Let’s be honest, “big data” has become a term that marketing and business folks throw around to sound technologically competent. Most of us do not always know what we actually mean when we use the term, and more specifically what big data means for our day-to-day marketing and sales practices.
Big data is used to describe the large, complex data sets – both structured and unstructured – that inundate businesses and the global economy on a daily basis. Think about all of the potential sources of data that marketers and sales professionals are faced with every minute of the day—from web forms and inbound calls to SEO and mobile analytics—today’s businesses are awash with data.
In case you’re wondering why you should care at all about big data, here are some stats to spark your interest:
- Sixty five percent of business executives from global brands say they embrace big data to stay competitive.
- Sixty one percent of CMOs admit that they have a long way to go in using big data properly.
- By 2018, there will be 21 billion internet-connected products.
- Big data spending is expected to reach $114 billion in 2018.
- Smart use of big data is helping cities eliminate $200 billion in wasted energy. For marketers, it’s useful to understand some basic terms when it comes to big data:
- Volume describes the total data collected from all your sources (including business tractions and machine to machine data). Thanks to technologies like Apache Hadoop (an open-source software framework written in Java for distributed storing and processing of large datasets) today’s marketers have unprecedented capabilities for managing volume.
- Velocity: The speed at which data streams are occurring within a system.
- Variety: In the world of omni-channel marketing, marketers have to manage a wide range of data sources: including numeric databases, text documents, audio, image, and videofiles, and much more.
- Variability: It’s important to understand the variability and patterns of change within your marketing and sales data. Managing and predicting peak-driven data loads (for instance, what’s driving a particular trend on Twitter) can be a monumental task for marketers.
- Complexity: Establishing relationships and correlations of data from myriad sources and formats can become incredibly complex.