At a conference in Lausanne in June 2014 SAS shared their current business performance and strategy. The privately held company (with just two individual shareholders) had revenues of just over $3 billion, with 5% growth. Their subscription-only license model has meant that SAS has been profitable and growing for 38 years in a row. 47% is Americas, 41% from Europe and 12% from Asia Pacific. They sell to a broad range of industries, but the largest in terms of revenue are banking at 25% and government at 14%. SAS is an unusually software-oriented company, with just 15% of revenue coming from services. Last year SAS was voted the second best company globally to work for (behind Google), and attrition is an unusually low 3.5%.
In terms of growth, fraud and security intelligence was the fastest growing area, followed by supply chain, business intelligence/visualisation and cloud-based software. Data management software revenue grew at just 7%, one of the lowest rates of growth in the product portfolio (fraud management was the fastest growing). Cloud deployment is still relatively small compared to on-premise but growing rapidly, expected to exceed over $100 million in revenue this year.
SAS has a large number of products (over 250), but gave some general update information on broad product direction. Its LASR product, introduced last year, provides in-memory analytics. They do not use an in-memory database, as they do not want to be bound to SQL. One customer example given was a retailer with 2,500 stores and 100,000 SKUs that needed to decide what merchandise to stock their stores with, and how to price locally. They used to analyse this in an eight-hour window at an aggregate level, but can now do the analysis in one hour at an individual store level, allowing more targeted store planning. The source data can be from traditional sources or from Hadoop. SAS have been working with a university to improve the user interface, starting from the UI and trying to design to that, rather than producing a software product and then adding a user interface as an afterthought.
In Hadoop, there are multiple initiatives to apply assorted versions of SQL to Hadoop from both major and minor suppliers. This is driven by the mass of skills in the market with SQL skills compared to the relatively tiny number of people that can fluently program using MapReduce. Workload management remains a major challenge in the Hadoop environment, so a lot of activity has been going on to integrate the SAS environment with Hadoop. Connection is possible via Hive QL. Moreover, SAS processing is being pushed to Hadoop with Map Reduce rather than extracting data. A SAS engine is placed on each cluster to achieve this. This includes data quality routines like address validation, directly applicable to Hadoop data with no need to export data from Hadoop. A demo was shown using the SAS Studio product to take some JSON files, do some cleansing, and then use Visual Analytics and In-Memory Statistics to analyze a block of 60,000 Yelp recommendations, blending this with another recommendation data set.