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Organizations should consider multiple aspects of deploying big data analytics. These include the type of analytics to be deployed, how the analytics will be deployed technologically and who must be involved both internally and externally to enable success. Our recent big data analytics benchmark research assesses each of these areas. How an organization views these deployment considerations may depend on the expected benefits of the big data analytics program and the particular business case to be made, which I discussed recently.

According to the research, the most important capability of big data analytics is predictive analytics (64%), but among companies vr_Big_Data_Analytics_08_top_capabilities_of_big_data_analyticsthat have deployed big data analytics, descriptive analytic approaches of query and reporting (74%) and data discovery (64%) are more readily available than predictive capabilities (57%). Such statistics may be a function of big data technologies such as Hadoop, and their associated distributions having prioritized the ability to run descriptive statistics through standard SQL, which is the most common method for implementing analysis on Hadoop. Cloudera’s Impala, Hortonworks’ Stinger (an extension of Apache Hive), MapR’s Drill, IBM’s Big SQL, Pivotal’s HAWQ and Facebook’s open-source contribution of Presto SQL all focus on accessing data through an SQL paradigm. It is not surprising then that the technology research participants use most for big data analytics is business intelligence (75%) and that the most-used analytic methods — pivot tables (46%), classification (39%) and clustering (37%) — are descriptive and exploratory in nature. Similarly, participants said that visualization of big data allows analysts to perform faster analysis (49%), understand context better (48%), perform root-cause analysis (40%) and display multiple result sets (40%), but visualization does not provide more advanced analytic capabilities. While various vendors now offer approaches to run advanced analytics on big data, the research shows that in terms of big data, organizational capabilities still revolve around more basic analytic access.

For companies that are implementing advanced analytic capabilities on big data, there are further analytic process considerations, and many have not yet tackled those. Model building and model deployment should be manageable and timely, involve specialized personnel, and integrate into the broader enterprise architecture. While our research provides an in-depth look at adoption of the different types of in-database analytics, deployment of advanced analytic sandboxes, data mining, model management, integration with business processes and overall model deployment, that is beyond the topic here.

Beyond analytic considerations, a host of technological decisionsvr_Big_Data_Analytics_13_advanced_analytics_on_big_data must be made around big data analytics initiatives. One of these is the degree of customization necessary. As technology advances, customization is giving way to more packaged approaches to big data analytics. According to our research, the majority (54%) of companies that have already implemented big data analytics did custom builds using big data-specific languages and interfaces. The most of those that have not yet deployed are likely to purchase a dedicated or packaged application (44%), followed by a custom build (36%). We think that this pre- and post-deployment comparison reflects a maturing market.

The move from custom approaches to standardized ones has important implications for the skills sets needed for a big data vr_Big_Data_Analytics_14_big_data_analytics_skillsanalytics initiative. In comparing the skills that organizations said they currently have to the skills they need to be successful with big data analytics, it is clear that companies should spend more time building employees’ statistical, mathematical and visualization skills. On the flip side, organizations should make sure their tools can support skill sets that they already have, such as use of spreadsheets and SQL. This is convergent with other findings about training needs, which include applying analytics to business problems (54%), training on big data analytics tools (53%), analytic concepts and techniques (46%) and visualizing big data (41%). The data shows that as approaches become more standardized and the market focus shifts toward them from customized implementations, skill needs are shifting as well. This is not to say that demand is moving away from the data scientist completely. According to our research, organizations that involve cross-functional teams or data scientists in the deployment process are realizing the most significant impact. It is clear that multiple approaches for personnel, departments and current vendors play a role in deployments and that some approaches will be more effective than others.

Cloud computing is another key consideration with respect to deploying analytics systems as well as sandbox modelling and testing environments. For deployment of big data analytics, 27 percent of companies currently use a cloud-based method, while 58 percent said they do not and 16 percent do not know what is used. Not surprisingly, far fewer IT professionals (19%) than business users (40%) said they use cloud-based deployments for big data analytics. The flexibility and capability that cloud resources provide is particularly attractive for sandbox environments and for organizations that lack big data analytic expertise. However, for big data model building, most organizations (42%) still utilize a dedicated internal sandbox environment to build models while fewer (19%) use a non-dedicated internal sandbox (that is, a container in a data warehouse used to build models) and others use a cloud-based sandbox either as a completely separate physical environment (9%) or as a hybrid approach (9%). From this last data we infer that business users are sometimes using cloud-based systems to do big data analytics without the knowledge of IT staff. Among organizations that are not using cloud-based systems for big data analytics, security (45%) is the primary reason that they do not.

