Our team works with organizations during implementation to understand what they need to track, how their current processes work, how staff interact with information, and how RiteTrack should be configured.That relationship continues after implementation. When customers have questions, need changes, want to improve a workflow, add functionality, or rethink how they are managing information, they have a team they can work with. RiteTrack support includes access to an assigned project manager, staff training, data requests, and regular system updates.
With an internally developed system, the organization itself takes on those responsibilities.
That may be a good fit for an organization with the staff, resources, and technical infrastructure to support it long term. But the true investment goes well beyond the time it takes to develop the first version.
Established Does Not Have to Mean Inflexible
One reason organizations may consider building their own data management system is flexibility. They do not want to change the way they operate simply to fit software that was designed for someone else. That is why RiteTrack was designed to be configurable because organizations and programs operate differently. Customers can configure what information they track, how workflows operate, who can access information, how reports are structured, and other areas of the system around their needs. When additional functionality is necessary, our team can also work with customers on customization. That gives organizations the ability to shape the software around their operations without taking on the responsibility of building and maintaining an entire data management platform themselves.
AI May Make a Strong Data Foundation Even More Valuable
As AI becomes easier to integrate into everyday work, organized and reliable data becomes even more useful.
Organizations still need to think about how data is structured, where it is stored, who has access to it, how consistently it is entered, and how staff can use it. We see AI as another tool that can work alongside good data management, not as a replacement for it.
That approach is also behind RiteTrack Assistant, our AI-enabled add-on within RiteTrack. Rather than creating a separate AI tool disconnected from an organization’s data management platform, RiteTrack Assistant works within RiteTrack and respects the user’s existing roles and permissions. It can help users interact with RiteTrack data, summarize case notes, suggest potentially useful dashboard tiles and reports, provide general AI assistance, and escalate unresolved RiteTrack-specific questions to the customer’s project manager.
In other words, the AI is being added to an existing system where the data, user access, workflows, reporting, and support structure are already in place. That relationship between technology and professional expertise is where AI can become genuinely useful.
The Question Is Bigger Than Build Versus Buy
Organizations absolutely can build their own software, and AI will continue making development faster and more accessible.
But deciding whether to build your own data management system requires more than comparing the cost of an established platform with the cost of creating an application. It also means deciding who will maintain it, secure it, document it, improve it, train users on it, preserve institutional knowledge, support staff, respond when something goes wrong, and keep the system useful as the organization changes.
For some organizations, owning all of those responsibilities internally may make sense. Others may prefer to keep their internal teams focused on their programs, operations, and communities while an experienced software partner focuses on the technology supporting that work.
At Handel, we have spent decades developing, implementing, supporting, and improving RiteTrack alongside the organizations that use it. New technology gives us more tools to build with, but it does not replace the experience, support, and long-term work required to turn software into a system an organization can depend on.
Interested in learning more about how an established, configurable platform can support your organization’s data management needs?
