You are currently browsing the tag archive for the ‘SAS’ tag.
Many businesses are close to being overwhelmed by the unceasing growth of data they must process and analyze to find insights that can improve their operations and results. To manage this big data they find a rapidly expanding portfolio of technology products. A significant vendor in this market is SAS Institute. I recently attended the company’s annual analyst summit, Inside Intelligence 2014 (Twitter Hashtag #SASSB). SAS reported more than $3 billion in software revenue for 2013 and is known globally for its analytics software. Recently it has become a more significant presence in data management as well. SAS provides applications for various lines of business and industries in areas as diverse as fraud prevention, security, customer service and marketing. To accomplish this it applies analytics to what is now called big data, but the company has many decades of experience in dealing with large volumes of data. Recently SAS set a goal to be the vendor of choice for the analytic, data and visualization software needs for Hadoop. To achieve this aggressive goal the company will have to make significant further investments in not only its products but also marketing and sales. Our benchmark research on big data analytics shows that three out of four (76%) organizations view big data analytics as analyzing data from all sources, not just one, which sets the bar high for vendors seeking to win their business.
In the last few years SAS has been investing heavily to expand its portfolio in big data. Today its in-memory infrastructure can operate within Hadoop, execute MapReduce jobs, access the various commercial distributions of Hadoop, conduct data preparation and modeling in Hadoop and extend it to its data and visual discovery and exploration tools. SAS has architected its analytics tools and platform to use Hadoop’s Pig and Hive interfaces, apply MapReduce to process large data sets and use Hadoop Distributed File System (HDFS) to store and access the big data. To exploit Hadoop more deeply, the SAS LASR Analytic Server (part of SAS Visual Analytics) connects directly to HDFS to speed performance. SAS LASR Analytic Server is an in-memory computing platform for data processing and analysis that can scale up and operate in parallel within Hadoop to distribute the computation and data workloads. This flexibility in the architecture enables users to adapt SAS to any type of big data, especially Hadoop deployments, for just about any scale and configuration. To work with other database-oriented technologies the company has built technical partnerships not only with major players Teradata and SAP but also with the new breed of Hadoop vendors Cloudera, Hortonworks and Pivotal, as well as with IBM BigInsights. SAS also engineered access to SAP HANA, which establishes further integrated into SAP’s data platform for analytics and other applications.
At the Inside Intelligence gathering, SAS demonstrated its new Visual Statistics product. Like its Visual Analytics this one is available online for evaluation. It offers sophisticated support for analysts and data professionals who need more than just a visually interactive analytic tool of the sort that many providers now sell. Developing a product like Visual Statistics is a smart move according to our research, which finds that predictive analytics and statistics is the most important area of big data analytics, cited by 78 percent of organizations. At this point visual and data discovery are most common, but we see that users are looking for more. SAS Visual Statistics can conduct in-memory statistical processing and compute results inside Hadoop before the data is transferred to another analytic data repository or read directly into an analytics tool. A demonstration of these capabilities at the analyst summit revealed how these capabilities along with the use of tools in SAS 9.4 could raise the bar for sophisticated analytics tools for business.
SAS also has a data management software suite for data integration, quality, mastering and governance and is working to make the product known for its big data support. This is another important area: Our research in big data analytics finds quality and consistency of data to be significant challenges for 56 percent of organizations and also that 47 percent are not satisfied with integration of information for creating big data analytics. SAS is expanding to provide data quality tools for Hadoop. Its portfolio is expansive in this area, but it should take steps to market these capabilities better, which spokespeople said it will do in 2014. Our recent research in information optimization found that organizations still are spending disproportionate amounts of time in preparing data (47%) and reviewing it (45%) for analytics. They need to address these difficulties to free their analysts to spend more time on analysis that produces recommendations for decision-makers and to collaborate on business improvement. SAS’s efforts to integrate data and analytics should help reduce the time spent on preparation and help analysts focus on what matters.
SAS also will expand its SAS Stream Processing Engine with a new release coming by midyear. This product can process data as it is being generated, which facilitates real-time analytics – that’s the third-most important type of big data analytics according to our research. Applying analytics in real time is the most important aspect of in-memory computing for two-thirds (65%) of organizations and is critical as SAS expands its SAS LASR Analytic Server. Our benchmark research on operational intelligence shows that the processing of event data is critical for areas like activity or event monitoring (said 62% of participants) and alerting and notification (59%). SAS will need to expand its portfolio in these areas but it is fulfilling on what I call the four types of discovery for big data.
