Deconstructing SAS Analytics Accelerator
Now and then I get queries from clients about SAS Analytics Accelerator, an in-database product that SAS supports exclusively with Teradata database. SAS does not publicize sales figures for individual products, so we don’t know for sure how widely Analytics Accelerator is used. There are some clues, however.
- Although SAS released this product in 2008, it has published no customer success stories. Follow the customer success link from SAS’ overview for Analytics Accelerator and read the stories; none of them describes using this product.
- SAS has never expanded platform support beyond Teradata. SAS is a customer-driven company that does not let partner and channel considerations impact its customers. Absence of a broader product rollout implies absence of market demand
- Unlike the rest of its product line, SAS has not significantly enhanced Analytics Accelerator since the product was introduced four years ago.
SAS supports seven in-database Base SAS PROCs (FREQ, MEANS, RANK, REPORT, SORT, SUMMARY and TABULATE) on many databases, including Aster, DB2, Greenplum, Netezza, Oracle and Teradata. Analytics Accelerator supports seven SAS/STAT PROCs (CORR, CANCORR, FACTOR, PRINCOMP, REG, SCORE, and VARCLUS), one SAS/ETS PROC (TIMESERIES), three Enterprise Miner PROCs (DMDB, DMINE and DMREG) plus macros for sampling and binning for use with Enterprise Miner. Customers must license SAS/STAT, SAS/ETS and SAS Enterprise Miner to use the in-database capabilities.
On the surface, these appear to be features that offer SAS users significantly better integration with Teradata than with other databases. When we dig beneath the surface, however, the story is different.
Anyone familiar with SAS understands from a quick look at the supported PROCs that it’s an odd list; it includes some rarely used PROCs and omits PROCs that are frequently used in business analytics (such as PROC LOGISTIC and PROC GLM). I generally ask SAS clients to list the PROCs they use most often; when they do so, they rarely list any of the PROCs supported by Analytics Accelerator.
The SAS/STAT PROCs supported by Analytics Accelerator do not actually run in Teradata; the PROC itself runs on a SAS server, just like any other SAS PROC. Instead, SAS passes a request to Teradata to build a Sum of Squares Cross Product (SSCP) matrix. SAS then pulls the SSCP matrix over to the SAS server, loads it into memory and proceeds with the computational algorithm.
This is a significant performance enhancement, since MPP databases are well suited to matrix operations and the volume of data moved over the network is reduced to a minimum. But here’s the kicker: any SAS user can construct SSCP matrices in an MPP database (such as IBM Netezza) and import it into SAS/STAT. You don’t need to license SAS Analytics Accelerator; every SAS customer who licenses SAS/STAT already has this capability.
This explains, in part, the unusual selection of PROCs: SAS chose PROCs that could be included with minimal R&D investment. This is a smart strategy for SAS, but says little about the value of the product for users.
Since SAS/STAT does not currently export PMML documents for downstream integration, in-database support for PROC SCORE is intriguing; once again, though, the devil is in the details. Analytics Accelerator converts the SAS model to a SQL expression and submits it to the database; unfortunately, this translation only supports linear models. SAS users can score with models developed in thirteen different SAS PROCs (ACECLUS, CALIS, CANCORR, CANDISC, DISCRIM, FACTOR, PRINCOMP, ORTHOREG, QUANTREG, REG, ROBUSTREG, TCALIS and VARCLUS), but with the exception of PROC REG these are rarely used in predictive modeling for business analytics. SAS seems to have simply selected those PROCs whose output is easy to implement in SQL, regardless of whether or not these PROCs are useful.
Overall, Analytics Accelerator lacks a guiding design approach, and reflects little insight into actual use cases; instead, SAS has cobbled together a collection of features that are easy to implement. When clients consider the tasks they actually want to do in SAS, this product offers little value.