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Forrester Wave: Big Data Predictive Analytics Market Led By IBM, SAS; New Entrants Compete

The first real look at Big Data analytics of the year comes in the shape of a new Forrester Wave report. The space may still be dominated by IBM and SAS but it appears that 2013 is going to be a year with some serious competition with a substantial number of strong performers jostling for a place in the Leaders category.

In all, this Wave, the Forresters Wave Big Data Predictive Analytics Solutions, Q1 2013 — which was researched and written up by Mike Gualtieri — assessed ten different vendors using 51-criteria. The vendors were ultimately broken down into Leaders, Strong Performers and Contenders, depending on how they performed across those criteria.

Again, like other Forrester Waves and Gartner reports, Forrester insists that these findings are only guidelines and that potential clients should only use the Wave report as a starting point for more comprehensive research before they start investing in anything.

Big Data Predictive Analytics Solutions, Q1 2013

That out of the way, the vendors that made it into the Wave this time around are: Angoss Software, IBM, KXEN, Oracle, Revolution Analytics, Salford Systems, SAP, SAS, StatSoft and Tibco Software. In an interesting side note buried deep in the report, Gualtieri points out that Oracle was not very forth-coming with information on its solution.

While Oracle chose not to provide full information for its big data predictive analytics solution, we included it in the Forrester Wave based on our analysis of publicly available information,” Gualtieri writes.

Oracle, as anyone that has followed it knows, is never reticent about publicizing its achievements, so the question to be asked is what is Oracle doing? Why won’t it discuss its products? Are we to take it that there is a problem or — more than likely — is it preparing something major this year?

Predictive Analytics Rise

All that said, there are three takeaways in this research that clients and vendors should keep in mind:

  1. Enterprises have woken up to the advantages of Big Data predictive analytics and vendors need to respond to that growing market need.
  2. The result is that the market is growing because more business and technology professionals see these solutions as a way to address opportunities.
  3.  For vendors, Big Data handling, modeling tools and algorithms are key differentiators.

The result, Forrester says, is that it expects the market to be “vibrant, highly competitive and flush with new entrants” over the coming year.

Big Data, Information Explosion

The Big Data market is a result of the massive growth in the availability of information and data since we all went electronic way back when. The result for enterprises is the development of huge data warehouses with buckets of information that, until relatively recently, no one was really able to manage.

Recent advances in business intelligence tools have opened up this information to enterprises to use as an asset in building their business. But predictive analytics goes a step further with advanced statistical, data mining and machine learning algorithms digging deeper to find patterns that can be projected into the future behaviors of customers.

The starting point then is here. Forrester describes Big Data analytics solutions as:

Software and/or hardware solutions that allow firms to discover, evaluate, optimize and deploy predictive models by analyzing big data sources to improve business performance or mitigate risk."

Data analysis can be done daily, weekly or even continuously. In order for enterprises to do it successfully they must:

  • Set the business goals: Outline what business goals they are hoping to achieve with predictive analytics.
  • Analyze data from a number of sources: Enterprises need to assess and identify all the possible sources of information. The more information, the better the analysis. With the current level of information, enterprises are advised to use a data visualization tool.
  • Prepare the data: Data must often be re-processed before running analysis algorithms. For example, data analysts may need to enrich the data with calculated aggregate fields.
  • Create the predictive model: There are hundreds of different statistical and machine learning algorithms and combinations that data analysts can run against the data to find predictive models.
  • Evaluate the model: Predictive models are about all the probabilities contained in the information. Analytics professionals need to constantly work with different models that work with the data they are using for a specific problem.
  • Monitor: Enterprises need to constantly assess the effectiveness of their results. Keep in mind the business idiom “Past results do not guarantee future performance”, Forrester says.

Big Data Predictive Analytics Evaluation Overview

To see how vendors compare to each other, Forrester evaluated the strengths and weaknesses of the top players in the predictive space. While the market is too young to really tease out trends the way it would in more established IT areas, Forrester says it expects the market to be highly competitive with new entrants over the next three years. Evaluations were made on the basis of:

 

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