Almost a year ago, in the depths of despair after the first 100 days of the COVID-19 pandemic, I wrote a post on how government agencies could consider their options using a framework called Respond, Recover and Reimagine. Now, with greater vaccine availability and mass vaccination by governments – in the US, over 3.5 million per day – it's time for public sector agencies to reassess their situations.

First, let me state the obvious: not many expected the death toll from this virus, and few others expected the huge economic and societal changes that resulted and/or were mandated. It's unclear whether many of these changes will have lasting impact, but some sort of change should be expected as individuals, communities and businesses have realized that there are different ways to interact with each other and with their government.

Digital transformation in government

One critical element of this changed landscape is how decisions are made. The global pandemic has accelerated the need to make decisions more quickly - to respond to citizens and the general public with the speed of the business community. “If I can get my groceries delivered today or tomorrow, why can’t I get a benefit or stimulus check in a similar time frame?”

Digital transformation has been a priority in the private sector for the last decade, but has been slow to take hold in governments. In my opinion, the reason is clear. It's easier to talk about transforming to "improve profits" or to "find new customers" or to “unlock value in business processes." But those reasons don't resonate with public sector agency leaders whose mission is to enhance the quality of life, serve citizens and improve outcomes for the people under their care.

Yet, we know that the same digital transformation approaches can work for government. Advanced analytics, including artificial intelligence and machine learning, can be an integral part of putting the available data to work to support the operational activities of a government agency. Using analytics can augment the existing resources of an agency so critical staff can focus on non-standard tasks and communicating with stakeholders. For example, detecting fraud can be automated by establishing analytically based thresholds for alerts given to caseworkers; once these alerts have been made, expert staff can further investigate and eliminate the potential fraud. The result? Reduced fraud means more funding available for those truly in need.

But that’s just one example of making better decisions in government. Even a better integration of the available data that spans different systems, and even systems across local, regional and state or provincial agencies, have far-reaching ramifications because caseworkers can identify the relevant services that a single individual should be, but is not currently, receiving.

Strong governance for government

What's even more critical for government in a digital transformation journey is transparency, which can lead to enhanced accountability and trust in the decisions being made. Analytics, as part of that journey – if implemented in the right way – can provide transparency into the reasons behind the decisions. For example, if regulators or inspectors general ask why a particular taxpayer’s return was identified as potentially fraudulent, the answer is readily available.

However, we should be cautious. Transparency should be balanced with vital privacy concerns. And other elements of a digital transformation program must be assessed in light of operational efficiency. When it comes to government benefits, for example, anything that is newly implemented must not disrupt current operations. As my colleague, Simon Overton observed, “Removing red tape is one thing. Removing important safeguards and weakening governance is quite another.”

Intelligent decisioning

The ultimate fusion of analytics for decisioning and the governance around that decisioning is an approach that SAS calls “intelligent decisioning”: Driving real-time interactions and automating analytically-driven decisions at scale.

SAS is a leader in this area and offers the full spectrum of components required: Data preparation, data visualization, statistics, machine learning, optimization, and econometrics. In order to better serve their constituents, including the individuals, families and communities that benefit from government services every day, government leaders need to adopt intelligent decisioning. There's no need to be put off by advantages that seem related to profit-oriented businesses. Intelligent decisioning can work for the public sector and, in fact, may be more significant in its ultimate benefit.

If you’d like to learn more about how SAS is already helping government agencies embrace intelligent decisioning – and how we could help your team achieve its digital transformation goals – please check out our new e-book, Intelligent decisioning in government, and reach out to our public sector experts today.

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About Author

Lee Ann Dietz

Global Government and Smart Cities Practice Director

Lee Ann Dietz is an analytics evangelist for transportation and smart cities at SAS. She has almost 25 years experience supporting customers with analytical solutions. Prior to joining SAS in 2012, Lee Ann held various positions with Railinc, DZone, Inc., and SAS. Lee Ann began her career with American Airlines and SABRE, after earning her MBA from the Darden Graduate School of Business at the University of Virginia and BA (Economics) degrees from Stanford University.

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