What is data analytics?
The use of the Internet and other communication systems and technologies (ICT) generates huge amounts of data of all kinds, such as the type of data packets that travel through networks, their route and frequency. In many cases, the cadence of keystrokes on a keyboard, the applications that a user runs and the websites that they visit can be detected.
Advanced Data Analytics and Machine Learning systems use this abundance of Big Data for various purposes:
Customer Data Management
Personalization of the user experience
Cybersecurity and anomaly detection
Fraud detection
Risk management
Operations analysis
Conducting market research
If we want to find a good definition of mom database analytics, we have to relate it to Business Intelligence , since it is nothing other than a door through which companies are able to understand the most relevant information to make sound business decisions.
Data analytics
How data analytics is used
The analytical process involved in advanced data analytics includes six steps:
First of all, it is necessary to know the initial state of the company and, if there is a given problem, to identify it clearly.
Next, it is essential to know what data needs to be analyzed to improve the initial situation or solve the problem.
The third phase is to prepare the data for analysis , since advanced data analytics moves within certain parameters.
Data analysis itself.
Implementation of analysis and models.
Performance monitoring and optimization .
Benefits of data analytics
Simplify complex processes
Especially in environments where the Information Technology (IT) department collaborates with teams specialized in data analytics, Big Data, Machine Learning and Artificial Intelligence.
Accelerates reaction capacity
While advanced data analytics tends to focus initially on analyzing the past to inform future decisions (business analytics), predictive analytics enables the company to stay ahead of events. Problem solving is no longer the only goal of this discipline, which also prevents such problems from occurring .
Help with monitoring and management tasks
Prescriptive data analytics is capable of fine-tuning analysis processes thanks to the intervention of Machine Learning techniques. As long as the data is consistent and reliable, artificial intelligence itself will be able to make certain operational decisions. This eliminates a large part of human error .
Eliminate risks in operational decisions
Data analytics offers analysis of a large number of variables in customizable reports that can be shared by all relevant departments within a company . This way, all actors in charge of operational decision-making have the same information. It is therefore easier to arrive at decisions that improve business results.
In addition, each of the departments involved can offer their own feedback to improve the operation of AI.
Immediacy without risk
One of the most important benefits of data analytics is the ability to make decisions in real time once the company is aware that there is a problem. The ability to decide at the right time is key to adapting the operation of companies to market trends. Something that Machine Learning and data analytics put within reach of any company that wants to stand out in its sector.