How to Overcome Your Data Analytics Challenges

Every agency can benefit from an increased use of data analytics, but many feds face challenges in innovating their organizations to adopt widespread data collection and analysis. Improved data analytics will help your agencies gain insight into key policy and management decisions, and it will allow you to effectively deliver on your mission for the American taxpayer.

Data analytics is the science of examining raw data with the purpose of drawing conclusions within that data, and many agencies are starting to see how data analytics can help them accomplish their missions more effectively. In fact, there are few decisions that we make today that aren’t based on data in some way. Citizens use customer feedback data to determine what products to buy or use data in fitness monitors to track their health.

For government, the ramifications are even larger. Data-driven decisions have national security implications. Using analytics, agencies can extract value from their data to detect fraud, reduce crime, and respond to cyberattacks. But as data increases in size and complexity, challenges for government have also increased. Figuring out how to begin using data analytics and ensuring that mission needs drive data analytics efforts are critical issues for government.

For most organizations, the first challenge is organizing existing data. Data resides all over the place in various silos that have different owners. As a result, IT departments struggle to keep track of all that data. Traditionally, IT leaders have dealt with those issues by adding more hardware or even designing custom applications to access and analyze the information. However, this often leads to inefficient operations that drown staff in data. They can’t get access to the information they really need, or they overlook key data sets.

The second challenge is the various structures of data. Because certain formats take longer to analyze than others, IT leaders don’t always know the best way to turn data into insights. Some of the information is easily accessible from relational databases and data warehouses. Meanwhile, some information, like semi-structured or unstructured data is not as easy to access and analyze.

Semi-structured data doesn’t conform to formal data models associated with relational databases. It only contains tags or other markers to help separate certain elements. Take email for example. Email has some structure to it, like timestamps and IP addresses, but most of an email’s data is unstructured content or text that someone has written.

Unstructured data is usually not organized in any predefined manner and is text-heavy. Sometimes, it may contain structured data such as dates, numbers, and facts. But unstructured data is especially difficult to sort through, because most of the information is based on free-flowing content such as blogs. Overall, analyzing unstructured and semi-structured data takes a lot more time and effort to find what’s useful and can be analyzed.

Finally, the third major challenge to data analytics projects is constrained resources -- both in terms of people and finances. Government IT budgets are usually limited. Most available funds are dedicated to hardware needs -- like data storage -- in addition to custom applications.

While data analytics can seem daunting, it can be done. By learning about data analytics opportunities, management strategies, and challenges to implementation, agencies can begin to use data in their everyday decision-making, and increased analytics will help government effectively deliver on their missions.

To learn more about data analytics opportunities, management, challenges and solutions, sign up for GovLoop Academy’s “Overcoming the Challenges of Data Analytics.”

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