Big Data has revolutionized how companies make decisions. Therefore, data models in Big Data must be designed and developed to meet the needs of companies and adapt to their objectives. How can you extract the maximum benefit from the vast amount of data we have? It is more than just a question of mass storage without meaning. To extract value, it is necessary to be very clear about what information is valuable and, above all, to be clear about the objectives of the analysis.
Discover the most used Big Data models
Big Data is based on handling large volumes of data, which can come from various information sources. Given the amount of data, its structuring and order are significant to achieve maximum benefits for companies. For this reason, they are classified into structured, unstructured, and semi-structured data. This classification has great informative value and is decisive when making decisions. These are the Big Data models most used by companies:
Descriptive data analysis
The descriptive data analysis model is the most used by companies. Its objective is to describe a data set and create simple summaries of statistical samples. These models allow you to analyze historical data and have a more accurate and orderly view. Use tools such as business intelligence, statistical analysis or data mining. It simplifies and summarizes the data to have a context with which to analyze and understand it.
Through descriptive analysis, it is possible to have a more precise and orderly vision. It lets us know how the company is at a specific moment, consulting different business indicators. It gives an idea, in short, of what has happened and what is happening. It is, for example, the type of analysis used by health authorities to reduce the spread of COVID-19.
Exploratory data analysis
This model examines and explores databases and summarizes their main features. It allows for finding relationships between variables, discovering patterns or anomalies, and formulating hypotheses. This helps drive design planning and data collection. In the same way, it allows for determining if the statistical techniques used for data analysis are appropriate.
Exploratory data analysis is essential because it makes it possible to identify apparent errors. But at the same time, it also helps to better understand the patterns in the data. Once helpful information is extracted, its features can be used for more complex data modelling, including machine learning. In this case, the most common data science tools for this type of analysis are Python and R.
Inferential data statistics
Inferential data models use a small sample of data to make inferences about a larger population. Thus, information is extrapolated and generalized to generate different analyses and predictions. But you have to be aware that, being a probabilistic calculation, it has a certain margin of error.
There are two main types of inferential statistics: Hypothesis tests (validate conclusions drawn from a segment of data) and Confidence intervals (random values used to identify margins of error). These models are frequently used in the commercial sector to generalize a population. In this way, competitive advantages or business opportunities can be identified.
Predictive data analysis
Predictive data analytics process data to find patterns that are useful in the future. It uses statistical modelling techniques, big data and machine learning to extract historical data and make forecasts. These tools offer different scenarios or forecasts of future customer behaviour based on probabilities.
Thanks to big data, therefore, the data obtained can be interpreted to get predictions. These will help predict how a person or population group will behave, which has many applications in business. This model can help predict a new product’s impact, obtain sales forecasts or avoid problems in the supply chain. The objective of this type of analysis is, ultimately, to answer the question of: “what will happen?”. Its application goes from electronic commerce to finance, energy or insurance, among other sectors.
Causal data analysis
This is the analysis that examines the cause-and-effect relationships between variables. It focuses, then, on finding the causes of the correlations. When aiming to identify causation, your main challenge is finding good data. Generally, causal analysis helps to understand the reasons why things happen. For example, it can help to understand why a business variable has not worked.
Causal data analysis is widespread in the pharmaceutical industry to study the causes of problems and successes in clinical trials. Also, in the IT industry, it is used to check the quality assurance of specific software.
A company that wants to be successful must have the necessary information that allows it to know everything that affects its business. So you can turn that information into an opportunity and transform it into benefits. Several big data models can be used to make the best decisions, and they, in addition, can be combined with each other. Its use depends on the data available to companies and the technical capacity they have. The challenge that companies have is, in all cases, to be able to extract the full potential of their data.
