In an attempt to become more customer-centric, an increasing number of companies are embracing a data-driven approach. And, thanks to continuing advances in big data technology, there’s a huge amount of it right at our fingertips.
In fact, according to Forbes, “data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain those customers, and 19 times as likely to be profitable as a result.” It seems big data has become an invaluable tool for creating value in a business, eliminating a reliance on ‘gut feel’ decision-making.
You may have heard these numbers before. Whether it was in a clickbait LinkedIn article or a boardroom meeting, the push to become more data-driven in our day to day work has become quite overwhelming.
But, do we really know what it means? What even is big data? How do we use it to become data-driven? And, more importantly, how does it mean we’ll all suddenly smash our goals and become incredible at our jobs?
Welcome to our big data blog series, where we’ll tackle these concerns head-on. So, let’s get started with the basics.
What is big data?
If we look at Wikipedia (because let’s face it, we all do), big data is described as “data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them.”
Sounds pretty overwhelming, right?
Don’t worry, it’s not as bad as it sounds.
Big data is not out to confuse us. The concept was actually born out of a desire to understand trends, preferences, and patterns in the huge databases that are generated whenever users interact with different systems, particularly online.
Big data: A brief history
Even back in the 1950s, decades before anyone uttered the term ‘big data’, businesses were using basic analytics (essentially manually examining numbers in a spreadsheet) to uncover insights and trends.
The term itself has been around since the 1990s and was made popular by US computer scientist, John Mashey. As you can imagine, the size of what is considered ‘big’ has changed significantly since then, and nowadays big data can range from a few dozen terabytes (1000 gigabytes) to many petabytes (1000 terabytes) of data.
In 2001, META Group (now Gartner) identified data growth challenges and opportunities as being three-dimensional. This means big data should have one or more of the following characteristics:
- Volume (amount of data)
- Velocity (speed of data in and out)
- Variety (range of data types and sources)
So, in 2012, the definition was updated to include, “Big Data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
Quite a mouthful, right?
Basically, the demands of big data encouraged the development of a whole bunch of tools and processes (also known as big data analytics) to help organizations analyze the immense data sets they are faced with. This means they can find out more about their customers which, in turn, helps them to develop new experiences, services, and products.
As IBM summarizes, “Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources to gain new insights resulting in better and faster decisions.”
So, what can I do with big data?
According to SAS, “it’s not the amount of data that’s important. It’s what organizations do with the data that matters.”
More recently big data has come to be associated with the advanced data analysis methods that extract value from data, rather than the size of a dataset itself.
So, in what ways can we use big data to make smart and valuable changes?
Unfortunately that’s a whole new blog post in itself. Keep your eyes peeled for the next post in this series, where we’ll continue to break down big data into small, manageable chunks.