Overcoming Obstacles to Cross-Agency Data Sharing
The Importance of Cross-Agency Data Sharing for State and Local Government
State and local governments are facing a multitude of complex, interrelated challenges — all at the same time. On top of addressing the everyday needs of their constituents, state governments continue to navigate the community impacts of the pandemic, the opioid epidemic, and the local impacts of climate change. Successfully tackling these issues calls for a comprehensive and collaborative approach, and doing so requires data sharing.
Most state and local governments have some degree of data sharing across agencies, but they lack a uniform approach. When agencies want to share data with each other, they do so on a one-time basis, negotiating directly with one another, without considering how other agencies could also benefit from the data. This approach isn’t scalable, but it’s also not conducive to responding to major public crises.
Cross-agency data sharing can fundamentally transform government operations by empowering non-technical government employees with an easy way to extract impactful insights from data. Data sharing helps organizations demystify data, create deeper engagement with communities, reduce wasted effort, and produce more meaningful results. To achieve this however, perceived obstacles to data access must first be overcome.
Data Sharing Challenges
The biggest obstacles to setting up an impactful data sharing program are typically cultural, not technical. Organizations have varying levels of comfort on providing data.
There may be a fear of releasing data, driven by a weariness of errors, misinterpretation, or misuse of data. Data sharing can become adversarial as stakeholders voice concerns around privacy, security, and liability.
To overcome these objections and secure buy-in across agencies, data sharing champions must ensure proper custody, protection, documentation, and dissemination of data resources and educate potential users about those methods.
Data Sharing Objection #1: “I’m concerned about data quality, security, or liability.”
A formal data governance framework and data trust will instill confidence and help secure buy-in in a number of ways.
First, a data trust helps agencies establish rules for data security, privacy, and confidentiality: what data is shared, how it is shared, who can contribute data, and who can access data. This can give agencies peace of mind about how their data will be used. The data trust is part of the larger data governance framework, which defines the rules that govern data management across the organization. By educating agencies about how data trust and data governance will ensure data quality and security, you’ll instill confidence and drive engagement.
Data Sharing Objection #2: “You don’t understand my priorities, so I won’t see value from this.”
Even when agencies and organizations are working on similar projects or have overlapping missions, it’s natural to be skeptical about a data sharing platform that is designed for use by many organizations. Decision makers may wonder if the platform will be applicable to their specific business case or will really provide value above and beyond their existing systems and applications.
The most successful data sharing platforms will be preceded by an in-depth investigation of potential users’ needs and also have a strategic onboarding program that begins with a deep discussion of their business questions. What insights are they hoping to glean? What gaps or blind spots are they hoping to eliminate? Starting this conversation early will allow a proactive approach to creating solutions that will quickly help users answer their most pressing questions.
Data Sharing Objection #3: “My team can’t invest time in new technology.”
The shift to remote work in 2020 required government employees to adopt new technologies and modify processes, so it’s not surprising that agencies are wary about new platforms. But by incorporating powerful self-service analytics applications into the data sharing platform, we can demonstrate to users that data sharing actually simplifies, not complicates, data analysis.
Instead of waiting for analysts to deliver reports or relying on clunky dashboards, a data sharing environment’s self-service analytics applications eliminate manual slice and dicing and include prebuilt reports and automated analyses. Plus, predictive analytics capabilities allow for even more advanced analyses.
Nevertheless, all data sharing onboarding programs must include user training and provide easy access to future support through on-demand training, how-to videos, and opportunities to network with power users.
