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.