Every day, enterprises are amassing mountains of data. In fact, according to IBM, companies have captured more data during the last two years than in the last 2000 years. With data storage capacities ever-increasing and data processing costs dropping dramatically, more companies and industries have access to Big Data than ever before. But, as the saying goes, “It’s not what you have. It’s what you do with what you have that matters.” Thanks to big data analytics, companies can now tap into these vast data resources to maximize marketing impact and gain a clear competitive advantage. Here’s a look at why big data analytics are the key to data driven marketing.
The power to process all data
Data-driven marketing necessitates the collection, integration and analysis of huge amounts of data from both external and internal sources. And today’s companies are processing 1,000 times more data than they did just 10-years ago. Of that data, only 20% is structured and readily analyzable. The other 80% is unstructured data, which, due to its unorganized nature, cannot be analyzed by traditional methods. This means that companies have been making critical marketing decisions based on the analysis of only 20% of available data. Big data analytics platforms allow companies to collect, store and analyze all data, both structured and unstructured. Using big data analysis, companies can find order in the chaos of raw data, discover new relationships, trends and patterns, and gain actionable insights on which to make sound marketing decisions.
The days of archiving data on tape are over. Data-driven marketing requires that massive amounts of data be stored for instant access. Enterprises also need to be able to track a vast array of data sets such as transaction data, customer data, search results, click-throughs, social interactions, and many others. The costs incurred in trying to store, manage and analyze all of this data on costly MPP data processing systems can go well beyond what many businesses can afford. However, thanks to big data platforms that utilize commodity hardware and open source software, the costs of storing, managing and analyzing big data have been substantially reduced. Designed to run on clusters of affordable industry standard commodity servers, as opposed to the high-end proprietary servers, platforms such as Hadoop feature high scalability at affordable prices. As more storage and compute capacity is needed, more commodity servers are employed to provide it. Big Data cloud computing platforms are another viable and potentially even more economical solution as they reduce upfront costs of putting up the infrastructure, giving businesses the flexibility to grow their spend with usage. At the same time these platforms significantly reduce the time to ROI as businesses do not have to wait to put together this infrastructure and instead can start on the analysis right away.
In an effort to reduce the costs of MPP based platforms, many companies segregate their data among various departments such as accounting, sales and marketing. Unfortunately, this practice of “siloing” data prevents marketers from gaining strategic information that can only be gained by integrating and analyzing disparate data. Big data analytics platforms give marketers a distinct advantage by desegregating siloed data.
Analysis in real-time
Traditionally, conventional MPP solutions have done a good job of managing and analyzing massive amounts of stored data. However, they can take days and weeks to perform tasks at times due to limitations on how elastically the infrastructure can expand to the needs of analysis. And in light of today’s formidable data demands, “good” is no longer good enough. Today’s big data analytics platforms can perform important tasks in minutes or hours. And a number of big data open source systems allow data to be analyzed interactively in real time. Fast analysis of data supported by elastic infrastructure that can grow easily with the demands of analysis, is of huge benefit to marketers, Cloud based big data platforms provide the elasticity needed to grow and shrink with the demands of analysis so that analysis is not constrained by capacity. This allows marketers to gain insights and make critical decisions sooner than later, which in today’s competitive world can make all the difference.