Finding the Right Advanced Analytics Software

Not all analytics tools are alike. Some have an easy-to-use UI for data preparation and exploration, while others offer full flexibility for writing machine learning code based on the latest open source libraries. This is why we use peer groups to compare products.

The peer groups are primarily based on the various activities involved in analytical projects and the different user groups. They take into account how customers say they use the software, which can vary substantially from product to product. But we also include the experience and judgment of BARC analysts in deciding on the groupings.

Peer groups are simply a guide to the reader to help make analytics tools easier to compare and to show why individual products return such disparate results. They are not intended to be a judgment of the quality of the tools.

Some products appear in more than one peer group. We use these peer groups in a consistent way in our analysis, as well as in .

The point to the peer groups is to make sure that the comparisons between the analytics tools make sense. The products are grouped together as we would expect them to appear in a software selection shortlist.

To make a proper choice, buyers should first segment the market into the tool types that fit their requirements. The peer groups are intended to help with this task.

Advanced analytics is increasingly being adopted across all industries. To make analytics work, a variety of tools is available on the market.

‘BARC Score Advanced Analytics Platforms’ analyzes the strengths and challenges of all of the leading vendors in the advanced analytics market as well as several smaller vendors that often have less visibility, but equally offer outstanding value to their customers.

Based on countless data points and many analyst interactions, advanced analytics software vendors are rated on a variety of criteria, from product capabilities and architecture to sales and marketing strategy, financial performance and customer feedback.

Calculating the individual ratings for all criteria and all vendors produces two scores per company: the ‘portfolio capabilities’ score and the ‘market execution’ score, which are plotted on each axis resulting in the vendor’s dot on the BARC Score graphic.

‘Portfolio Capabilities’ includes a series of weighted criteria based on three major areas: functionality, infrastructure and product-related evaluation criteria.

‘Market Execution’ takes into account a blend of product, sales and marketing strategy as well as certain organizational, financial and geographical considerations.

There are three separate inclusion criteria for this BARC Score: the first is associated with a vendor’s product characteristics, the second is linked to the financial results and the installed user base relating to those products, and the third refers to the regional distribution and support of the software.

Data mining and predictive analytics cover a range of tasks. The two main tasks executed with data mining are unsupervised learning (finding patterns in unlabeled data) and supervised learning (inferring information from labeled data). In addition, common applications of predictive analytics include forecasts based on time series analysis. To be evaluated in this Score, a vendor has to cover all these three areas as they are related and may, depending on the task, build on each other. In this BARC Score, only software that enables users to specify their own workflows is considered for inclusion.

In addition, the vendor has to generate a minimum of 1 million EUR in license revenue per year with the above product set, spread across two regions of the world. In order to accommodate vendors with an open source business model that have also gained popularity in commercial applications, we also include software that has an installed user base of at least 10,000 users.