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Advanced Analytics Software

Advanced Analytics Software

Advanced analytics is a part of data science that uses high-level methods and tools to focus on projecting future trends, events, and behaviors. This gives organizations the ability to perform advanced statistical models such as ‘what-if’ calculations, as well as future-proof various aspects of their operations.

The term is an umbrella for several sub-fields of analytics that work together in their predictive capabilities.

The major areas that make up advanced analytics are predictive data analytics, big data, and data mining. The process of advanced analytics includes all three areas at various times.

Data mining is a key aspect of advanced analytics, providing the raw data that will be used by both big data and predictive analytics. Big data analytics are useful in finding existing insights and creating connections between data points and sets, as well as cleaning data.

Finally, predictive analytics can use these clean sets and existing insights to extrapolate and make predictions and projections about future activity, trends, and consumer behaviors.

Advanced analytics also include newer technologies such as machine learning and artificial intelligence, semantic analysis, visualizations, and even neural networks. Taken together, they help advanced analytics software create an accurate enough canvas to make reliable predictions and generate actionable BI insights on a deeper level.

The most popular products in category Advanced Analytics Software All category products

SAS COST and Profitability Management
SAS FRAUD Framework for Insurance

F.A.Q about Advanced Analytics Software

A list of tips on how to manage the process of building an advanced analytics program

  • Start with a proof-of-concept project to demonstrate the potential business value of analytics applications.
  • Take training seriously. New data management and analytics skills likely will be needed, especially if big data platforms and tools like SAS advanced analytics tools are involved.
  • Develop processes to ensure that business units are ready to act on analytical findings so the work of data scientists and other analysts doesn't go to waste.
  • Monitor and assess advanced and predictive analytics software on a regular basis to make sure the data being analyzed is still relevant and the analytical models being run against it are still valid.

Advanced analytics tools

There are a variety of advanced analytics tools to choose from that offer different advantages based on the use case. They generally break down into two categories: open source and proprietary.

Open source tools have become a go-to option for many data scientists doing machine learning and prescriptive analytics. They include programming languages, as well as computing environments, including Hadoop and Spark. Users typically say they like open source advanced analytics tools because they are generally inexpensive to operate, offer strong functionality and are backed by a user community that continually innovates the tools.

On the proprietary side, vendors including Microsoft, IBM and the SAS Institute all offer advanced analytics tools. Most require a deep technical background and understanding of mathematical techniques.

In recent years, however, a crop of self-service analytics tools has matured to make functionality more accessible to business users. Tableau, in particular, has become a common tool. While its functionality is more limited than deeper technical tools, it does enable users to conduct cluster analyses and other advanced analyses.