Over the past few years, we have heard big data defined in many, many different ways, and so, it is not surprised there’s so much confusion surrounding the term, because of all the misunderstanding and misperceptions.

How do we define big data?

Gartner defines big data as follows:

“Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.”

In other words when it becomes difficult to store, search, analyse, share etc. a given amount of data using our traditional database management tools, that large and complex datasets are called to be big data.

Big Data structure

Big Data are characterized by its volume, velocity and variety:

  • Volume: big data doesn’t sample; it just observes and tracks what happens (the amount of data).
  • Velocity: big data is often available in real-time (the speed of information generated and flowing into the enterprise).
  • Variety: big data draws from text, images, audio, video; plus it completes missing pieces through data fusion (the kind of data available).

So, big data is a collection of data from traditional and digital sources that represents a source for ongoing discovery and analysis. Some people like to constrain big data to digital inputs like web behaviour and social network interactions; however we can’t exclude traditional data derived from product transaction information, financial records and interaction channels, such as the call center and point-of-sale. All of that is big data.

In defining big data, it’s also important to understand the mix of unstructured and multi-structured data that comprises the volume of information.

Unstructured data comes from information that is not organized or easily interpreted by traditional databases or data models, and typically, it’s text-heavy. Metadata, Twitter tweets, and other social media posts are good examples of unstructured data.

Multi-structured data refers to a variety of data formats and types and can be derived from interactions between people and machines, such as web applications or social networks. A great example is web log data, which includes a combination of text and visual images along with structured data like form or transactional information.

In our days industry leaders are focused on additional V’s in addition to 3Vs from Gartner, such as

  • Variability: Inconsistency of the data set can hamper processes to handle and manage it.
  • Veracity: The quality of captured data can vary greatly, affecting accurate analysis.
  • Visualisation: Once data have been processed, they need to presenting: readable and accessible.
  • Value: The value lies in rigorous analysis of accurate data, and the information and insights this provides.

One thing is clear: every enterprise needs to fully understand big data – what it is to them, what is does for them, what it means to them –and the potential of data-driven marketing, starting today. Don’t wait. Waiting will only delay the inevitable and make it even more difficult to unravel the confusion.

Once you start tackling big data, you’ll learn what you don’t know, and you’ll be inspired to take steps to resolve any problems. Best of all, you can use the insights you gather at each step along the way to start improving your customer engagement strategies; that way, you’ll put big data marketing to work and immediately add more value to both your offline and online interactions.

The past decade’s successful web startups are prime examples of big data used as an enabler of new products and services. For example, by combining a large number of signals from a user’s actions and those of their friends, Facebook has been able to craft a highly personalized user experience and create a new kind of advertising business. It’s no coincidence that the lion’s share of ideas and tools underpinning big data have emerged from Google, Yahoo, Amazon and Facebook.

The emergence of big data into the enterprise brings with it a necessary counterpart: agility. Successfully exploiting the value in big data requires experimentation and exploration. Whether creating new products or looking for ways to gain competitive advantage, the job calls for curiosity and an entrepreneurial outlook.

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