Advanced data analytics

Advanced data analytics is used to enable accuate measurement, analysis, and monitoring based on real parameters and data. It acts as a framework to control the data generated by the business itself. It allows us to gather information from any type of document, whether structured or unstructured, and transform it into a readable data format.

Conducting an analysis on IoT data will help control production, manage a potential data flood, and drive the commercial value of your brand.

Advanced analytics is a comprehensive set of analytical techniques and methods, such as Big Data, Artificial Intelligence (AI), Machine Learning, etc.

These techniques allow for better predictive analysis and provide insights into technological change. As it occurs, a broader perspective is offered, enabling organizations to formulate better responses and act based on more precise forecast and processes. 

What types exist?

Within advanced data analytics, we can differentiate between 3 main types:

• Descriptive Analytics: this is the preliminary stage where a summary of historical data is created to provide useful information and prepare for subsequent analysis.
• Diagnostic Analytics: this phase involves processing data to identify a specific failure or anomaly and reach the root cause of the problem. Afterward, we can prepare a report describing the failure and its possible solutions or apply predictive or prescriptive analytics.
• Predictive Analytics: this provides us with the tools to determine unknown, uncertain, or manually costly data in our business.
• Prescriptive Analytics: by optimizing processes and setting rules, this type of analytics allows us to execute a specific strategy to achieve a defined objective.

More topics of interest