The term Big Data is something that has been very fashionable in recent years and it could not be otherwise. Every day, more and more programs receive and manage amounts of information and data from different media: from applications, electronic device sensors, from the web, social networks, etc. The most interesting thing about all this information is that it has several uses that can contribute a lot both at an operational and decision-making level.

So what is the actual definition of Big Data? With this word we refer to a data set of great variety, with increasing volumes and at an increasing speed. In fact, when talking about Big Data, the three Vs are referred to :

Volume : by volume we refer to all the large amount of unstructured data that we should process. The larger this volume, the more complex it will be to process all this data.

Velocity – This V refers to how quickly this data is received. It is not the same if we transmit them to a disk and then to a memory, or directly to a disk. Currently, there are many artificial intelligence products that provide the data in real time.

Variety : refers to the various types of data that we have to process. It is possible to find unstructured data that needs additional processing to facilitate its analysis.

Difference Between Big Data and Business Intelligence

The term BI (Business Intelligence) is often confused or mixed with the term Big Data, but it is important to distinguish the two concepts. When we talk about BI, we also refer to the management and processing of a large number of data, but it is usually distinguished from Big Data mainly by two things:

• the volume and magnitude of data (in Big Data it is greater)
• In BI, internal company information is almost always exploited and a set of structured data is analyzed.

Therefore, in the case of large volumes of data, structured or not, and highly diversified where we need great processing speed, we would be talking about Big Data.

How does Big Data work?

The operation of Big Data can be divided into five macro areas: Data Sources, Integration, Management, Analysis and Delivery or Presentation.

Data Sources:

As we have said before when we talk about Big Data we refer to large volumes of data. Therefore, it is important to identify all the sources and channels from which the information will arrive, such as: social networks, electronic device sensors, web pages, mobile applications, etc.

Integration:

In this phase all this amount of heterogeneous and unstructured information must be processed through a data integration mechanism to ensure that it is all formatted and available for analysts to use.

Management:

Once the data is integrated, it must be stored and the processing requirements of our preference set. To carry out this work there are different solutions that have their servers in the cloud or in the facilities of a company. The cloud system is the most implemented, since it turns out to be the most compatible with different technologies and it is possible to increase its capacity whenever it is needed.

Analysis:

This is the phase that makes the whole process profitable, since it helps us to make important decisions about our business, such as: developing a new product, preparing a new offer, investing in a new market, etc. With this analysis we can provide a competitive advantage to our organization, which will allow us to position ourselves in an increasingly global and competitive market.

Delivery:

Once the analysis is done, we can decide how to consult this valuable information. We can create interactive dashboards, receive detailed reports by email, obtain reports from expert analysts, etc.

Big Data Application Examples

We start from the premise that Big Data can be implemented in any environment and not only in the business world. Its applications are really numerous and can be used for different sectors. Here are some examples:

Applications in the business world:

• Product development
• Improve customer experience
• business forecasts
• machine learning
• Operating efficiency
• Innovation: improving planning and decision making

Applications in other sectors:

•  Improvement of public health: it can be used in the coding of genetic material and access to our medical records can provide important information to determine patterns and improve diagnoses.
• Science and research: CERN uses data from the particle accelerator for its studies on the universe.
• Improved security: for example, the interception of certain conversations to fight terrorism.
• Improvement at the urban level: traffic flow can be improved.

As can be seen, Big Data is a solution with multiple uses and which is a safe bet for not wasting a large amount of information and transforming it to improve decision-making. This is possible thanks to the possibility of analyzing the results and the actions carried out in real time, which allows distinguishing the most successful aspects, or quickly correcting errors and modifying our previously designed strategy.