Have you ever been late for work? Most likely, you have. And it was probably the result of something not going as planned. Traffic was heavier than expected; a road was closed for construction; you had a flat tire (and no spare).
If you think about it, getting to work on time seems easy, but there are a surprising number of things that must go right for that to happen. But you don’t think about each of those steps (until something goes wrong) because you’ve done it so many times and you have a plan – even if you don’t realize it.
Successfully implementing AI in government is like trying to get to work on time. It sounds simple, but it takes planning and coordination to make the journey smooth.
That’s where data management and governance arrive on the scene. In RELI Labs, we are developing a robust data ingestion and management process to streamline access, ensure quality, and enable scalable machine learning and AI activities. By automating pipelines and enforcing governance standards, RELI Labs empowers agencies to build smarter models faster, driving innovation and actionable insights across government domains. We make sure the journey from raw data to reliable insights is smooth, safe and aligned with where you’re trying to go.
Starting With the End in Mind
Before you start out, you need to know where you’re going and what you’re trying to accomplish. Whether you’re detecting fraud, responding to public health threats or improving service levels, you always start by asking: What outcome are we trying to achieve? Only by answering that question can you know what data is needed to get there.
Is Your Data AI-Ready?
You would never drive a car with a flat tire or questionable brakes. Likewise, you shouldn’t build AI on data that’s incomplete, outdated or poorly formatted. But if you don’t take an intentional look at the data you are using, you could get stranded on the (metaphorical) side of the road.
RELI Labs’ ingestion pipelines include built-in validation steps to catch issues early, ensuring that data is clean, complete and ready for AI. These steps include:
- Cleaning and validating data — resolving duplicates, fixing inconsistent formats and checking for missing values.
- Ensuring completeness and accessibility — making sure the data covers what’s needed and is available to the right systems and teams.
- Tracking where data came from and how it’s been used — also known as data lineage, this helps maintain trust and transparency throughout the AI lifecycle.
- Understanding and addressing bias — auditing the data to identify sampling bias or other patterns that could skew results.
- Documenting comprehensive metadata — such as data definitions, sources, update frequency and usage notes, to support better governance and reuse.
This is the work that needs to be done upfront. It’s not glamorous and it takes time. Just like cleaning the windshield and filling up the tank with gas, it may seem like it’s delaying your progress, but it’s impossible to get where you are going without this step.
But even with careful planning, sometimes the data you need isn’t available or is incomplete. That’s when you need to be creative.
For a deeper look at what it truly means to build AI-ready data foundations, read our latest article, AI-Ready Data: The Driving Force Behind Every Successful AI Strategy.
Filling in the Gaps
In a perfect world, we would have enough data, tagged and labeled appropriately, to fill up our AI tanks. But that’s not always the case. Sometimes the amount of data is limited, or it may not even exist. In those cases, we must determine what we have and how we can augment it.
RELI Labs supports creative data augmentation strategies, including synthetic data generation and expert labeling, to overcome real-world data limitations. This includes:
- Real-world data: Actual records such as claims, case histories or scanned images. It reflects real events and is often the most valuable; but it may be messy, incomplete or unlabeled.
- Synthetic data: Artificially generated data used to simulate real-world scenarios or fill gaps in existing datasets. It’s especially useful when real data is scarce, sensitive or needs to be augmented for training purposes.
- Human-labeled data: Data that has been tagged or classified by experts. This is essential when real-world data lacks clear labels or when specific features need to be identified for model training.
To make data even more suitable for machine learning, we often need to “engineer” parts of it by selecting, transforming or creating new variables from raw inputs. For instance, pre-calculating an age from a birthdate might make the learning process easier. This process helps fill in gaps and highlight the most relevant patterns for AI models.
Deciding when to use each type of data requires clear criteria, based on the use case and performance goals. This ensures that data sourcing decisions are intentional and outcome focused, not just defaulting to what is available or convenient.
Domain-Specific AI: Power in Specialization
ChatGPT, Claude and most commercial AI platforms are trained on a staggering amount of data, making them excellent for general purpose tasks. But if you’re dealing with a domain that is very narrow or focused on a particular set of use cases, having all that extra data can give you poor results, diluting the effectiveness of the system for your specific needs.
Leveraging subject matter experts to select and integrate domain-specific data, especially in complex fields like healthcare, security and public policy can make the results more accurate and relevant to your specific needs.
Of course, having the right data is only part of the equation. You also need to make sure it’s handled responsibly.
Follow the Rules of the Road: Secure and Ethical AI Use
Just like obeying speed limits and wearing seatbelts, AI needs to follow the rules too. To ensure that you are protecting your data and that the outputs are compliant and ethical, you need to establish guardrails for your AI.
This involves establishing:
- Encryption and access controls
- Compliance with FISMA, HIPAA and other standards
- Defense against AI-specific threats like data poisoning, model inversion, and prompt injection
These steps cannot be bolted on after you implement your solution. In RELI Labs, we are building processes and methodologies to integrate security and compliance into our data workflows, building AI solutions that meet federal standards from day one. These considerations need to be included from the beginning, establishing a data integrity system that is resilient and transparent.
Real-World Routes We’ve Taken
Here are a few of the data journeys we’ve taken in practice:
- Healthcare Claims Review: Assessing data quality and lineage before training models to detect fraud. Using domain-informed data and Retrieval-Augmented-Generation (RAG), which combines external data sources with AI models, to improve accuracy.
- Airport Security: Cataloging and governing image data used in computer vision models, ensuring it’s ethically sourced and fit for purpose.
- Customer Service AI Agents: Securing and labeling data to train models that summarize case histories and suggest next-best actions, all while keeping humans in control.
These examples show how intentional data management can support real-world impacts across different domains and use cases.
Arriving with Confidence
AI often gets the spotlight. But behind every successful AI implementation is a well-orchestrated data journey. Just like a good commute, it takes:
- Planning: Knowing where you’re going and how to get there
- Coordination: Making sure all systems are working together
- Compliance: Following the rules to keep everyone safe
- Adaptability: Navigating detours and unexpected challenges
When done right, data management isn’t a burden; it’s the infrastructure that makes AI possible.
At RELI, we help agencies complete that data journey every day… safely, securely and in service of the public good.
