Advanced analytics is a data analysis method designed to extract useful information and insights from data, in order to help business teams make informed decisions. It relies on the use of sophisticated statistical and computational techniques such as predictive modeling and machine learning.
However – as its name suggests – Advanced Analytics requires a high level of skill and expertise if it is to be implemented successfully:
Performing advanced analysis requires building an efficient data pipeline, because data preparation and preprocessing are a must if you want to have clean and normalized data available in your data warehouse. Data visualization also proves very useful to make your findings accessible – and easily understandable – for your stakeholders.
As a matter of fact, both BI and advanced analytics enable stakeholders to leverage data and gather insights. However, these tools are far from identical:
You now know that advanced analytics enables organizations to gather insights and make predictions based on large and complex datasets. But how is it done?
First, by identifying patterns and trends in these datasets.
Here is an example: in a retail company, Business Users need to analyze customer purchase data and understand their customers’ behavior, by answering the following questions:
But advanced analytics goes even further: for instance, it enables Business Users to
All in all, advanced analytics helps organizations gain and maintain a competitive edge, by always keeping one step ahead – which makes it a key component of business success.