Last month SAP quietly released a new interface to ramp up: SAP BusinessObjects Predictive Analysis. Ramp up is the vendor’s approach to releasing production software to a limited number of customers. The product is expected to be generally available later this year.
Predictive analytics has become an increasingly important part of the business analytics market and an area in which SAP has lagged behind chief competitors Oracle, IBM, and SAS. SAP currently OEMs capabilities from SPSS, and with IBM’s acquisition of SPSS in 2009, it seemed only a matter of time before SAP would develop an alternative solution. Competitive and market forces have shaped SAP’s latest endeavor, including the momentum of open source R, in-memory and in-database processing, and the convergence of analytics with business intelligence
Open Source R: R is an open source project initiated by academics in New Zealand in the mid 1990s. It is a statistical language without a graphical user interface. A number of vendors, including SAS, have been adding support for R. For example, Information Builders WebFOCUS Rstat and TIBCO Spotfire S+ leverage R. SAP BusinessObjects Predictive Analysis provides a graphical user interface to R.
There are two aspects to SAP BusinessObjects Predictive Analysis, one is the model development and execution, the other is the visualization to identify patterns in the data. I was impressed by the number of visualizations Predictive Analysis automatically generated to show patterns in the data. To be clear, this is not a visual discovery tool for business users that competes with the likes of Explorer or Tableau. It is a tool for statisticians. But I suspect this integrted visualization capability will prove to be a differentiator.
In-data base processing: Historically, statisticians have worked on offline data files—whether SAS datasets or flat files--to analyze their data and build models. With increasing volumes of data, database and analytic vendors have been pushing more of the processing into the database. SAS for example, was one of the first to push the processing into Teradata in 2008, and now supports in-database processing with Netezza, EMC Greenplum, DB2, and AsterData . Last month Oracle released Oracle Advanced Analytics that bundles Oracle R Enterprise, providing support for R models to be processed in the Oracle database. With the new SAP BusinessObjects Predictive Analysis, processing can be pushed into Hana, the vendor’s new in-memory appliance that includes its own statistical function library.
Mainstream Analytics?: There has never been a rallying cry for mainstream analytics that there has been for business intelligence. Instead, the cry has been for greater integration and ease of consumption of analytic models. Developing the models remains a task for skilled statisticians. In this regard, some BI vendors have simplified the ease in which a model can be called from a dashboard or report, with the results of the model transparently embedded. An example is a report that displays sales by customer, with a flag to indicate likelihood of churning. MicroStrategy was one of the first to provide such seamless integration but its adoption has been slow.
There has been greater momentum for analytics to be embedded in pre-built analytic applications, and this is an area where SAS solutions are market leaders but in which IBM has been growing.
With the SAP Predictive Analysis, there is nice integration with the BI platform in that the tool can access a universe, either the version 3 format (.UNV) or the new version 4 format (.UNX). This is a similar level of integration in the capabilities SAP currently OEMs from SPSS. However, there currently is no easy way to embed model results in a dashboard or report. SAP expects various analytic applications to take advantage of the new capability over time. For example, the SAP Smart Meter Analytics uses clustering and segmentation algorithms on energy consumption.
SAP BusinessObjects Predictive Analysis is not the vendor’s first entry into the advanced analytics space, but it does seem to be a higher level of commitment to the market than earlier efforts. It’s too early to say if this will have any impact on either SAS’ or SPSS’ share in this space. However, it certainly will improve the capabilities of the analytic applications and will make SAP a natural addition to any customer’s short list new to predictive analysis.
Cindi Howson, BI Scorecard