So what really is “Big Data” anyway? If you do any online searching you will find a number of slightly different definitions for this seemingly new phenomenon.
If you do any online searching you will find a number of slightly different definitions for this seemingly new phenomenon. Much of this is because it is a relatively new term for a concept that has been around for a while. In general “Big Data” is the term used for the collection of data sets so large and complex that they become difficult to process using traditional data processing applications. Although this may sound very IT orientated in nature, Big Data is being used across many industries and job functions for a multitude of reasons, from helping to better predict changes in customer demand to determining the optimal location to build a distribution center and even to better determine the cause of health ailments such as cancer.
Very simply, in today’s world we have access to more data than ever before but unless we are able to mine, analyze and interpret it effectively it is essentially useless. So how do we even begin this overwhelming task of knowing what data to look at and be able to turn it into actionable strategies?
First and foremost is the ability to collect data. There are many software tools on the market today that have historically been used as part of advanced analytics disciplines, such as predictive analytics and data mining. However, the collection of Big Data may include unstructured data sources, which are typically text heavy but may contain data such as dates, numbers, and facts as well. Due to the lack of traditional data architecture unstructured data sources used for Big Data analytics may not fit in traditional data warehouses. Furthermore, traditional data warehouses may not be able to handle the processing demands posed by Big Data. As a result, a new class of Big Data technology has emerged and is being used in many Big Data analytics environments.
The second step is to perform a value analysis on the data you are collecting. If you analyze the data more closely, which data sets have the potential to provide business advantages? What data could potentially give you a competitive advantage or help you better serve your customers? Once the data is organized and prioritized, you’ll be able to better decide which data sets you’d like to begin to analyze.
Finally, even though Big Data can seem very robotic, it still requires a human element to be able to effectively analyze the data and turn it into actionable business strategies. Building the right team of people is the biggest step your company can take towards conquering Big Data. However, in many cases this is much easier said than done.