SAS

Last Updated: 7th December 2018
Analyst Coverage: Philip Howard

SAS is headquartered in North Carolina and was founded in 1976. The company started life by offering a 4GL-based statistical package for financial and economic analysis but has expanded to offer a multi-faceted product set, providing organisations with an information delivery system that spans all aspects of analytics and associated technologies. This includes business analysis, analytics and data science as well as practical applications thereof (customer intelligence and so forth). In addition, complementary capabilities such as data quality and data integration are also offered.

Since its foundation, SAS has expanded primarily in an organic manner and is now a multi-billion-dollar company that has offices and partners worldwide. It remains privately owned. A notable point is that SAS consistently invests a significant proportion of its revenues into R&D, typically at around 25%. This emphasis on R&D, plus the company’s refusal to listen to the blandishments of the stock market (where short-term trends are more important than long-term profits), has meant that SAS is, by some margin, the largest pure-play vendor in the analytics market.

Company Info

Headquarters: Wittington House, Henley Road, Medmenham, Marlow, Buckinghamshire SL7 2EB, UK
Telephone: 01628 486 933

Company Links

DataFlux

Last Updated: 29th January 2013

What it does

The SAS solution in the data discovery and profiling space is part of the SAS DataFlux Data Management Platform. DataFlux started life as a data quality vendor but, after it was acquired by SAS, has expanded its capabilities to include data integration (including both ETL and data virtualisation/federation), master data management and complex event processing along with a business glossary (see other SAS product pages). The company has been active in the data governance space for some years. Until recently it has acted as an independent subsidiary of SAS but that has now changed, with the brand now having been taken directly within the SAS umbrella. This will be good for SAS customers but will likely mean that the company loses traction as an independent vendor looking for sales in non-SAS accounts. Given the quality of the product this is unfortunate.

The DataFlux Data Management Platform is arguably the leading product in the market in technical terms and is certainly a leading product. It is especially strong in its technical competency for both profiling and profiling combined with discovery. In particular, DataFlux has very strong support for users, both at a business and technical level.

Markets and channels

Historically, DataFlux has had a broad focus across industry sectors and across organisation sizes. SAS, on the other hand, is well-known for focusing on the top end of the market. It is common when companies are absorbed like this that the parent company will continue to claim that the relevant products will be marketed independently but in practice that never happens: salespeople prefer low hanging fruit and that is the existing client base. For all intents and purposes therefore we expect DataFlux sales to be concentrated on the SAS user base. This is also likely to mean that companies that are otherwise non-SAS users may move to other platforms in the fullness of time.

SAS has a rich partner program but it is difficult to isolate partners that are specific to Dataflux data profiling/discovery.

Users

Customer numbers are measured in terms of thousands though how many of these are using profiling/discovery as opposed to other elements of the Data Management Platform is not clear. In any case it will be a significant number and many of these are household names.

Technology

Data profiling and discovery is a part of the DataFlux Data Management Platform, which is an integrated (truly integrated) platform with common metadata across the entire suite. It runs under Windows, Linux and AIX. JDBC and ODBC are supported and there are native drivers for Greenplum, DB2, Informix, MySQL, Oracle, Postgres, SQL Server and SAP Sybase. However, there are no facilities for supporting non-normalised data such as Oracle nested tables. On the other hand, COBOL copybooks are supported; along with text files; XML; and spreadsheets; as well as Hadoop and Hive.

Services

SAS is a major worldwide organisation with offices all around the globe. DataFlux thus enjoys the consulting, support and training capabilities that one would expect from a company of the size of SAS.

SAS Event Stream Processing

Last Updated: 6th December 2018
Mutable Award: Gold 2018

What is it?

Figure 1

SAS has been offering stream processing for a number of years, especially for fraud applications. However, a major driver is now the Internet of Things (IoT) and applications such as predictive maintenance and smart “anything”. The basic principle is to capture and apply analytics to data in motion.

In terms of the products themselves, SAS Event Stream Processing (ESP) comes with a variety of options and components, as illustrated in Figure 1. The plus signs indicate additional license options. CAS stands for Cloud Analytic Services. The ESP for Edge option will run on something as small as a Raspberry Pi and supports analytics on those small but powerful platforms. Both event-based and window-based (time sliced) options are available.

Customer Quotes

"Subsea surveillance of oil platforms to help avoid unplanned production interruptions, resulted in the prediction of a submersible pump failure that saved the company $3 million."
Major energy company

"Our transportation assets are equipped with edge devices that manage hundreds of data elements per second to optimize their operation. We use SAS Event Stream Processing in our edge devices to enable our customers with analytical insights on their data in real time to minimize operating costs, identify predictive maintenance issues before they become a problem."
Major rail transportation manufacturer

What does it do?

Perhaps the biggest differentiator for SAS in this space is its analytic capabilities, though it’s also notable for its performance. The options available are illustrated in Figure 2, many of which have only been introduced in the latest release (5.1).  A key feature is the continuous improvement of in-stream models, using machine learning.

Figure 2

As far as Figure 2 is concerned, DS2 is a SAS specific language and ASTORE, also proprietary, is used to support algorithms that are trained offline but which are scored in-stream. Model Manager, which is a separate product, supports PMML (predictive modelling mark-up language) and will convert supported model types (more than a dozen currently) into SAS code for deployment on the ESP Server. There is also support for RESTful APIs to run other models. Python notebooks are supported to drive the ESP engine, publish events to ESP, and display results, and models written in Python and C are also supported, though not Java-based models. R will be supported later during 2018. Additional facilities in the ESP Server that are worth mentioning include in-stream geofencing, text analytics, in-stream time pattern recognition (including time-series similarity analysis and time-series clustering), and the ability to build data quality rules into the streaming process.

Of the other elements within the ESP environment, ESP Studio provides an environment for constructing visual models, designed for use by non-technical personnel, while Streamviewer provides a visual analytic dashboard environment that lets you combine real-time and historic data. Event Stream Manager is used to update or deploy analytic algorithms without requiring any downtime on the server, and similarly, add new ESP servers as required. It will automatically discover new servers using ESP agent technology for automated monitoring.

SAS provides connectivity to over 300 end points and supports a variety of standard protocols including MQTT, BACNet publisher connector and adaptor (for smart homes), OPC-UA connector and adaptor (for machine-to-machine communications), a UVC connector (Video4Linux), a WebSocket publisher connector, and a URL publisher connector (for RSS news feeds, JSON from a weather service or News from an HTML page). There are also facilities provided so that you can write your own connectors. In this context it is worth mentioning the new SAS ESP Community (communities.sas.com/IOT), which is moderated by SAS but user-driven.

Why should you care?

Machine learning and the Internet of Things are top of mind for many chief data officers and SAS has significant expertise in both of these areas. It is the world’s largest business intelligence and analytics company. It should therefore not come as any great surprise that the company is exceptionally strong when it comes to the breadth of analytic capabilities it provides, regardless of whether this is for data at rest or data in motion. Put this together with the fact that Mckinsey has predicted that the Internet of Things will generate $11 trillion of economic impact by 2025 and you have significant opportunities for SAS to exploit its strength in analytics, across a range of IoT-based deployments. The company is also actively researching analytics for Blockchain, another environment which is set to have a major impact.

The Bottom Line

As a company, SAS is long-established and has a reputation for enterprise-level capability. At the same time it has been a leading light in the analytics market for 40 years. In our view, that makes SAS a major contender, if not a leader, in the market for streaming analytics platforms.