True government efficiency comes from building muscle, with data as the protein

Wouldn’t it be nice if you could make government more efficient through mere cuts? But cuts won’t fully cut it. Real gains in government efficiency—national, state, or local—will depend on reinvention by leaders willing to build bold new capabilities beneath the surface that drive efficiency and effectiveness.

It will take investments in technology that enable vastly improved data visibility, faster and smarter government decision-making requiring less specialized human effort, and automation that helps citizens help themselves.

This is widely understood: A recent KPMG survey showed 85% of government and public sector respondents are prioritizing emerging technologies over maintaining legacy ones.

That makes sense, because legacy systems are showing their age. Fragmented workflows, siloed data, and a general lack of digital capabilities hinders progress beyond programmatic cuts.

Consider the problem of fraud—the most extreme form of government inefficiency, seeing as taxpayers get absolutely nothing for their dollar (or negative value, seeing as it stokes further criminal activity). The Government Accountability Office estimates federal government fraud losses to cost U.S. taxpayers somewhere between $233 billion and $521 billion annually from 2018-2022. That’s 3% to 7% of average federal obligations, and even that could be a significant understatement.

Data is at the center of real government efficiency

The key to true government efficiency will be to harness the full potential of data to make better programmatic and policy decisions to deal with fraud waste and abuse as well as to provide better service at a lower price point.

We need to reimagine the very infrastructure that makes government performance and efficiency possible. It’s about reimagining the digital foundation that empowers every mission, from maintaining critical assets to securing our communities.

On the fraud front, identities-management firm Socure estimates that international criminal groups using stolen and fake identities account for 2% to 12% of all incoming applications for government services and/or loans. Businesses can fall prey to fraud also, but their systems are newer, their data architectures better, and their success rates in rooting out bad actors higher.

Combining AI-powered analytics with tools that enable cross-database querying can help governments elevate their fraud-detection games. But it will take investment.

Better planning with data analytics + AI

Data’s importance to government goes far beyond fraud detection and prevention. Policy decisions can reverberate, and making the right call requires a clear understanding of follow-on impacts.

Consider possible federal Medicaid cuts. What are the downstream costs of low-income individuals delaying care and landing in pricey emergency rooms? How will that affect the state and local government outlays that also impact taxpayers?

Data analytics packages paired with AI front ends can let nontechnical staff model various scenarios quickly, enabling better decisions.

That’s just one of countless examples of how rationalized government data and powerful analytics capabilities feeding off that data can benefit the public.

States put data to work for government efficiency

U.S. states already boast such capabilities. Intelligent spend management across sourcing and contracts, procurement, payments, and supplier management has been a focus. For example:

• Pennsylvania uses advanced data analytics to gather financial transactions, grants, contracts, and other siloed information and distill it all into holistic views of spending by type of spend, agency, and vendor. That same system is serving up candidates for internal audits based on hard data, rather than the old way based on manual, subjective processes.
• The Indiana Management Performance Hub’s Transparency Portal leverages data analytics and data visualization tools to deliver detailed data on vendors, expenditures, revenues and assets to state leaders, personnel, government watchdogs, and others to help improve how the state spends its taxpayer dollars.
• California aims to use AI to analyze proposed legislation to make sure it’s not duplicative and to check for revenue or expense impacts on laws already on the books.

Building a framework

Doing such things takes hard work and investment. Agencies and departments need to put together a plan and execute on a number of activities:

1. Create roadmaps
2. Understand their goals and how new technologies and revised processes can achieve them
3. Set priorities and KPIs (related to reduced fraud losses, caseload productivity, cost savings, increased revenues, citizen satisfaction, and so on),
4. Automate where the gains look greatest
5. Iterate as those new systems and processes pave additional lanes for government efficiency

Along the way, they should explore data collaboratives within and outside of government to improve analytical power and outcomes.

No question, governments should cut where there’s fat. But the greater focus should be on building the muscle that powers true government efficiency, and data are the proteins upon which that muscle will be built.