In the age of online shopping, marketing automation, point-of-sale computing and other instant customer feedback, Big Data has become the recent buzz word, touted as the be-all and end-all answer to the marketer’s prayers -but what is Big Data really?
First let’s define Big Data. According to a Wikipedia, the term Big Data can be applied to a dataset “whose size is beyond the ability of commonly used software tools to capture, manage, and process within a tolerable elapsed time,” or a dataset that can only be processed or mined using the power of multiple computers simultaneously. Many people have simplified big data to mean large databases of millions and millions of records, but this is not strictly true. The actual threshold of what constitutes Big Data is a moving target though because data-handling abilities can differ from organization to organization – What might be considered Big Data by Dunkin’s could be easily crunched by – say – the human genome project. In general though, we are talking about datasets of enormous magnitude, and the method in which we handle or process that data.
In today’s world where every device is generating millions of bits of information, there are many opportunities for organizations to make use of these huge distributed data sets. For marketers, this kind of data gives us actionable information about how best to get our product into customers’ hands. Tasks like A/B testing (e.g. which email message results in higher sales?), association rule-learning (e.g. a customer who buys onions with potatoes is more likely to buy hamburger) and lead-scoring (what combination of clicks and downloads is most likely to lead to a sale) are just a few examples of the kind of answers Big Data can provide.
But with opportunity comes challenge. Not only is the size of big data daunting (we’re talking about databases on the order of exabyte and petabytes) but also the velocity of the data being input and output is immense and hard to handle. Additionally, variances in the data structure (pre-parsed structured data versus random bits of unstructured data) need to be managed to best make use of your data. Across different industries Big Data creates a competitive advantage and large value propositions; Simple statistics – Larger data wins every time. Analysis and cross referencing of large data sets provides insights and advantages which would otherwise be impossible to obtain.
Telling the Story of Big Data: Visualization
For some really cool examples of data visualization and how it can help to understand vast resources od data check out this blog on webdesignerdepot. http://www.webdesignerdepot.com/2009/06/50-great-examples-of-data-visualization/
Investments in designing and implementing distributed marketing analysis systems to better understand your customers, prospects and suppliers can pay off quickly. By interlinking the many sources of data about these individuals throughout the enterprise, you can: better target marketing campaigns to increase conversion rates and reduces marketing costs; Predict which item a customer will need next; or determine with predictive modeling how the supply and demand parts of your business intersect and streamline the supply chain and cash flows. In short, for companies that that decide to use them, solving these Big Data problems can generate significant cost savings and provide a serious competitive advantage.