By putting in the hands of users more insights derived from the analysis of huge volumes of data (Big Data), applications are enriched and decision-making is automated.
Analytics for everyone
Without a doubt, every company is a potential analytical company; each process is an analytical process that can be improved; and each collaborator can use the analytics. The first thing to do to launch an analytical project is to have the data in order to proceed with its analysis, and use the insights resulting to the benefit of the business.
The faster an organization can move through the analytics cycle, the sooner it will realize tangible value from its investments in that technology. This cycle is based on three key pillars: data management, analytics and data visualization.
Data management is the first step in handling large volumes of information and starting an analytical project. All data used in business activities needs to be managed as well as possible to obtain reliable information that allows decisions to be made in areas such as product development, customer service, protecting data privacy and innovating, among others.
For its part, analytics analyzes countless rows of numbers and figures, and performs complex calculations quickly, putting the results in the hands of business users to have a broad view of what is happening in the business and determine the direction what it will take in the future.
Finally, data visualization makes the results of analytical work more understandable. By visualizing the data in different types of graphs and tables, it is much easier to understand the results.
Innovation is ubiquitous
The value of analytics lies in its ability to enable organizations across industries to innovate and modernize in a variety of areas. It is the ideal medium to explore quite interesting possibilities.
– Analytics can track consumer behavior across physical and digital channels to match it with offers that will capture their attention and meet their needs.
– Big Data can be used to optimize logistics. By considering various factors that influence delivery speed and reliability, such as traffic and weather, smarter logistics can help you operate more sustainably.
– Analytics can provide data protection. With advanced pattern recognition and behavior correlation, risks can be better assessed. It also helps safeguard the privacy of personal data.
– Financial institutions combine analytics with artificial intelligence to manage regulatory and credit risks, capital planning, and money laundering prevention.
– In telecommunications, it helps combat fraud, communicate effectively with customers in physical and digital channels, personalize offers and advertising, as well as optimize and automate network operations.
– With analytics, hand in hand with artificial intelligence and machine learning, governments are putting data to work to improve their results for citizens.