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Big Data & Analytics: Data Treatment

Data Treatment

The era of big data is already here. We’re talking about a reality, not a prediction in a short-medium term. In today’s society the data is information. And we’ve always been taught that information is power. The main question about the data management is: how do I manage the vast amount of data generated daily? And most importantly: how am I sure that my database is composed of 100% reliable data?
It’s needed to be clarified that the level of specificity and contextualization of data generates a clear differentiation in terms of quality. There are remarkable differences between the absolute record of visits on a website (raw data), and recording visits classified based on the time, the geographical area of ??the visitor, etc. (explicit data). From this point, the most important thing is to manage this information in an optimal way to ensure we have a high-quality database.
Data quality is an issue that has increasingly worried to the IT departments of companies. This is achieved by developing a strict quality policy (Data Quality):

The Data Quality industry is experiencing an exponential growth in 2016, and the reason is simple: the today’s speed of data generation is far superior to the ability of human beings to accumulate and classify all of them in an optimized way. In fact, according to Martin Doyle, the following Experian qualitative data statistics are a clear example of this:

When our BI strategy is well planned, when the entire team participating in the data collection and reporting has unified the ultimate goal of the work, and, last but not least, when we have a quality database, with lots of filtered, cleaned and optimized data forming a good raw material on which to build our work, we will have established the culture of data in our company, and we will can benefit from all the descriptive and predictive information that the big data is contributing to the pioneers in the use of BI.
They are already ahead, what are you waiting for?



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