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Our benchmark research into business technology innovation shows that analytics ranks first or second as a business technology innovation priority in 59 percent of organizations. Businesses are moving budgets and responsibilities for analytics closer to the sales operations, often in the form of so-calledvr_Big_Data_Analytics_15_new_technologies_enhance_analytics shadow IT organizations that report into decentralized and autonomous business units rather than a central IT organization. New technologies such as in-memory systems (50%), Hadoop (42%) and data warehouse appliances (33%) are top back-end technologies being used to acquire a new generation of analytic capabilities. They are enabling new possibilities including self-service analytics, mobile access, more collaborative interaction and real-time analytics. In 2014, Ventana Research helped lead the discussion around topics such as information optimization, data preparation, big data analytics and mobile business intelligence. In 2015, we will continue to cover these topics while adding new areas of innovation as they emerge.

Three key topics lead our 2015 business analytics research agenda. The first focuses on cloud-based analytics. In our benchmark research on information optimization, nearly all (97%) organizations said it is important or very important to Ventana_Research_Benchmark_Research_Logosimplify informa­tion access for both their business and their customers. Part of the challenge in optimizing an organization’s use of information is to integrate and analyze data that originates in the cloud or has been moved there. This issue has important implications for information presentation, where analytics are executed and whether business intelligence will continue to move to the cloud in more than a piecemeal fashion. We are currently exploring these topics in our new benchmark research called analytics and data in the cloud Coupled with the issue of cloud use is the proliferation of embedded analytics and the imperative for organizations to provide scalable analytics within the workflow of applications. A key question we’ll try to answer this year is whether companies that have focused primarily on operational cloud applications at the expense of developing their analytics portfolio or those that have focused more on analytics will gain a competitive advantage.

The second research agenda item is advanced analytics. It may be useful to divide this category into machine learning and predictive analytics, which I have discussed and covered in vr_predanalytics_benefits_of_predictive_analytics_updatedour benchmark research on big data analytics. Predictive analytics has long been available in some sectors of the business world, and two-thirds (68%) of organizations as found in our research that use it said it provides a competitive advantage. Programming languages such as R, the use of Predictive Model Markup Language (PMML), inclusion of social media data in prediction, massive scale simulation, and right-time integration of scoring at the point of decision-making are all important advances in this area. Machine learning also been around for a long time, but it wasn’t until the instrumentation of big data sources and advances in technology that it made sense to use in more than academic environments. At the same time as the technology landscape is evolving, it is getting more fragmented and complex; in order to simplify it, software designers will need innovative uses of machine learning to mask the underlying complexity through layers of abstraction. A technology such as Spark out of Amp-Lab at Berkeley is still immature, but it promises to enable increasing uses of machine learning on big data. Areas such as sourcing data and preparing data for analysis must be simplified so analysts are not overwhelmed by big data.

Our third area of focus is the user experience in business intelligence tools. Simplification and optimization of information in a context-sensitive manner are paramount. An intuitive user experience can advance the people and process dimensions VR_Value_Index_Logoof business, which have lagged technology innovation according to our research in multiple areas. New approaches coming from business end-users, especially in the tech-savvy millennial generation, are pushing the envelope here. In particular, mobility and collaboration are enabling new user experiences in both business organizations and society at large. Adding to it is data collected in more forms, such as location analytics (which we have done research on), individual and societal relationships, information and popular brands. How business intelligence tools incorporate such information and make it easy to prepare, design and consume for different organizational personas is not just an agenda focus but also one focus of our 2015 Analytics and Business Intelligence Value Index to be published in the first quarter of the year.

This shapes up as an exciting year. I welcome any feedback you have on this research agenda and look forward to providing research, collaborating and educating with you in 2015.

Regards,

Ventana Research

SAS Institute, a long-established provider analytics software, showed off its latest technology innovations and product road maps at its recent analyst conference. In a very competitive market, SAS is not standing still, and executives showed progress on the goals introduced at last year’s conference, which I coveredSAS’s Visual Analytics software, integrated with an in-memory analytics engine called LASR, remains the company’s flagship product in its modernized portfolio. CEO Jim Goodnight demonstrated Visual Analytics’ sophisticated integration with statistical capabilities, which is something the company sees as a differentiator going forward. The product already provides automated charting capabilities, forecasting and scenario analysis, and SAS probably has been doing user-experience testing, since the visual interactivity is better than what I saw last year. SAS has put Visual Analytics on a six-month release cadence, which is a fast pace but necessary to keep up with the industry.

Visual discovery alone is becoming an ante in the analytics market,vr_predanalytics_benefits_of_predictive_analytics_updated since just about every vendor has some sort of discovery product in its portfolio. For SAS to gain on its competitors, it must make advanced analytic capabilities part of the product. In this regard, Dr. Goodnight demonstrated the software’s visual statistics capabilities, which can switch quickly from visual discovery into regression analysis running multiple models simultaneously and then optimize the best model. The statistical product is scheduled for availability in the second half of this year. With the ability to automatically create multiple models and output summary statistics and model parameters, users can create and optimize models in a more timely fashion, so the information can be come actionable sooner. In our research on predictive analytics, the most participants (68%) cited competitive advantage as a benefit of predictive analytics, and companies that are able to update their models daily or more often, our research also shows, are very satisfied with their predictive analytics tools more often than others are. The ability to create models in an agile and timely manner is valuable for various uses in a range of industries.

