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What is Big Data for?


What is Big Data for?

Some time ago I heard that phrase that ” Big data” is like the subject of sex in school: everyone talks about it but few know what it is. I also read repeatedly that the concept of “Big data” is inseparable from the “new hyperconnected world” of people-devices that use, generate and publish data / content through countless applications and social media. Some phone number contact list that we can characterize with the triple “V”: ) much more Volume
2) much more Variety according to its origin and nature (structured and unstructured)
3) much more Speed ​​in its update. From here, we can conceive of “Big data” as the ability to aggregate these colossal, varied, and rapidly changing amounts of data, structure it, and process it for analysis . Thus, through statistical models and techniques, the cause-effect relationships between them in the more or less recent past can be better understood to determine the best decisions in real time or in the more or less immediate future.


If we are talking about relationships with our clients, the scope of application of those decisions would be the strategies, policies and actions to attract, retain and retain clients. In other words, decisions that concern the functions of Betting Email List , Sales, Customer Service and Support, all of which have a direct impact on the life cycle of our customers. Well, once the concept has been reviewed and if we look back 10-15 years in time, we will realize that “Big data” does not differ at all with respect to the situation of the companies that at that time began their journey in the implementation of technologies and methodologies that allow to record, process and analyze the volumes, variety and speed of change of their own data, “Owned data”. It was at the end of the 90s and the beginning of the 2000s when database technologies (datawarehouse), advanced statistical analysis techniques and data mining appeared and little by little, they allowed to develop and commercially exploit customer knowledge models. These models have allowed us, among others, to obtain indicators at the level of each client as vital as the probability of abandonment (to proactively prevent it and extend the average life of clients), the propensity to buy a certain product (to improve the cross-selling and increase the average revenue per customer) or the potential value (for the application of segmented customer service and loyalty policies that allow optimizing costs). I will not deny it. It is clear that this “new hyper-connected world” which it previously avoided is assuming a triple somersault compared to the previous situation . Both in terms of technological, methodological and legal complexity as well as richness, precision and application of the new knowledge that we face. But the primary concept, in those terms of technological and methodological solutions that were needed then and are now required, is entirely identical. And this is so because the underlying need for companies remains exactly the same: they need analytical intelligence for more accurate, real-time decision making. The difference, for the moment, is only in the degree of maturity of these solutions: those of 10-15 years ago, those of the “Owned data”, are perfectly consolidated, while the current ones, those of the “Big data”, have just finished. born and there is almost everything to do and much more, to prove its effectiveness. And, although I do not doubt that this will be the case, at this point, it seems obvious to ask ourselves: are we making the most of our “Owned data”? We must not forget that it is in our data that real, updated, accurate, generally structured, cheap and unique information resides (no competitor has it). Do we know how far we can go with our data to improve our decision-making and have a significant impact on the business results of our company?


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