Data isn't like petrol you can just pump into a car and drive away. You need to process it before you can put it in the tank.
Ever heard of a zettabyte? That's the equivalent of 1,000 exabytes. One exabyte is the equivalent of 1,000 petabytes, which is the number we get when we put together 1,000 terabytes. And one terabyte – as you might already know - is the same as 1,000 gigabytes.
Keeping up so far?
More and more data available
As early as 2012, the IT company EMC commissioned a study showing that the world would produce 40 zettabytes of digital data by 2020. That number is so large it's difficult for umans to comprehend. On average, the amount of data in the world doubles every two years. And, after 2020, if the Internet of things continues even deeper integration into the lives of both people and companies, that development will get an even bigger boost.
At Graydon, we've been working with large volumes of data for years, like the data we use to determine a company's creditworthiness. Now, we're also applying our expertise to profile companies in areas outside of financial risk management. That means we can now predict which companies will grow in the future. That's very valuable information that provides insight into the possibilities open to a company. It allows a company to focus their sales people and make their marketing spend more effective. Some of the new insights we're developing include a company's willingness to move, staff turnover and whether a company may be involved in fraud. Just think of what we could do with that information in the future.
Companies see obstacles
All of this data provides interesting insights that can help companies grow and serve their customers better. Many companies are now seeing all the possibilities that big data offers, as well. Many are saying to themselves, "We need to do something with this." In practice, however, it's not always that easy, because translating a mountain of data into useful insights is no mean feat. How do you actually organise that big data? How do you interpret the information? And - more importantly - what do you do with it? What does all this data say about my company or my customers? These are all important questions that need to be answered before you can reach any insights. I also regularly see managers making frantic attempts to set up an internal big data analytics team. More and more companies are struggling to find enough data scientists with the right expertise.
Importance of data preparation
The fact that so much data is available today doesn't mean that you can use it immediately. I often see this kind of flawed thinking in companies, where they overlook the data preparation step. I like to compare data to oil. Oil is very valuable, but you have to refine it. Otherwise it's just black goo that we can't do much with. For data, that
means the data has to be prepared before it can be used. At Graydon, at least 80 percent of our professionals are continuously working on data preparation. We organise, arrange, and structure all the information that comes to us. Many companies underestimate the importance of that process, but we know the truth: that data is only really worth something if it's been cleaned, structured, and ordered. Then we can compare it to other information and prepare it for use cases. Only then will we be able to perform the right analyses and deliver the data to our customers as customer-ready insights that they can use as raw material in their own refining process.
All of that requires knowledge and a lot of experience. Don't underestimate those aspects when you look at setting up your own data department. Understand the algorithms, find the right specialists, and come to grips with how enormously dynamic the information can be. In data-driven companies, that's what it's all about.
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