There are three ways that SAS allows high performance computing. The first is the more traditional grid approach which distributes processing across multiple nodes. The second is the in-database approach that allows SAS to run as a process inside of the database. vr_Big_Data_Analytics_08_top_capabilities_of_big_data_analyticsThe third is extracting data and running it in-memory. The system has the flexibility to run on different large-scale database types such as MPP as well Hadoop infrastructure through PIG and HIVE. This is important because for 64 percent of organizations, the ability to run predictive analytics on big data is a priority, according to our recently released research on big data analytics. SAS can run via MapReduce or directly access the underlying Hadoop Distributed File System and pull the data into LASR, the SAS in-memory system. SAS works with almost all commercial Hadoop implementations, including Cloudera, Hortonworks, EMC’s Pivotal and IBM’s InfoSphere BigInsights. The ability to put analytical processes into the MapReduce paradigm is compelling as it enables predictive analytics on big data sets in Hadoop, though the immaturity of initiatives such as YARN may relegate the jobs to batch processing for the time being. The flexibility of LASR and the associated portfolio can help organizations overcome the challenge of architectural integration, which is the most widespread technological barrier to predictive analytics (for 55% of participants in that research). Of note is that the SAS approach provides purely analytical engine, and since there is no SQL involved in the algorithms, its overhead related to SQL is non-existent and it runs directly on the supporting system’s resources.

As well as innovating with Visual Analytics and Hadoop, SAS has a clear direction in its road map, intending to integrate the data integration and data quality aspects of the portfolio in a singlevr_Info_Optimization_04_basic_information_tasks_consume_time workflow with the Visual Analytics product. Indeed, data preparation is still a key sticking point for organizations. According to our benchmark research on information optimization, time spent in analytic tasks is still consumed most by data preparation (for 47%) and data quality and consistency (45%). The most valuable task, interpretation of the data, ranks fourth at 33 percent of analytics time. This is a big area of opportunity in the market, as reflected by the flurry of funding for data preparation software companies in the fourth quarter of 2013. For further analysis of SAS’s data management and big data efforts, please read my colleague Mark Smith’s analysis.

Established relationships with companies like Teradata and a reinvigorated relationship with SAP position SAS to remain at the heart of enterprise analytic architectures. In particular, the co-development effort that allow the SAS predictive analytic workbench to run on top of SAP HANA is promising, which raises the question of how aggressive SAP will be in advancing its own advanced analytic capabilities on HANA. One area where SAS could learn from SAP is in its developer ecosystem. While SAP has thousands of developers building applications for HANA, SAS could do a better job of providing the tools developers need to extend the SAS platform. SAS has been able to prosper with a walled-garden approach, but the breadth and depth of innovation across the technology and analytics industry puts this type of strategy under pressure.

Overall, SAS impressed me with what it has accomplished in the past year and the direction it is heading in. The broad-based development efforts raise a final question of where the company should focus its resources. Based on its progress in the past year, it seems that a lot has gone into visual analytics, visual statistics, LASR and alignment with the Hadoop ecosystem. In 2014, the company will continue horizontal development, but there is a renewed focus on specific analytic solutions as well. At a minimum, the company has good momentum in retail, fraud and risk management, and manufacturing. I’m encouraged by this industry-centric direction because I think that the industry needs to move away from the technology-oriented V’s toward the business-oriented W’s.

For customers already using SAS, the company’s road map is designed to capture market advantage with minimal disruption to existing environments. In particular, focusing on solutions as well as technological depth and breadth is a viable strategy. While it still may make sense for customers to look around at the innovation occurring in analytics, moving to a new system will often incur high switching costs in productivity as well as money. For companies just starting out with visual discovery or predictive analytics, SAS Visual Analytics provides a good point of entry, and SAS has a vision for more advanced analytics down the road.

Regards,

Tony Cosentino

VP and Research Director

SAS Institute held its 24th annual analyst summit last week in Steamboat Springs, Colorado. The 37-year-old privately held company is a key player in big data analytics, and company executives showed off their latest developments and product roadmaps. In particular, LASR Analytical Server and Visual Analytics 6.2, which is due to be released this summer, are critical to SAS’ ability to secure and expand its role as a preeminent analytics vendor in the big data era.

For SAS, the competitive advantage in Big Data rests in predictive vr_predanalytics_predictive_analytics_obstaclesanalytics, and according to our benchmark research into predictive analytics, 55 percent of businesses say the challenge of architectural integration is a top obstacle to rolling out predictive analytics in the organization. Integration of analytics is particularly daunting in a big-data-driven world, since analytics processing has traditionally taken place on a platform separate from where the data is stored, but now they must come together. How data is moved into parallelized systems and how analytics are consumed by business users are key questions in the market today that SAS is looking to address with its LASR and Visual Analytics.

