This blog post is an excerpt from GovLoop’s recent guide, How You Can Use Data Analytics to Change Government. Download the full guide here.
By now, data analytics has become one of the biggest trends in government. Agencies need it, companies are offering it, and solutions are driven by it. One of the most important aspects of data analytics is its ability to integrate with existing systems, in terms of data sources, applications, infrastructure, as well as security. But it can take a lot of work to reach that level of integration.
Rob Lindsley, Director of Data Analytics for Oracle Public Sector, spoke with GovLoop about ways that organizations can work to make data analytics as user-friendly and flexible as possible. Oracle’s own strategy is focused on providing organizations with a variety of cloud options — allowing for self-service analytics and data visualization — and presenting big data and predictive analysis without a steep learning curve. Simply put, the strategy is to give customers “access to the data they need paired with the analytics they want,” according to Lindsley.
The way government agencies provide analytics to their users is also changing. They can run analytic solutions in their own datacenters; companies that offer the analytics services can deliver them as cloud offerings; or agencies can have third parties run their datacenters. Regardless, the tools or services should be easily available to consumers both inside and outside the organization.
Lindsley described Oracle’s role in government agencies as running the gamut from core business operations like finance and human resources to mission support functionality and beyond. “Oracle has a strong presence in the central operations of many government agencies, but we also have a lot of customers using analytics to accomplish their agency’s core mission in a variety of areas, including transportation, a child services and in defense organizations. They’re using analytics to meet their objectives quickly and more efficiently.”
One of the hallmarks of Oracle’s analytics platform is that it’s offered in both an on-premise deployment model or in the cloud. Users can select which option best suits their needs. For the cloud option, users can choose a public, private or hybrid model. Flexibility is key, because users can easily change between on-premise deployment and cloud deployment as projects develop and agencies’ needs evolve over time. For example, an agency might test a new system in the cloud and then move it to an on-premises datacenter for production deployment. Or they might use cloud infrastructure for public-facing websites and offer improved data transparency.
A variety of data sources are essential to improve flexibility for analytics platforms. Traditional data analytics employs “official” data warehouses or other standard data models, but increasingly government employees need to integrate their own data into the analysis, whether through an Excel spreadsheet or a current roster of government contractors. Oracle delivers self-service capabilities that allow end-users to incorporate a variety of data sources into their analytics.
Agencies are also pushing to instill a culture of data-driven decision making. Both big data analytics and predictive analysis have significant potential for employees when they can be used without a steep learning curve. The government has vast amounts of information on the services it provides, and it also has access to information about external events. An example of an agency successfully harnessing the power of big data analytics is the Department of Transportation (DOT) in a mid-western state. By combining data on traffic patterns, road conditions, safety features, and the weather, this agency can optimize the travel experience, make best use of limited resources and increase the safety of drivers across the state.
Oracle has also helped agencies use existing data to improve current projects and predict future trends. One project involved the U.S. Army, which for more than a decade has worked with Oracle to help identify which units are best suited for particular missions. The process aggregates numerous datasets, including those for staffing, training levels and existing resources. The Army started with straightforward metrics to evaluate unit readiness and then moved toward descriptive analysis, using information about specific capabilities unique to the unit. More recently, the Army is using predictive analysis to determine what types of units will be required in future scenarios.
Lindsley notes these examples highlight the democratization of data access, the usability of modern analytics tools, and easy implementation. Oracle’s strength is solving complex technology challenges, he says. “For government, this means our analytics initiatives can help agencies address a variety of requirements – whether it’s massive data sets, stringent security, or scalability. These examples remind us what can be achieved when we can seamlessly incorporate data analytics into our problem-solving strategies.”