In 2022, a remarkable 88% of companies chose to boost their investments in data analytics, signaling a paradigm shift where a robust data strategy is no longer considered a luxury but a business imperative.
Forward-thinking federal organizations from the US Citizenship and Immigration Services (USCIS) to the Social Security Administration (SSA) are harnessing the power of big data to do more than just sit on a virtual shelf. They’re using data to make informed business decisions, uncover trends and insights, optimize efficiency and productivity, and reduce silos between federal agencies.
Private companies perform data analytics to drive revenue and for other reasons, usually related to profits. The federal landscape, however, is different. Yet, government agencies can still harness the same type of power and receive tangible benefits.
- Allocate resources to where they are best utilized
- Leverage data to identify inefficiencies to correct them
- Improve service delivery and public safety
- Help leaders improve public policy decision-making with current data
Importance of data
Ninety percent of executives agree it’s important to increase data analysis within the next few years, and many government organizations will face a reckoning when building a strong data analytics strategy from the ground up.
However, without a major shift, government agencies risk poor decision-making, missed opportunities, and decreased consumer trust. Here are five key components to consider when building a government data analytics strategy.
1A Data-Driven Culture
Only 27% of organizations identify as data-driven, and this number is most likely even less when it comes to government agencies. Indeed, federal offices have significant opportunities to reform their culture into something more analytical. A data-driven culture starts with leadership buy-in and trickles down across human resources, marketing, and operations. This might include increasing collaboration across departments, making data more accessible and visible to the average government employee, and encouraging teams to rely on this information to assess a project’s successes and failures.
One example of a data-driven culture is the USCIS. The USCIS processes over 8 million applications annually in partnership with the Department of Homeland Security and U.S. Customs and Border Protection. Without a data-driven culture across all teams, it would be impossible to accurately analyze, store, and share all immigration data.
Pros:
- More-evidence based decision-making
- Improved employee accountability
Cons:
- Reduced creativity in problem-solving
- Overreliance on data
2Continuous Process Optimization
Great data analytics strategies are constantly optimized and finessed. As more data comes in, federal organizations are able to refine and standardize their processes. Once teams are comfortable using data to drive decision-making, you’re able to make even more incremental process improvements along the way.
For example, the National Institute of Health (NIH) is evolving its current data nalytics strategy after realizing the areas of machine learning and artificial intelligence were vastly under-resourced. They’re continually optimizing their data processes after discovering the disproportional number of researchers and communities represented.
Pros:
- Incremental improvements in day-to-day efforts
- Small tweaks add up to significant changes over a long period of time
Cons:
- Focus is more on processes than action
3Data Hygiene
According to Gartner, bad data costs organizations an average of $12.9 million per year. When it comes to keeping clean and accurate databases, it’s a team effort to ensure best practices across government agencies. This means automating data cleansing, updating as frequently as possible, removing unnecessary clutter, and reducing organization silos.
With regard to data hygiene, the Department of Defense struggled with consolidating financial management systems due to inconsistent and poor data. But, through proper data cleansing, they produced accurate financial budgets and ensured their compliance with regulatory systems.
Pros:
- Increases overall productivity
- Allows for the highest level of confidence in data-driven decisions
- Allows for consumer segmentation and personalization
Cons:
- Data cleaning technology might mishandle information
4Integrated Technology
Furthermore, one of the fastest ways to create messy data is through applications and software that don’t sync in real time. Popular enterprise analytics tools that work together are Snowflake, Tableau, Microsoft Power BI, Google Analytics, and more.
As a real-life example, during COVID-19, government agencies quickly adopted integrated technology to sync public health data in real time. Health-focused organizations could quickly uncover trends in exposure, identify outbreaks, and provide better support to citizens.
Pros:
- Reduced room for manual, human error
- Faster data access
Cons:
- Legacy systems might not work in conjunction with newer technology
- Complex integrations
5Focus on Scale
The next stage of a successful data analytics strategy is scale. Once your government organization has mastered clean data at a micro level, you can confidently expand across teams and departments. For example, the Social Security Administration currently uses a big data strategy to identify suspected fraudulent claims. Utilizing a big data strategy empowers them to better analyze “massive amounts of unstructured data” with disability claims. It also enables them to better process claims and speed up the decision-making process. The federal agency began its huge push to improve back in 2013 by thinking outside the box and utilizing open-source platforms, increasing its processing capabilities 60 times faster.
Pros:
- Comprehensive business-wide insights
- Ability to speedily process large amounts of data
Cons:
- Resource-intensive and technically complex
- Increased likelihood of data quality concerns
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