How Organizations Can Leverage the Power of AI in their Data Analytics Projects

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In this special guest feature, Clarke Allen, Senior Director of Strategic Business Initiatives, Qlarion, discusses how organizations can: build and deploy AI and ML models. ensure the results are explainable, and collaborate and speed decision-making. Qlarion is a leading government innovation firm that provides data and analytics solutions to government agencies. A transformative leader, Clarke partners with federal, state, and local government organizations to adopt innovative technologies and services that drive positive outcomes for constituents. Clarke is motivated by helping to make a positive impact on peoples’ lives through data and analytics, and he has nearly three decades of success delivering on this commitment.

Government decisions, particularly in the wake of a crisis, stem from a seemingly simple question: what is the data telling us?

That’s what the state of Virginia asked when deciding how to monitor and respond to its opioid crisis. To answer that question, the Commonwealth launched a data sharing and analytics platform. It compiles previously siloed data from various government agencies, healthcare organizations, community groups, and law enforcement so that stakeholders can make data-driven decisions that support their communities.

This data sharing framework was later adapted to aid in COVID-19 response and recovery. Because Virginia had this framework already in place, the Commonwealth was well positioned to handle this rapidly evolving crisis.

Building an initial framework is just the beginning, though. As states advance in their analytical maturity, many government agencies will look to add artificial intelligence (AI) and machine learning (ML) into the mix. These cutting-edge technologies can help with everything from minimizing repetitive tasks to accelerating time to insights—improvements that can help ensure governments are ready to respond and protect citizens no matter what.

Government organizations looking to leverage the power of AI in their data analytics projects should adhere to these three best practices:

1. Get your data in order

Government preparedness is only as good as the data you have access to, and the same can be said for AI. Large, high-quality data sets are crucial to training AI and ML models. To take advantage of these technologies, governments must standardize and clean the raw data so that it can be ingested by the system. As part of this process, the data and analytics team will also need to ensure that individual privacy rights are protected.

This isn’t just about merging data, but rather performing quality checks to ensure it’s ready for use. Automation can play a crucial role here, as it removes human error from the data collection and organization process.

Data teams should also think about what data exists that they may not be taking advantage of. Because AI can bring together many disparate data sets, governments may be able to glean insights from unusual places. For example, in one project, researchers used Facebook data to understand the aftermath of natural disasters. Similarly, bringing together social media and 911 calls may lead to insights that can increase government preparedness.

2. Emphasize agility and collaboration

Data is the fuel for an AI or ML model, and success is built over time. A pilot program should act as a feedback loop that allows the data team to evaluate initial results, add more datasets, and adjust the model as needed.

Such collaboration is best supported by a platform where everything from data ingestion to model building can happen in one place. Often government teams run into roadblocks when they try to hand off different parts of the project, thanks to disparate tools and processes. Government agencies should consider platforms that bring data prep, experimentation, and production into a seamless workflow, and ones that let users automatically track experiments and code. This will allow for faster deployment of models that can automate repetitive tasks for employees and model public health risks that inch you closer to the gold standard of predictive analytics.

3. Ensure results are explainable

In government, there’s another challenge: making sure any insights that are generated by AI or ML models, as well as the methods that produced them, can be explained to a lay audience. Many people are reluctant to rely on AI and ML for decision making because they don’t understand what went into the model. This is sometimes called the “black box” problem.

One solution is to rely on open source software, which is built on transparency. It not only enables the aforementioned iteration and collaboration, but it makes it easier to explain how you came to particular results. This is particularly important in government, where agencies must not just make quick decisions, but maintain communication and trust with constituents.

In addition, as data scientists participate in the continuous feedback loop and watch the model learn, the results will become more reliable and the practitioners will be more comfortable with the entire AI process.

The bottom line

Government organizations can and should leverage AI in their data analytics projects to improve preparedness. Data is a key ingredient in government decision making, particularly in crisis response where every second counts and lives are at stake. Forward-thinking government organizations rushed to implement or expand upon data sharing frameworks in the wake of COVID-19. As we enter 2021, it’s time to build on that foundation.

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