Data Analytics

Agencies Need to Analyze Big Data Effectively to Improve Citizen Services

By breaking down silos and embracing emerging technology, government can put data to work.

Jon Mazella is director of state and local government at CDW•G.

As the opioid crisis rages, government agencies have analyzed data to determine patterns and discover where best to allocate resources to meet the resulting public health challenge. By seeing where growth in opioid use is occurring, officials can concentrate emergency health services in those areas or invest in public education for victims and families.

State and local governments strive to improve the lives of citizens in these ways and others, but to fully realize the opportunities to do so, they must leverage data analytics to empower their decision-making. Many would benefit from implementing a Big Data platform that gathers information otherwise locked away in silos, uncovering connections and recognizing trends in real time.

But Big Data analysis faces institutional barriers, including aging infrastructure, insufficient budgets, technical complexity, data governance issues and workforce deficiencies. Developing a digital transformation strategy that employs established and emerging technology can go a long way to addressing these challenges.

Cloud Helps Agencies Better Analyze Data

Cloud computing empowers the collection of information from distant sources, breaking down legacy silos and potentially preparing data for analysis.

Agencies can pull structured data from databases — license registrations, real estate sales, public records — and uncover potential issues before they occur. Correlating data in such records can provide insights into things that are happening in a specific ZIP code, for example, enabling agencies to get ahead of problems. While government might scramble to react to a full-blown crisis like the opioid epidemic, agencies can take proactive measures in other cases to minimize troubling trends revealed by data analysis.

Presenting information in a format that is easily understood is an important part of data governance."

Jon Mazella Director of State and Local Government, CDW•G

Analytics also can provide insights from unstructured data, information found in video surveillance files or measurements from sensors connected to the Internet of Things. Analytics tools can pinpoint critical details in this data and tag it geographically or otherwise to reveal what’s happening on the ground. Officials can then respond to these developments. IoT is providing deeper insights into citizen behavior, particularly in smart cities, allowing government to smooth citizen experiences by, say, adjusting traffic patterns or adding emergency monitoring if necessary.

State and local governments increasingly are assigning responsibilities for data analytics and business intelligence to IT managers. According to a 2018 survey by Gartner, 68 percent of states have at least one full-time employee dedicated to data analysis or business intelligence, making it a big priority for states.

Agencies Can Obtain Insights with AI

Presenting information in a format that is easily understood is an important part of data governance. Machine learning and data visualization tools enhance data analytics by generating insights that clearly depict specific information.

Many cities and states have invested in business intelligence dashboards that display information for easy consumption. For example, Chicago deployed Microsoft BI to establish the Chicago Data Portal at data.cityofchicago.org. Through the portal, members of the public can check the status of service requests via 311 calls, find resources such as vaccination centers, access current or historical information on traffic conditions and crashes, and more.

To sort the incredible amount of data available, agencies will apply AI to data analysis. According to Gartner, “by 2020, in 30 percent of smart city implementations, artificial intelligence (AI) will become a critical feature, up from less than 5 percent today.” The power of AI and machine learning could break down many of the barriers to data analysis and add predictive capabilities.

Globally, we already have seen successful pilots for enhancing citizen mobility, crowd management, disaster preparation and recovery, pollution control and more. Identifying use cases for AI will be an important step for effective data analysis in the next three years

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