The 5 applications of Big Data

Digital Performance Management
5 aplicaciones del Big Data

5 applications of Big Data

Before learning more about the 5 applications of Big Data and the operation and characteristics of Big Data technologies, it is key for us to know the possible main problems of each company to which Big Data can provide a solution.

Below, we detail the 5 applications of Big Data:

  1. The exploration of big data:

For large companies it is necessary to be able to navigate easily both within the company’s systems and the data that comes from outside. Therefore, the 3 “V” of Big Data (speed, volume and variety) represent the challenge that companies face when making the best decisions, improve their operations and reduce risks.

The increase in “noise” or “gross data” makes us wonder how we can contextualize this data to perform a better analysis and make more limited decisions to our goal. The exploration of data reduces the risk of leakage of confidential information thanks to its security mechanisms

2. 360º vision about the client:

In order to achieve total customer knowledge, each company must obtain information from internal and external sources. In this way, we can advise you to understand the client’s behavior and predict your future actions.

Employees who work with other clients must have the necessary information to establish a relationship of trust that a commitment on the part of the consumer. To achieve this the employee must be able to have internal information (according to the client’s behavior with other experiences of the company), as well as externally (analyzing their tastes and interests obtained in social networks, email, among other sources of information).

3. Security/intelligence extension:

The 3rd applications of big data is they are mechanisms to locate anomalies and prevent attacks. They are solutions that make it possible to discern between massive amounts of data (both internal and external) possible hidden relationships, patterns of behavior that can be detected and prevent security threats. It also offers the possibility of discovering a fraud by checking in real time the activity history of an account, which is possible to unmask an abnormal behavior or a suspicious transaction.

The three main applications are:

  1. Improved intelligence and surveillance vision: analysis of data in motion and at rest to find associations or discover patterns.
  2. Predicting and mitigating cyber attacks in real time: by analyzing network traffic, companies can discover new threats and prevent digital attacks.
  3. Prediction and crime prevention: the ability to analyze data from the telecommunications network and social networks allows detecting threats and preempting criminals before they act.

4. Operations Analysis:

This analysis allows us to obtain real-time visibility of operations, customer experience, transactions and behavior. With a data accelerator you can ingest and process large volumes of data to provide detailed knowledge of the company’s status. The machine data can be correlated with other company data such as customer or product information, although the large volume of data is in different formats that, without the solution, are not compatible with the others.

This combination is very useful for those responsible for making operational decisions, while increasing the intelligence and efficiency of operations. These decision makers can visualize the data through different systems to obtain the most informed vision possible and to react quickly to any unforeseen event.

5. Increase the data warehouse or Data Warehouse:

Another applications of big data is the aim is to expand an existing data storage structure by applying the advantages of Big Data to increase its value. The increase in the Data Warehouse is based on two basic needs: take advantage of different types of data to gain new business perspectives in real time, and to optimize the data storage structure, facilitating the task and saving costs. There are three types of Data Warehouse:

  1. Pre-Processing Hub (pre-processing core): Provides a mounting area or “landing zone” for the data before deciding which ones are incorporated into the data warehouse.
  2. Discovery / Analytics (discovery-analysis): gives the ability to perform analyzes that should have been done before in the Data Warehouse, in order to optimize the data warehouse and enable new types of analysis.
  3. Query-able Data Store – downloads data that is queried infrequently or from a considerable age in the data warehouse using software and information integration tools, and stores them in a low-cost storage space, but keeping them still accessible from the solution.