Preface

When I was a freshly minted college graduate, my first job saw me working as a contractor for an intelligence agency, building desktop applications in Visual Basic to connect databases to a visual front end. I’ve been working in this industry for almost two decades, and although the technology has changed, the problem of making sense of the relationships embedded in vast quantities of data has become even more urgent. In today’s marketplace, there’s a real need to be able to understand data quickly, effectively, and elegantly.

More organizations are collecting more data for more purposes every day than a team of analysts could parse in an entire career. It used to be only large government agencies that were handling this volume of data, but now even small-scale companies are gathering information on that enormous scale. Big data is no longer merely the province of government.

The biggest problem facing industry is that too much data is being collected, and most of it is irrelevant. So how do you see the trees in the forest?

Graph visualization is just one of many top-notch tools to identify patterns in this universe of big data, but it’s ideally suited for aiding nonscientists to gain an understanding of what’s going on in their data and to make informed decisions about how to handle it. If you’re not making decisions based on the data, then why bother collecting it in the first place?