SAS also is moving deeper into cloud computing with support for both private and public clouds through investments in its own data centers. Cloud computing is an increasingly popular approach to building a sandbox environment for big data analytics. Our research finds that more than one-fifth of organizations prefer to use cloud computing in an on-demand approach. SAS will have to provide even more of its portfolio using big data in the cloud or risk customers turning to Amazon and others for processing and potentially other computing uses. SAS asserts it is investing and expanding in cloud computing.
SAS’s efforts to make it easier to work with big data and apply analytics is another smart bet; our research finds that most organizations today don’t have enough skilled resources in this area. One way to address this gap is to design software that is more intuitive, more visual and more interactive but sophisticated in how it works with the primitives of Hadoop; SAS is addressing this challenge. Our research finds growth of in-memory (now used by 42%) and Hadoop (50%) technologies, which will have more impact as they are applied directly to business needs and opportunities. SAS is at the intersection of data management and analytics for big data technologies, which could position it well for further growth in revenue. SAS is betting that big data will become a focal point in many deployments and they can help unify data and analytics across the enterprise. Our research agenda for 2014 finds this to be the big opportunity and SAS is fixated on being the vendor of choice for it. If you have not examined how SAS can connect big data architectures and facilitate use of this important technology, it will be worth your time to do so.
CEO & Chief Research Officer
Teradata recently gave me a technology update and a peek into the future of its portfolio for big data, information management and business analytics at its annual technology influencer summit. The company continues to innovate and build upon its Teradata 14 releases and its new processing technology. Since my last analysis of Teradata’s big data strategy, it has embraced technologies like Hadoop with its Teradata Aster Appliance, which won our 2012 Technology Innovation Award in Big Data. Teradata is steadily extending beyond providing just big data technology to offer a range of analytic options and appliances through advances in Teradata Aster and its overall data and analytic architectures. One example is its data warehouse appliance business, which according to our benchmark research is one of the key technological approaches to big data; as well Teradata has advanced support with its own technology offering for in-memory databases, specialized databases and Hadoop in one integrated architecture. It is taking an enterprise management approach to these technologies through Teradata Viewpoint, which helps monitor and manage systems and support a more distributed computing architecture.
By expanding its platform to include workload-based appliances that can support terabytes to petabytes of data, its Unified Data Architecture (UDA) can meet a broad class of enterprise needs. That can help support a range of big data analytic needs, as my colleague Tony Cosentino has pointed out, by providing a common approach to getting data from Hadoop into Teradata Aster and then into Teradata’s analytics. This UDA can begin to address challenges in data activities and tasks in the analytic process, which our research finds are issues for 42 percent of organizations. Teradata Aster Big Analytics Appliance is for organizations that are serious about retaining and analyzing more data, which 29 percent of organizations in our research cited as the top benefit of big data technology. This appliance can handle up to 5 petabytes and is tightly integrated with Aster and Hadoop technology from Hortonworks, a company that is rapidly expanding its footprint, as I have already assessed.
The packaged approach of an appliance can help organization address what our technology innovation research identified as the largest challenges in big data: not enough skilled resources (for 56% of organizations) and being hard to build and maintain (52%). These can be overcome if an organization designs a big data strategy that can apply a common set of skills, and the Teradata technology portfolio can help with that.
At the influencer summit, I was surprised that Teradata did not go into the role of data integration processes and the steps to profile, cleanse, master, synchronize and even migrate data (which its closest partner, Informatica, emphasizes) but focused more on access to and movement of data through its own connectors, Unity Data Mover, Smart Loader for Hadoop and support of SQL-H. For most of its deployments there is a range of complementary data integration technology from its partners as much as it is a Teradata only approach. For SQL-H Teradata takes advantage of the metadata HCatalog to improve access to data in HDFS. I like how Teradata Studio 14 helps simplify the view and use of data in Hadoop, Teradata Aster and even spreadsheets and flat files for building sandbox and test environments for big data. (To learn more, look into the Teradata Developer Exchange.) Teradata has made it easy to add connecters to get access to Hadoop on its Exchange which is a great way to get the latest advances in its utilities and add-ons to its offerings.
Teradata provided an early peak on the just announced Teradata Intelligent Memory, a significant step in adapting big data architectures to the next generation of memory management. This new advancement can cache and pool data that is in high demand (hot) across any number of Teradata workload-specific platforms by processing data to determine the importance of data (described as hot, warm or cold) for fast and efficient access and applying analytics. This technological feat can then utilize both solid-state and conventional disk storage to ensure the fastest access and computation of the data for a range of needs. This is a unique and powerful way to support an extended memory space for big data and to intelligently adapt to the data patterns of user organizations; its algorithms can interoperate across Teradata’s family of appliances.
