Big Data Models Most Used in Business

Any data model in Big Data must be designed and developed to meet the needs of the business, and for this, it is necessary to know the objectives and goals of the organization, so that it effectively has the necessary functionalities to facilitate the decision-making process in the company.

Monday, July 12, 2021

What is a data model applied to business?
It is a type of language, an abstract representation of information oriented to talk about the relationships that a company's data have with each other. It makes it possible to describe the type of data that exists, and all the real elements involved in a problem or situation.

You may be interested in: "Consumer Mobility as Data."

There are 6 types of Big Data models that are most commonly used by businesses:

Descriptive Analysis
This is the most widely used model, its objective is to describe or summarize a set of data, thus generating simple summaries on samples and common descriptive statistical measures (measures of central tendency, variability, frequency, position, etc.).

The government sector uses this descriptive model with COVID-19 data to have a summary of cases/deaths, of the population of a particular state infected by the virus.

Exploratory Analysis
The purpose of this model is to examine or explore the data to find relationships between variables that were previously unknown. It is useful for discovering new connections, forming hypotheses, and driving design planning and data collection.

The environmental sector employs this analysis to measure temperature changes over a period of time to explore increased human activity and industrialization while forming relationships from the data.

Inferential Analysis
Inferential models consist of using a small sample of data to infer about a larger population, with the objective of extrapolating and generalizing the information to generate analyses and predictions.

In the commercial sector, it is used to analyze samples and make generalizations about a population, this allows to know which store can be the busiest in a shopping center.

Predictive Analytics
It uses historical or current data to find patterns to make predictions about the future. The accuracy of this model depends on the input variables and different mathematical models.

This type of model has been widely used in political elections, since it requires input variables such as historical data, trends and current data, applying different mathematical models in order to obtain a more accurate prediction about the possible winning candidate.

Causal Analysis
Analyzes the cause and effect of relationships between variables, focusing on finding the cause of a correlation, being applied in randomized studies focused on identifying causality, scientific studies in which the cause of the phenomenon must be extracted and pointed out, finding out the causal relationship between variables, changing one variable and what happens to another.

The pharmaceutical industry uses this model to approve new drugs, conducting randomized control trials in order to test the effect of the drug, and thus measure the results in order to bring the drug to market.

Also see: "Big Data and Business: Answers to unknown questions."

Mechanical Analysis
Aims to understand the exact changes in variables that lead to other changes in several variables, applied in physical or engineering sciences, situations that require high precision and little margin of error, in a way a predictive analysis, but modified to address studies that require high precision and meticulous methodologies for physical or engineering science.

The scientific sector uses mechanistic analysis, involving a precise balance of control and manipulation of variables with very precise measurements of desired outcomes, to simulate a safe and efficient nuclear fusion that delivers energy to a certain region.


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More on this topic

What are Predictive Models and What are they Used for?

July 2021

Predictive models are statistical tools that use machine learning supported by Big Data mining to predict and forecast likely future outcomes with the help of historical and existing data by inputting multiple parameters.

They can be used to predict virtually anything containing existing data, in every sector imaginable, from ratings of any program, a customer's next purchase, credit risks, decision making among others.

How to Measure Pedestrian Flow with Mobility Data?

July 2021

Through solutions based on advanced mobility data analytics and predictive models, it is possible to identify different types of patterns, needs, strategies or even future consumer trends.

There are hundreds of solutions and analyses that can be performed thanks to mobility data, such as forecasting models, tracking and predictive market models, business intelligence, real estate project evaluations, solutions based on geomarketing, probabilistic models, among others.

Big Data Applied to the Port Sector

July 2021

In today's digital age, competition in the port sector has led companies to constantly invest in solutions that help them increase productivity and reduce overall costs, consequently, the demand for advanced solutions, such as maritime data analytics, is growing at an impressive rate among commercial shippers and other end users.

The port industry is a complex network of people, countries, and organizations, including shipowners, authorities, classification societies, cargo traders, oil companies, and other businesses, to name just a few. The need to track economic flows in this global supply chain has driven the industry to keep extensive data records.

Geospatial Data to Optimize Supply Chains

July 2021

Through geospatial data analysis techniques, CentralAmericaData carried out an analysis of five Walmart distribution centers in Florida, United States, with the aim of identifying patterns in the supply chains of these five centers and their relationships with commercial establishments and other logistics complexes in the State.

Through this analysis, whose objective is to show how geospatial data science techniques can be applied to solve problems in the logistics sector, the existing relationships between Walmart distribution centers and their supply sites were identified and characterized, so that different large commercial chains can evaluate and at the same time improve processes in their respective supply chains.

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