Types of analysis performed with Big Data

Big data analytics is the process of analyzing large and complex data sources to discover trends, patterns, customer behaviors and market preferences to make better business decisions.  

Basically, 4 types of analysis can be performed by applying Big Data. In the following, we will describe what each of them consists of.

The 4 types of analysis in Data Analytics

Predictive analytics

Also known as Data Science, predictive analytics forecasts future possibilities based on patterns found in the company's data that were analyzed.

Big Data, therefore, is a tool that makes it possible to anticipate market behavior. This allows companies, for example:

• Detect trends to make a reliable estimate of future user demand. For example, streaming and multimedia services analyze our reading, viewing and listening habits to build individualized experiences and launch recommendations. Netflix collects the tastes and interests of its users to forecast how its upcoming series will be received and make decisions about its future projects.
• Optimize business processes in companies. For example, for the parcel delivery company UPS, the use of Big Data led to the optimization of delivery routes, with considerable savings in fuel and a reduction in the delivery time of its packages, which translated into a clear improvement in the quality of life of its employees. Likewise, Amazon's algorithms are able to draw trends to direct the consumer to the product they are looking for from the home screen and thus facilitate the purchasing process.

The importance of predictive analytics in companies will be discussed in the next section. 

Prescriptive analysis

It is performed with the purpose of revealing the possible consequences that an action may cause for the business. The prescriptive analysis facilitates the choice of the most appropriate strategies or those that generate the best results for the company.

Descriptive analysis

This type of analysis provides information on the current situation based on historical data, and is therefore focused on real-time decision making.

It is used, for example, to gather information about the internal organization of a company, allowing to better understand the strengths and weaknesses of its employees, as well as to detect profiles of those who have left or could leave the company. In this way, actions can be designed to promote employee motivation and commitment or to make a more strategic selection of profiles, according to the needs or even shortcomings of the company.

Diagnostic analysis

It is used to examine the results and evolution of certain business actions. In this way, timely adjustments can be made to the strategies being implemented.

The importance of predictive analytics in companies

Predictive analytics helps businesses understand their customers' needs. The programs used to explore their preferences store information to analyze and predict their behaviors.

In addition, predictive analytics allows companies to maximize their efficiency and work proactively, as they have more (and better) information to make faster and more accurate decisions.

The platforms of Big Data are enabling companies to fine-tune inventory management through predictive analytics, which evaluate multiple factors, such as weather patterns, seasonal trends and historical sales data. The goal is to determine what a company needs to have the right stock at the point of sale.

In this regard, Zarathe world's largest clothing retailer, bases its success on its ability to spot new trends as soon as they emerge. The data the company manages comes both from daily inventory and store orders, as well as from customer feedback. To take advantage of this data, Zara has numerous artificial intelligence tools for the automation and management of Big Data.

Key sectors in the implementation of Big Data

Retail. It allows through AI to predict what the demand for the most popular products will be. It can predict trends, optimize prices to gain a competitive advantage and identify potential customers who will be interested.

In this sense, SAIL, the intelligent trade optimization software developed by Gamco, allows to know predictively what goods and/or services to offer to each customer, when to offer them and how to increase sales through machine learning and data analysis. SAIL predicts which products or services to offer to which customers, and how to achieve recruitment or sales using the commercial levers and promotions available.

Finance. Big Data improves efficiency and profitability by monitoring financial market activities. It also provides tools to facilitate data access and risk analysis through AI for decision making.

Big Data has changed the way we combat fraudulent behavior by observing patterns in insurance claims or customer bills. It is therefore possible to detect signs of possible delinquent customers and thus avoid non-payments. 

In this sense, the solution ARM-SaaSGamco's Artificial Intelligence-based predictive models can be implemented in order to execute the best actions aimed at mitigating the effects of non-payment and commercial optimization of a business.

► You may be interested in: How to get more customers and less delinquency with Artificial Intelligence and Big Data 

Health. Improving medical care and treatments, developing new drugs and obtaining vital information based on population patterns.

Industry. It automates production processes thanks also to the advance of robotics. Sensors are being incorporated into manufacturing equipment to monitor machine efficiency. In this way we can anticipate possible errors and determine when the next maintenance will be necessary.

Smart cities. By analyzing Big Data, public services in interconnected cities are being improved.