Perhaps the most important consideration for big data analytics is choosing vendors to partner with to achieve organizational objectives. When we understand the move from custom technological approaches to more packaged ones and the types of analytics currently being implemented for big data, it is not surprising that a majority of research participants (52%) are looking to their business intelligence systems providers to supply their big data analytics solution. However, a significant number of companies (35%) said they will turn to a specialist analytics provider or their database provider (34%). When evaluating big data analytics, usability is the most important vendor consideration but not by as wide a margin as in categories such as business intelligence. A look at criteria rated important and very important by research participants reveals usability is the highest ranked (94%), but functionality (92%) and reliability (90%) follow closely. Among innovative new technologies, collaboration is important (78%) while mobile access (46%) is much less so. Coupled with the finding that communication and knowledge sharing combined is an important benefit of big data analytics, it is clear that organizations are cognizant of the collaborative imperative when choosing a big data analytics product.

Deployment of big data analytics starts with forethought and a well-defined business case that includes the expected benefits I discussed in my previous analysis. Once the outcome-driven framework is established, organizations should consider the types of analytics needed, the enabling technologies and the people and processes necessary for implementation. To learn more about our big data analytics research, download a copy of the executive summary here.

Regards,

Tony Cosentino

VP & Research Director

SAP recently presented its analytics and business intelligence roadmap and new innovations to about 1,700 customers and partners using SAP BusinessObjects at its SAP Insider event (#BI2014). SAP has one of the largest presences in business intelligence due to its installed base of SAP BusinessObjects customers. The company intends to defend its current position in the established business intelligence (BI) market while expanding in the areas of databases, discovery analytics and advanced analytics. As I discussed a year ago, SAP faces an innovator’s dilemma in parts of its portfolio, but it is working aggressively to get ahead of competitors.

vr_bti_br_technology_innovation_prioritiesOne of the pressures that SAP faces is from a new class of software that is designed for business analytics and enables users to visualize and interact on data in new ways without relationships in the data being predefined. Our business technology innovation research shows that analytics is the top-ranked technology innovation in business today, rated first by 39 percent of organizations. In conventional BI systems, data is modeled in so-called cubes or other defined structures that allow users to slice and dice data quickly and easily. The cube structure solves the problem of abstracting the complexity of the structured query language (SQL) of the database and slashes the amount of time it takes to read data from a row-oriented database. However, as the cost of memory decreases significantly, enabling the use of new column-oriented databases, these methods of BI are being challenged. For SAP and other established business intelligence providers, this situation represents both an opportunity and a challenge. In responding, almost all of these BI companies have introduced some sort of visual discovery capability. SAP introduced SAP Lumira, formerly known as Visual Intelligence, 18 months ago to compete in this emerging segment, and it has gained traction in terms of downloads, which the company estimated at 365,000 in the fourth quarter of 2013.

SAP and other large players in analytics are trying not just to catch up with visual discovery players such as Tableau but rather to make it a game of leapfrog. Toward that end, the capabilities of Lumira demonstrated at the Insider conference included information security and governance, advanced analytics, integrated data preparation, storyboarding and infographics; the aim is to create a differentiated position for the tool. For me, the storyboarding and infographics capabilities are about catching up, but being able to govern and secure today’s analytic platforms is a critical concern for organizations, and SAP means to capitalize on them. A major analytic announcement at the conference focused on the integration of Lumira with the BusinessObjects platform. Lumira users now can create content and save it to the BusinessObjects server, mash up data and deliver the results through a secure common interface.

Beyond the integration of security and governance with discovery analytics, the leapfrog approach centers on advanced analytics. SAP’s acquisition last year of KXEN and its initial integration with Lumira provide an advanced analytics tool that does not require a data scientist to use it. My coverage of KXEN prior to the acquisition revealed that the tool was user-friendly and broadly applicable especially in the area of marketing analytics. Used with Lumira, KXEN will ultimately provide front-end integration for in-database analytic approaches and for more advanced techniques. Currently, for data scientists to run advanced analytics on large data sets, SAP provides its own predictive analytic library (PAL), which runs natively on SAP HANA and offers commonly used algorithms such as clustering, classification and time-series. Integration with the R language is available through a wrapper approach, but the system overhead is greater when compared to the PAL approach on HANA.

