Advanced analytics

Definition

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: 

• Statistical analysis is the process of collecting and analyzing data in order to identify patterns and trends – all thanks to statistical methods.
• Machine learning relies on artificial intelligence – that is, systems that can learn from the data they are presented with and make predictions autonomously.
• Data mining is the process of discovering patterns and relationships within large datasets in order to predict outcomes. 
• Predictive modeling relies on observing and interpreting historical and current data. It empowers data teams and Business Users to predict future events and make informed decisions.

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. 

Advanced analytics vs. business intelligence

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: 

• Advanced analytics is used to discover insights and draw predictions from the data at hand, in order to solve complex business problems. BI, on the other hand, focuses on making business performances visible and easily understood by creating reports and dashboards (among other things). 
• Advanced analytics requires a higher level of technical expertise, and has to be carried out with specialized tools and methods:  it is, unsurprisingly, the turf of Data Scientists.

Implementing advanced analytics

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:

• Which products are the most popular?
• Among which customer segments?
• How has it evolved over time?

But advanced analytics goes even further: for instance, it enables Business Users to

• forecast future events;
• detect fraud or anomalies;
• optimize business processes;
• customize marketing campaigns.

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.