Teradata has also invested further into its data and computing architecture through what it calls fabric-based computing. That can help connect nodes across systems through access on the company’s Fabric Switch using its BYNET, Infiniband and other methods. (Teradata participates in the OpenFabrics Alliance, which works to optimize access and interconnection of systems data across storage-area networks.) Fabric Switch provides an access point through which other aspects of Teradata’s UDA can access and use data for various purposes, including backup and restore or data movement. These advances will significantly increase the throughput and combined reliability of systems and enhance performance and scalability at both the user and data levels.
Tony Cosentino pointed out the various types of analytics that Teradata can support; one of them is analytics for discovery through its recently launched Teradata Aster Discovery Platform. This directly addresses two of the four types of discovery I have just outlined : data and visual discovery. Teradata Aster has a powerful library of analytics such as path, text, statistical, cluster and other areas as core elements of its platform. Its nPath analytic expression has significant potential in enabling Aster to process distributed sets of data from Teradata and Hadoop in one platform. Analytic architectures should apply the same computational analytics across systems, from core database technology to Teradata Aster to the analytics tools that an analyst is actually using. Aster’s approach to visual and data discovery is challenging in that it requires a high level of expertise in SQL to make customizations; the majority of analysts that could use this technology don’t have that level of knowledge. But here Teradata can turn to partners such as MicroStrategy and Tableau, which have built more integrated support for Teradata Aster and offer easier to use that are interactive and visual designed for analysts who do not want to muck with SQL. Teradata has internal challenges in improving support for analysts and the analytic processes they are responsible for; its IT-focused, data-centric approach will not help here. Our big data research finds that staffing and training are the top two barriers for using this technology, according to more than 77 percent of organizations; vendors should note this and reduce the custom and manual work that requires specific SQL and data skills in their products.
Regarding analytics specifically, Teradata has continued to deepen its analytics efforts with partner SAS. A new release of Teradata Appliance supports SAS High-Performance Analytics for up to 52 terabytes of data and also supports SAS Visual Analytics, which I have tried and assessed and tried myself.
Through its Teradata Aprimo applications Teradata continues its efforts to attract marketing executives in business-to-consumer companies that require big data technology to utilize a broad range of information. Teradata has outlined a larger role for the CMO with big data and analytics capabilities that go well beyond its marketing automation software. The company announced expansion to support predictive analytics and has outlined its direction for supporting customer engagement. It needs to take steps such as these to ensure it tunes into business needs beyond what CIOs and IT are doing with Teradata as a big data environment for the enterprise.
Along these lines I have also pointed out that we should be cautious about accepting research that predicts the CMO will outspend the CIO in the future. What I have seen in these assertions is flawed in many facets and often come from those who have no experience in market research and the role marketing and dealing with technology expenditure in that context. As we have done research into both the business and IT sides, we have discovered the complexities of making practical technology investments; for example, our research into customer relationship maturity found that inbound interactions from customers occur across many departments; they occur in marketing (in 46% of organizations), but more often through contact centers (77%), where Teradata should strengthen its efforts. On the plus side Teradata continues to demonstrate success in assisting customers in marketing, winning our 2013 Leadership Award for Marketing Excellence with its deployment at International Speedway Corp. and in 2012 at Nationwide Insurance with Teradata Aprimo. Our current research into next-generation customer engagement already identifies a need to support multichannel and multidepartment interactions. Teradata could further expand its efforts in these areas with existing customers; KPN won our 2013 Leadership Award in Customer Excellence after connecting Teradata with its Oracle-based applications and supporting BI systems.
Overall Teradata is doing a great job of focusing on its strengths in big data and areas where it can maximize the impact of its analytics, especially marketing and customer relations. While IBM, Oracle, SAP and other large technology providers in the database and analytic markets tend to minimize what Teradata has created, it is has a loyal customer base that is attracted to the expanded architectures of its appliances and its broader UDA and intelligent memory systems. I think with more focus on the processes of real business analysts and further simplifying usability, Teradata’s opportunity could grow significantly. In helping its customers process more of the vast volumes of data and information from the Internet, such as weather, demographic and social media, it could make clear the broader value of big data in optimizing information from the variety of data in content and documents. It could expand its new generation of tools and applications to exploit the use of this information as it is beginning to do with marketing applications from Teradata Aprimo. If Teradata customers find it easier to access information and share it across lines of business through social collaboration and mobile technology, that will further demand for its technology to operate on larger scales in both the number of users and the places where it can be accessed even via cloud computing. Exploiting in-memory computing along with providing more discovery potential from analytics will help its customers utilize the power of big data and trust in Teradata to supply it.
CEO & Chief Research Officer