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At the Informatica World 2014 conference, the company known for its data integration software unveiled the Intelligent Data Platform. In the last three years Informatica has expanded beyond data integration and now has a broad software portfolio that facilitates information management within the enterprise and through cloud computing. The Intelligent Data Platform forms a framework for its portfolio. This expression of broad potential is important for Informatica, which has been slow to position its products as capable of more than data integration. A large part of the value it provides lies in what its products can do to help organizations strengthen their enterprise architectures for managing applications and data. We see Informatica’s sweet spot in facilitating efficient use of data for business and IT purposes; we call this information optimization.
Informatica’s Intelligent Data Platform is built in three layers. The bottom layer is Informatica Vibe, the virtual data machine that I covered at its launch last year. Informatica Vibe won our Ventana Research 2013 Technology Innovation Award for information optimization. It virtualizes information management technology to operate on any platform whether on-premises or in any form of cloud computing.
Above Informatica Vibe in the platform is a data infrastructure layer, which contains all the technologies that act upon data, from integration through archiving, masking, mastering, quality assurance, security, streaming and other tasks. At the core of this second layer is Informatica PowerCenter, which provides data integration and other capabilities central to processing of data into information. PowerCenter provides parsing, profiling, joining and filtering but also is integral for data services through Informatica’s Data Integration Hub that operates in a publish-and-subscribe model. The latest PowerCenter release, version 9.6, focuses on providing agility in development and provides a series of packaged editions that provide certain levels of functionality; users choose among them to fit their requirements. This developer support includes advances in test data management and data masking for enterprise-class needs. There are editions for Informatica Data Quality, too. The latest release of Informatica MDM, 9.7, improves the user experience for data stewards along with enhanced performance and governance. Not much was mentioned at the conference about Informatica’s Product Information Management (PIM) offering that our most recent Value Index vendor and product assessment rated Hot.
The third layer is data intelligence. Here Informatica has added capabilities to organize, infer and recommend action from data and to provision and map data to business needs. In addition Informatica’s Business Glossary and Metadata Manager help establish consistent definitions and use of data for operational or analytical tasks. Informatica RulePoint, a product that also was not mentioned much at the conference, processes events through workflow in a continuous rule based manner; depending on how processing occurs, its function is to support complex event processing or event streaming.
On top of the Intelligent Data Platform, Informatica has added a couple of new innovations. Project Springbok, which is not yet released, is a tool for preparation of data for analytics and operations through its Innovation division. This new product will use Informatica’s expertise in providing access to and integration of data sources, which according to our information optimization benchmark research is the top analyst requirement in 39 percent of organizations. Despite data warehouse efforts, analysts and business users still have to access many data sources. Simplifying information is critical for nearly all organizations that have more than 16 data sources. Demonstrations showed that Springbok can dynamically create and automate the transformations that run in PowerCenter. It also offers access to a master reference to ensure that data is processed in a consistent manner. IT professionals gain visibility into what business units are doing to show how they can help in provisioning data. Even in beta release Springbok has significant potential to address the range of data issues analysts face and reduce the time they spend on data-related tasks. Our research has shown for several years that this data challenge presses organizations to diversify the tools they use, and software vendors in this market have responded. Informatica will have to compete with more than a dozen others and demonstrate its superiority for integration. Our research finds that the lines of business and IT now share responsibility for information availability in 42 percent of organizations. Informatica will have to demonstrate its value to line of business analysts who are evaluating a new generation of tools for data and analytics.
A second innovation is a new data security product called Secure@Source, also being developed in the Innovation unit, is designed to protect data assets where they are stored and processed. This product moves Informatica into the information security market segment. Secure@Source helps users discover, detect, assess and protect data assets in their persistent locations and during consumption by applications or Internet services. The question is whether Informatica can convince current customers to examine it or will have to approach information security professionals who are not users of Informatica. Security of data is among the top five required data activities according to our research and a key part of the manageability requirements that organizations find important in considering products. Informatica has an opportunity to insert itself into the dialogue in this area if it properly presents the new product to IT and business people alike.
In big data Informatica has made steady progress, but to reach its potential in this segment will require more investments in the mixed big data environments, not just Hadoop. As our research has shown for three years, customers want big data to distribute processing and integration of data across sources. Our recent research on big data analytics finds that three out of four (76%) define big data analytics as being about accessing and analyzing all sources of data. This poses a challenge for data integration, and our new research on big data integration finds that most have a long way to go in accessibility and mastering of data. Informatica begins to address this and has an opportunity in helping develop a new generation of data architecture.
In cloud computing, the company has consolidated its efforts to ensure that the cloud is part of its core technology. It released new versions for its cloud-based integration, quality, master and real-time data management products; these begin to address the challenge of process and application integration, which are important considerations for businesses in determining whether integrate or replace point cloud solutions to improve efficiency of tasks and business processes. Informatica has continued to focus on integrating mostly with the large cloud computing providers and has yet to invest in streamlining processes in particular lines of business. This has left openings for other cloud integration providers to compete, making it harder than expected for Informatica to dominate in this segment. The next step here is up to Informatica.
I believe that one of the highest potential opportunities for Informatica is in the application architectures of organizations whose business processes have been distributed through a collection of cloud-based applications that lack interconnectivity and integration. For example, finance departments often have software from different providers for budgeting and planning, consolidation and reporting, accounting and payroll management. When these applications are spread across the cloud, connecting them is a real challenge, let alone trying to get information from sales force automation and customer service applications. The implications of this are shown in our finance analytics research : Data-related tasks consume the most time and impede the efficiency of financial processes as they do in all other line of business areas that we have researched. Similar situations exist in customer-related areas (marketing, sales and customer service) and employee management processes (recruiting, onboarding, performance, compensation and learning). Informatica has made progress with Informatica Cloud Extend for interconnecting tasks across applications, which can help streamline processes. While perhaps not obvious to data integration specialists, this level of process automation and integration is essential to the future of cloud computing. Informatica also announced it will offer master data management in the cloud; this should help it not just to place a data hub in the cloud but to help companies interoperate separate cloud applications more efficiently.
Overall the Informatica Intelligent Data Platform is a good reference model for tasks related to turning data into information assets. But it could be much distinct in how its automation accelerates the processing of data faster and helps specific roles work faster and smarter. This platform does not provide a context for enterprise architectures that are stretched between on-premises and various cloud deployments. Organizations will have to determine whether Informatica’s approach fits their future data and architectural needs. As Informatica pushes its platform approach, it has to ensure it is seen as a leader in big data integration, helping business analysts with data, supporting a larger number of application sources and connecting cloud computing through unifying business applications. This won’t be easy to accomplish as Informatica has not been as progressive in the broader approach to big data and use across operations and analytics.
Informatica has been growing substantially and is getting close to US$1 billion in annual software revenue. We have recognized its success through rating it a Hot vendor in our Data Integration Value Index and naming one of its customers, the CIO of UMass Memorial Health Care, the Chief Information Officer in our 2013 Leadership Awards. Informatica has been continuing substantial investment in R&D. Its acquisitions of data-related software companies have helped it grow, and Informatica has invested to integrate the products with PowerCenter. With almost half (49%) of organizations planning to change their information availability processes, the opportunity for Informatica is significant; its challenge is to gain the confidence and recognition by business customers, who now play a larger role in the selection and purchasing of software. This will require Informatica to speak their language of business and not just technology but the business processes that they are held accountable. Informatica is a major player in information management; now it must become as significant a choice for streamlining business processes and use of applications and data across the enterprise and cloud computing to enable information optimization.
CEO & Chief Research Officer
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