Big Data: Transforming trash to treasure

There is no accurate definition of Big Data, but seeing it as just “bigger and more” is an understatement. To put it into context, it is far more data than what you can process using conventional technologies and it is typically measured in petabytes or exabytes. However, even more important aspect than its size is its use: how to make sense of and profit from endless strings of data.


There are three major characteristics of Big Data. While volume is the most obvious one, there are also two remaining attributes: velocity and variety, referring to the speed of processing and to different types of data (properties also known as 3Vs). On top of that, there is also the accuracy with which the data can predict business development – in theory, the more data you analyze, the more precise results you get.




Tricky part of getting valuable insight from Big Data starts with the variety of unstructured items which are often incomplete and hard to access and analyze. But once you succeed at doing so, you can receive answers to questions you didn’t even know you could ask. In fact, out of all data out there, probably more than one third could be useful if targeted appropriately.


These data consist of billions of records of millions of people. Since the volume outgrew conventional solutions, it created space for innovations and new tools. Storing and processing it was no longer efficient and therefore it triggered development of new technologies such as MapReduce and Hadoop. Hadoop has managed to deal with the storing issue and erased problem which would have evolved in the past.




Take an example of Google answering more than 3 billion questions per day. There are two possibilities how to deal with these inquiries: you can either answer and let them lose their value right after that or you can turn them into a valuable input. With the use of big data, the information is no longer seen as day-to-day statistics – it helps to predict user behaviour. If the data is analyzed properly, the results you get become a new source of evidence. The whole concept is still relatively new but already broadly used – you can experience it with LinkedIn suggesting you new connections or Amazon recommending books to buy. This is no coincidence, in fact, this is the science of predictive analytics.


Having a lot of data in itself is not beneficial – data on their own have no value. Actually, they can even drag you down financially as storing and safeguarding it is not free of charge. But once you manage to give meaning to those information, they become crucial. In other words, you have to process data first in order to gain the knowledge and be a step ahead of your customers’ expectations.



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