Advanced Analytics

What is it and why does it matter to me?

Cátia Silva e Santos, Closer Consulting

Advanced Analytics is an umbrella term for a group of high-level methods and tools that can help us get more out of the available data, using predictive modeling, machine learning algorithms, deep learning, business process automation and other statistical methods to analyze business information from a variety of data sources. The predictive capabilities of Advanced Analytics can be used to forecast trends, events, and behaviors. This gives organizations the ability to perform advanced statistical models such as “what-if” calculations, as well as to future-proof various aspects of their operations.

These are referred to as “Advanced” Analytics to differentiate them from the traditional analytics approach, usually accomplished using business intelligence (BI) systems. Currently, we see Advanced Analytics being increasingly coupled with cloud analytics, to leverage the vast amounts of data being generated every day.

Traditionally, data is processed to inform business users of the past performance of business operations and, therefore, the data is gathered and aggregated into a clean format for reporting purposes. Nowadays, organizations want to get a 360-degree view of their business, identify the root causes of success or failure in operations, and control as much future uncertainty as possible.

A key difference between Advanced Analytics and the traditional BI is the approach. In the latter, systems are built to have repeatable analysis performed, analyzing pre-defined KPIs. At the same time, in the former, the process begins with a question, a problem that requires an answer, then a set of analysis is designed to dive into the data and mine the business insight to answer the question. Advanced Analytics is a valuable resource to enterprises because it enables an organization to get greater functionality from its data assets and can help address some of the more complex business problems that traditional BI reporting cannot.

Some of the areas that make up the magic of Advanced Analytics include machine learning and artificial intelligence, semantic and graph analysis, data and text mining, complex event processing, pattern matching, predictive analytics, data visualizations, sentiment analysis, network, and cluster analysis, multivariate statistics, simulation, neural networks, and the list is constantly expanding as new techniques are invented and adapted to the data analytics world.

Now that we have looked at “What is Advanced Analytics?”, you may be wondering “What can Advanced Analytics do for me?”. Because of its wide range of disciplines and such broad applicability, there are multiple excellent use cases for Advanced Analytics. For example, using it in the context of the industrial sector can have a significant impact throughout the supply chain, from procurement to production and even delivery, through the combined use of forecasting and optimization techniques.

In addition to enabling greater use of data assets and providing decision-makers with greater confidence in data accuracy, Advanced Analytics also offers accurate forecasting, faster and data-driven decision-making, deeper insights, improved risk management and the ability to anticipate problems or opportunities.

So as technology keeps evolving, new data sources are created every day. Client patterns are changing, faster and more informed decision-making is imperative and taking advantage of the available data becomes an increasingly significant competitive advantage.

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