‘Big Data’ is probably one of the biggest buzz words in IT right now. At the same time, it is often a misunderstood discipline, starting with the definition. Big Data is not just about large amounts of data, as you might expect, but it’s also about predicting the future.
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Because what would you say if Big Data enabled you to predict, fairly accurately, when a meeting room is available, how many employees are coming to your facility tomorrow and what their preference for lunch is? Or exactly how long it will take you to get to the office tomorrow, or the best type of transport to use?
But first, let's go back to basics: what is Big Data, and, above all, what isn’t it? Gartner defines it as follows:
'High-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.'
The adjective 'big' actually only points to the aspect of high volumes. And rightly so: we are storing more and more data digitally. To make a comparison: in 2020 we will store fifty times more data than in 2010 and, for the time being, that exponential growth will continue to grow.
But just as important is the tremendous speed with which data becomes available (high velocity) and the various manifestations (or formats) available (high variety). Actually, we should be talking about, big, fast, varied data, but that doesn't sound as good.
There are roughly two ways to analyse data. You can first determine what you want to measure: for example, whether the occupancy level of meeting rooms in an organisation decreased during the financial crisis. You can then search for data in order to answer that question: in this case, you will need a summary of the number of meeting rooms and the number of reservations over the past 15 years.
This is how data helps you find an answer to a question. But the best thing about data is that it can also give an answer to a question that you have not even asked. If you have access to a lot of data, business analytics enables you to go in search of possible correlations between various factors that are available in your datasets. You might discover that it is not the crisis but the percentage of men compared to women in a department that mostly influences the occupancy level of meeting rooms.
If you know how to predict an event from the past (occupancy level of meeting rooms) with data from the past (percentage of men compared to women), then the next step is as logical as it is simple: with today’s data you can describe future patterns. This is also referred to by the term ‘Analytics’; a development that emerged in the consumer market and is focused on marketing and the prediction of consumer behaviour.
During our webinar about Big Data we elaborated on the definitions around Big Data and analytics, and the importance of Big Data for housing management and facility services. Large amounts of data are being stored within Real Estate and Facility Management systems, and that will only continue to grow in the years to come. Additionally, other data sources can be used. What can you gain from this as an organisation? What types of data are relevant, what kind of applications and what is required in this regard?