Jim Goodnight, the company’s founder and plainspoken CEO, says he saw the industry changing a few years ago. He speaks of a large bank doing a heavy analytical risk computation that took upwards of 18 hours, which meant that the results of the computation were not ready in time for the next trading day. To gain competitive advantage, the time window needed to be reduced, but running the analytics in a serialized fashion was a limiting factor. This led SAS to begin parallelizing the company’s workhorse procedures, some of which were first developed upwards of 30 years ago. Goodnight also discussed the fact that building these parallelizing statistical models is no easy task. One of the biggest hurdles is getting the mathematicians and data scientists that are building these elaborate models to think in terms of the new parallelized architectural paradigm.

Its Visual Analytics software is a key component of the SAS Big Data Analytics strategy. Our latest business technology innovation benchmark research [http://www.ventanaresearch.com/bti/] found that close to half (48%) of organizations present business analytics visually. Visual Analytics, which was introduced early last year, is a cloud-based offering running off of the LASR in-memory analytic engine and the Amazon Web Services infrastructure. This web-based approach allows SAS to iterate quickly without worrying a great deal about revision management while giving IT a simpler server management scenario. Furthermore, the web-based approach provides analysts with a sandbox environment for working with and visualizing in the cloud big data analytics; the analytic assets can then be moved into a production environment. This approach will also eventually allow SAS to combine data integration capabilities with the data analysis capabilities.

With descriptive statistics being the ante in today’s visual discovery world, SAS is focusing Visual Analytics to take advantage of the vr_bigdata_obstacles_to_big_data_analytics (2)company’s predictive analytics history and capabilities. Visual Analytics 6.2 integrates predictive analytics and rapid predictive modeling (RPM) to do, among other things, segmentation, propensity modeling and forecasting. RPM makes it possible for models to be generated via sophisticated software that runs through multiple algorithms to find the best fit based on the data involved. This type of commodity modeling approach will likely gain significant traction as companies look to bring analytics into industrial processes and address the skills gap in advanced analytics. According to our BTI research, the skills gap is the biggest challenge facing big data analytics today, as participants identified staffing (79%) and training (77%) as the top two challenges.

Visual Analytics’ web-based approach is likely a good long-term bet for SAS, as it marries data integration and cloud strategies. These factors, coupled with the company’s installed base and army of loyal users, give SAS a head start in redefining the world of analytics. Its focus on integrating visual analytics for data discovery, integration and commodity modeling approaches also provides compelling time-to-value for big data analytics. In specific areas such as marketing analytics, the ability to bring analytics into the applications themselves and allow data-savvy marketers to conduct a segmentation and propensity analysis in the context of a specific campaign can be a real advantage. Many of SAS’ innovations cannibalize its own markets, but such is the dilemma of any major analytics company today.

The biggest threat to SAS today is the open source movement, which offers big data analytic approaches such as Mahout and R. For instance, the latest release of R includes facilities for building parallelized code. While academics working in R often still build their models in a non-parallelized, non-industrial fashion, the current and future releases of R promise more industrialization. As integration of Hadoop into today’s architectures becomes more common, staffing and skillsets are often a larger obstacle than the software budget. In this environment the large services companies loom larger because of their role in defining the direction of big data analytics. Currently, SAS partners with companies such as Accenture and Deloitte, but in many instances these companies have split loyalties. For this reason, the lack of a large in-house services and education arm may work against SAS.

At the same time, SAS possesses blueprints for major analytic processes across different industries as well as horizontal analytic deployments, and it is working to move these to a parallelized environment. This may prove to be a differentiator in the battle versus R, since it is unclear how quickly the open source R community, which is still primarily academic, will undertake the parallelization of R’s algorithms.

SAS partners closely with database appliance vendors such as Greenplum and Teradata, with which it has had longstanding development relationships. With Teradata, it integrates into the BYNET messaging system, allowing for optimized performance between Teradata’s relational database and the LASR Analytic Server. Hadoop is also supported in the SAS reference architecture. LASR accesses HDFS directly and can run as a thin memory layer on top of the Hadoop deployment. In this type of deployment, Hadoop takes care of everything outside the analytic processing, including memory management, job control and workload management.

These latest developments will be of keen interest to SAS customers. Non-SAS customers who are exploring advanced analytics in a big data environment should consider SAS LASR and its MPP approach. Visual Analytics follows the “freemium” model that is prevalent in the market, and since it is web-based, any instances downloaded today can be automatically upgraded when the new version arrives in the summer. For the price, the tool is certainly worth a test drive for analysts. For anyone looking into such tools and foresee the need for inclusion predictive analytics, it should be of particular interest.

Regards,

Tony Cosentino
VP and Research Director

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