It’s no mystery that there is a massive amount of digital data in the world today. From texts to tweets, financial statements to health records, legal documents to weather-tracking stats, there is more information available to – and created by – citizens than ever. In fact, many data scientists estimate that nearly 90 percent of today’s data was created in just the past few years.
Not only is there simply more data, but there is a wider array of new types of data, many of which are complex and difficult to make sense of. Much of today’s data is not merely traditional business records, but now includes machine and human data. Machine data refers to all of the data created by devices connected to the Internet of Things: RFID tags, cash registers, body cameras, activity trackers, home security webcams and more. But the most prevalent and quickly growing type of data is human data: the texts, tweets, Facebook posts, Instagram pics, emails and voice calls transmitted by humans every day.
The upside of this is that organizations have access to more data that can be used to make better business decisions and improve customer experiences. The downside? Most agency IT infrastructures were not designed to handle the complexity or speed of the data, let alone have advanced analytics in mind. In today’s information-filled world, though, it is imperative that our government capitalizes on all this data, and transforms into a data-driven organization.
But what does it mean to truly be data-driven in a world that is bursting at the seams with this cacophony of information? GovLoop recently sat down with Diana Zavala, DXC Director of Analytics and Data Management, U.S. Public Sector, to explore how public sector agencies can maximize the power of this deluge of data to improve citizen engagement and employee productivity through data sharing and data analytics.
In this Industry Perspective, we will:
- Discuss the pressing need for public sector agencies to be data-driven;
- Explore common challenges to becoming data-driven;
- Demonstrate how to predict the future with analytics;
- Examine how to become a prescriptive analytic powerhouse.
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