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Big Data as an evolution of Business Intelligence

by | Jul 12 2018 | Data & AI

Big Data and Business Intelligence… in 1914 what for centuries was the dream of many engineers came true: the Panama Canal. Throughout the 14.000th century, it has allowed more than 4.000 ships to transit a year, loaded with up to XNUMX containers each. However, at the same time that investment in the Canal was recovering, the construction of increasingly large ships that did not fit in it and the growth of world maritime freight traffic, made its expansion necessary.

After years of studies, it was decided to enlarge the Panama Canal. Inaugurated in 2016, the expansion today allows the transit of ships with up to 18.000 containers. Yes OK it was necessary to build new locks, developing new methods to supply them with water, and the depth of the channel had to be enlarged in some areas to allow the passage of deeper vessels, the option of enlarge the existing work and use the infrastructures already built, before other options, such as creating a new canal that would cross Nicaragua.



This example comes to mind before the perception of certain organizations of not needing a Big Data system, because they already have their Business Intelligence system (BI from now on) that gives them what they need. In these cases I dare to ask if they would be interested in not creating Big Data from scratch, but rather extending their current BI with certain new capabilities.

Is Big Data an alternative to BI, and therefore a well-implemented BI represents a barrier to entry to Big Data? I do not think so.

A first way to augment a BI is to add new sources of information whose hidden knowledge is not exploited systematically or in its entirety. This is especially interesting when it comes to unstructured data, such as e-mail, tweets, blogs, websites ... In addition to internal origins so far neglected, either due to their large volume or because their structure requires a Too expensive processing, it is also nowadays more affordable to process data from external sources, which can even be downloaded.

Second, the evolution of technology allows us to deepen the treatments applied to data. We often find that our BI allows us to get satisfactory analyzes, which is a limitation: satisfaction with what we have does not push us to seek what we do not know. In that sense, the predictions that are made in a BI, when they are made, are very limited with respect to what can be done with modern Big Data systems. The application of techniques automatic learning (machine learning) to our data allows us to enrich the knowledge provided by our BI.

And why haven't these data and functionality extensions been made until now? The reason is simple: technology did not allow it.

Nowadays, computing power and the development of telecommunications They allow the execution of algorithms that consume many resources, and that until now were at a theoretical level or were used with small volumes of data. Thanks to computing power, we can do much more in our facilities.

And thanks to the development of telecommunications, we can, on the one hand, collect information from distant and diverse sources, and on the other, resort to distributed processing and storage, either internally or in the cloud. Real time is already applicable in processes in which until recently it was unimaginable.

From these three reflections a possible scheme for the implementation of a Big Data solution emerges:

enrich the sources of information,

Introduce automatic learning,

upload to the cloud.

There are three lines of action that can be undertaken one after the other or in parallel, and in any case independently. And it is possible that only one or two will suit us. Every organization has its needs and its priorities.

Big Data and Business Intelligence

In conclusion, an organization that already has a BI solution will have the possibility to extend it in any of the directions indicated to reach Big Data. And an organization without BI installed will be lucky to create its Big Data from scratch, starting from modern technologies.


If you want more information about the Big Data Panel proposal, visit our page.

Edward Suja

Edward Suja

Eduardo is Big Data Manager in the Project Engineering Area of Panel Sistemas. You can visit his profile at LinkedIn and contact him via e-mail at this address.

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