Enterprise-level companies work with a large volume of data, which makes their analysis and subsequent decision-making complex. It’s necessary to combine data from diverse sources in order to obtain insights and analyze information about consumers and the market. In this article, we are going to address the four types of data analytics that you can (and should) use in your business.
Descriptive analysis
In a business, this refers to the main metrics within the company. For example, profits and losses in the month, sales made, etc. This data analysis answers the question, “what’s happening now?” Companies can analyze data on the customers ​​of a specific product, the results of campaigns launched, and other pertinent sales info.
Descriptive analysis allows companies to make immediate decisions with a high level of surety since they’re using concrete and up-to-date data. The information coming from this type of analysis is often displayed in graphs and tables, which allows the managers to have a global vision of the monitored data.
Predictive analysis
Predictive analysis has to do with either the probability of an event occurring in the future, the forecast of quantifiable data, or the estimation of a point in time in which something could happen through predictive models.
This type of analysis makes forecasts through probabilities. This is possible thanks to different predictive techniques, which have been honed
in the stock and investment market.
Diagnostic analysis
The next step in complexity of data analysis, diagnostic analysis requires that the necessary tools must be available so that the analyst can delve deeper into the data and isolate the root cause of a problem.
Diagnostic analysis seeks to explain why something occurs. It relates all the data that is available to find patterns of behavior that can show potential outcomes. It is essential to see problems before they happen and to avoid repeating them in the future.
Prescriptive analysis
Prescriptive analysis seeks to answer the question, “what could happen if we take this measure?” Authoritative studies raise hypotheses about possible outcomes of the decisions made by the company. An essential analysis for managers, it helps them to evaluate the best strategy to solve a problem.
Analyzing data is essential to respond to the constant challenges of today’s competitive business world. It’s no longer enough to analyze the events after they have occurred — it’s essential to be up to date with what’s happening at each moment. Monitoring systems are necessary tools in the business world of today because they allow us to analyze to the second what is happening in the company, enabling immediate action — and hopefully bypassing severe consequences.
An excellent example of this is a traffic application that helps you choose the best route home, taking into account the distance of each route, the speed at which one can travel on each road and, crucially, the current traffic restrictions.
While different forms of analysis can provide varying amounts of value to a business, they all have their place.
Processing techniques and data analysis
In addition to the nature of the data that we want to analyze, there are other decisive factors when choosing an analysis technique. In particular, the workload or the potentialities of the system to face the challenges posed by the analysis of extensive data: storage capacity, processing, and analytical latency.
Stream or stream processing is another widely used feature within Big Data analytics, along with video analytics, voice, geo-spatial, natural language, simulation, predictive modeling, optimization, data extraction and, of course, the consultation and generation of reports. When making decisions aiming for the highest value to one’s business, there’s a wide variety of advanced analytic styles to choose from.
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