Have you ever looked up at the night sky and connected the stars to form constellations? It’s easy to do when stars are perceived to be the same distance away. But what if you fly out beyond the stars? The easy-to-distinguish constellations become unrecognizable, just as the interpretation of data can change without proper context. Without context, data is useless, and any visualization you create with it will be useless too.
Using data without context is like citing an abridged quote as a main discussion point in an essay, only to find out later that the speaker meant the opposite of what you thought. To truly understand the huge amounts of data collected, you need to know the metadata, or the data about the data. You need to know what to look for in your data, what to do with it, and what tools to use.
Big data is not a new-age solution, it merely refers to the extremely large pools of data that companies have stored today. However, the difference with big data is how quickly it can be analyzed. In the past, surveys and their processing would take weeks. Today, aggregating social media data, search data, and other forms of big data offer real-time results. The ability to react to the market and make decisions has changed dramatically.
To start understanding data, it is important to ensure data quality, measure success on metrics that matter, be active, know the three basic challenges, and embrace virtualization. Poor data quality means higher marketing costs and the potential to misunderstand the true profile of an important customer. Working from hypothesis to test to conclusion ensures success on relevant metrics. Data should not be static and should instead lead to an immediate course of action. The three basic challenges of big data are storing, processing, and managing it effectively. Virtualization gives end-users flexibility, lower costs, and freedom from IT vendor lock-in.
Big data isn’t necessarily better, but it is different. To unlock its full potential, we need to make serious changes to how we think, manage, and operate. By using the right tools and strategies, we can leverage big data to find patterns that correlate with real-world phenomena and gain insights into our companies.