The broader vision for Lumira and the BusinessObjects analytics platform SAP said is “collective intelligence,” which it described as “a Wikipedia for business” that provides a bidirectional analytic and communication platform. To achieve this lofty goal, SAP will vr_Big_Data_Analytics_02_defining_big_data_analyticsneed to continue to put resources into HANA and facilitate the integration of underlying data sources. Our recently released research on big data analytics shows that being able to analyze data from all data sources (selected by 75% of participants) is the most prevalent definition for big data analytics. To this end, SAP announced the idea of an “in-memory fabric” that allows virtual data access to multiple underlying data sources including big data platforms such as Hadoop. The key feature of this data federation approach is what the company calls smart data access (SDA). Instead of loading all data into memory, the virtualized system sets a proxy that points to where specific data is held. Using machine learning algorithms, it can define how important information is based on the query patterns of users and upload the most important data into memory. The approach will enable the company to analyze data on a massive scale since utilizing both HANA and the Sybase IQ columnar database which the company says was just certified as the world record for the largest data warehouse, at more than 12 petabytes. Others such as eBay and Teradata may beg to differ with the result based on another implementation, but nevertheless it is an impressive achievement.

Another key announcement was SAP Business Warehouse (BW) 7.4, which now runs on top of HANA. This combination is likely to be popular because it enables migration of the underlying database without impacting business users. Such users store many of their KPIs and complex calculations in BW, and to uproot this system is untenable for many organizations. SAP’s ability to continue support for these users is therefore something of an imperative. The upgrade to 7.4 also provides advances in capability and usability. The ability to do complex calculations at the database level without impacting the application layer enables much faster time-to-value for SAP analytic applications. Relative to the in-memory fabric and SDA discussed above, BW users no longer need intimate knowledge of HANA SDA. The complete data model is now exposed to HANA as an information cube object, and HANA data can be reflected back into BW. To back it up, the company offered testimony from users. Representatives of Molson Coors said their new system took only a weekend to move into production (after six weeks of sandbox experiments and six weeks of development) and enables users to perform right-time financial reporting, rapid prototyping and customer sentiment analysis.

SAP’s advancements and portfolio expansion are necessary for it to continue in a leadership position, but the inherent risk is confusion amongst its customer and prospect base.  SAP published its last statement of direction for analytic dashboard about this time last year, and according to company executives, it will be updated fairly soon, though they would not specify when. The many tools in the portfolio include Web Intelligence, Crystal Reports, Explorer, Xcelsius and now Lumira. SAP and its partners position the portfolio as a toolbox in which each tool is meant to solve a different organizational need. There is overlap among them, however, and the inherent complexity of the toolbox approach may not resonate well with business users who desire simplicity and timeliness.

SAP customers and others considering SAP should carefully examine how well these tools match the skills in their organizations. We encourage companies to look at the different organizationalVRMobileBIVI roles as analytic personas and try to understand which constituencies are served by which parts of the SAP portfolio. For instance, one of the most critical personas going forward is the Designer role since usability is the top priority for organizational software according to our next-generation business intelligence research. Yet this role may become more difficult to fill over time since trends such as mobility continue to add to the job requirement. SAP’s recent upgrade of Design Studio to address emerging needs such as mobility and mobile device management (MDM) may force some organizations to rebuild  dashboards and upscale their designer skill sets to include JavaScript and Cascading Style Sheets, but the ability to deliver multifunctional analytics across devices in a secure manner is becoming paramount. I note that SAP’s capabilities in this regard helped it score third overall in our 2014 Mobile Business Intelligence Value Index. Other key personas are the knowledge worker and the analyst. Our data analytics research shows that while SQL and Excel skills are abundant in organizations, statistical skills and mathematical skills are less common. SAP’s integration of KXEN into Lumira can help organizations develop these personas.

SAP is pursuing an expansive analytic strategy that includes not just traditional business intelligence but databases, discovery analytics and advanced analytics. Any company that has SAP installed, especially those with BusinessObjects or an SAP ERP system, should consider the broader analytic portfolio and how it can meet business goals. Even for new prospects, the portfolio can be compelling, and as the roadmap centered on Lumira develops, SAP may be able to take that big leap in the analytics market.

Regards,

Tony Cosentino

VP and Research Director

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

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