Data is increasingly important for companies to learn about their operation, optimize it and make decisions for the future. But in order to extract the information they need, they have to treat the data they collect in an appropriate way, for which they need to apply very specific techniques. As the data analyticswhich is the ability to apply quantitative analysis and different treatment technologiessuch as analysis using algorithms, at the information. This is done to find trends in it, as well as solutions to specific problems.
As the amount of data that companies collect and have to process grows, the amount of information they can obtain to speed decision making and anticipate problems. Also to improve processes, income and benefits.
In fact, companies that most skillfully use digital analytics tools and processes achieve results that are 2.5 times better than those that do not, according to IDC. This consultancy also points out that companies are investing heavily in Big Data and analytics functions, which according to VentureBeat has led to data analytics in 2021 already being a market that exceeded 215,000 million dollars.
data analytics tends to be predictive, and opens the door to many improvements and innovations in other technological areas. Among them the iterative refinement of machine learning algorithms that drive a good part of Artificial Intelligence. In addition, it significantly improves Business Intelligence processes and decision making.
For this reason, companies are creating teams in charge of working with data and hiring data managers. They are also putting policies in place for working with data, and investing in bulk data collection solutions from internal and external sources that allow them to work with unstructured, structured, and mixed data. Their goal with this is to derive value from large and growing amounts of data. But just by collecting data they can’t do it.
Generally, the data is collected raw, and without processing it, no valuable information is obtained. It is at this point that data analytics enters the scene, which is responsible for studying the data collected, regardless of its source, to extract useful information from it.
The professionals in charge of obtaining this information are the data analysts, or data analytics engineers. To do this, in addition to examining them, they refine and transform them. They also apply models. Thanks to all this they can find patterns and useful information for the company. Higher-level analysts can also generate dashboards and reports, which can help less-experienced analysts on your team do their jobs.
Types of data analytics
Depending on its level of implementation, data analytics can be classified into four types: descriptive, diagnostic, predictive and prescriptive. The first allow companies and organizations to understand their past. It is responsible for collecting and visualizing historical data to answer questions related to past events and events. Therefore, descriptive analytics is suitable for measuring the result of decisions that have already been made in the company.
The second, diagnostic analytics, offers a basis for discovering what has happened, but also for knowing why it has happened. It is responsible for exploring historical data to identify patterns and dependencies between variables that could explain a certain result.
Predictive analytics uses knowledge of the path opened by descriptive analytics to establish what is likely to happen in the future. So, for example, you can use historical trends to predict how the business will perform after raising the price of a product by 20%. It combines predictive modeling, statistics, data mining, and advanced analytics.
Finally, prescriptive analytics, as its name suggests, uses machine learning to give companies useful recommendations to achieve the results they want to achieve. In addition, it can help with information for a better management of the company, increase sales and obtain more income.
Main data analytics models
Analysts can use several types of models to analyze data to identify patterns and trends. Each one works differently, and offers different results. Therefore, if a company wants to use a model to obtain certain information, it will have to carefully analyze which model is the best one to use. The main ones are the following:
1 – Regression analysis: establishes the relationship between a given set of variables, both dependent and independent. These relationships are used to identify important trends and patterns among the variables. For example, it can be used to correlate social spending with sales revenue to understand the impact of social investments on sales. The information that it allows to obtain can help the management of a company to make decisions related to investments of a social nature.
2 – Monte Carlo simulation: Also known as multiple probability simulation. It is responsible for estimating the possible outcomes of an uncertain event. It offers companies several possible outcomes, along with the probability of each occurring. Many companies use it for risk analysis.
3 – Factor analysis: This technique involves taking a large amount of data and reducing it to a smaller size, which will make it easier to manage and understand. Companies often reduce variables by extracting their common elements and reducing them to a smaller number of factors. It helps uncover patterns that were hidden, and shows how they overlap.
4 – Cohort analysis: With this type of analysis, instead of examining the data as a whole, analysts divide it into related groups to analyze it over a period of time. These groups usually have some common characteristics or experiences during a specific period of time.
5 – Cluster analysis: This type of analysis is responsible for grouping data into groups, so that the elements of one group are similar to each other, but are completely different from those of another group when making a comparison between them. It provides insights from the distribution of data, and can easily help uncover the patterns behind specific anomalies. For example, it can help to establish why there are more complaints in some establishments of a chain than others.
6 – Time series analysis: This type of model studies the characteristics of a variable with respect to time, and identifies trends that can be useful to help predict its behavior in the future. For example, it is used to analyze sales figures and find out, for example, what results can be achieved in the next quarter.
7 – Sentiment analysis: This analysis deals with identifying the emotional tone that exists in a data set. It is used so that companies and organizations can identify what their customers think of a product, service or idea.
