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

We recently released our benchmark research on big data analytics, and it sheds light on many of the most important discussions occurring in business technology today. The study’s structure was based on the big data analytics framework that I laid out last year as well as the framework that my colleague Mark Smith put forth on the four types of discovery technology available. These frameworks view big data and analytics as part of a major change that includes a movement from designed data to organic data, the bringing together of analytics and data in a single system, and a corresponding move away from the technology-oriented three Vs of big data to the business-oriented three Ws of data. Our big data analytics research confirms these trends but also reveals some important subtleties and new findings with respect to this important emerging market. I want to share three of the most interesting and even surprising results and their implications for the big data analytics market.

First, we note that communication and knowledge sharing is a primary vr_Big_Data_Analytics_06_benefits_realized_from_big_data_analyticsbenefit of big data analytics initiatives, but it is a latent one. Among organizations planning to deploy big data analytics, the benefits most often anticipated are faster response to opportunities and threats (57%), improving efficiency (57%), improving the customer experience (48%) and gaining competitive advantage (43%). However, once a big data analytics system has moved into production, the benefits most often mentioned as achieved are better communication and knowledge sharing (51%), gaining competitive advantage (51%), improved efficiency in business processes (49%) and improved customer experience and satisfaction (46%). (The chart shows rankings of first choices as most important.) Although the last three of these benefits are predictable, it’s noteworthy that the benefit of communication and knowledge sharing, while not a priority before deployment, becomes one of the two most often cited later.

As for the implications, in our view, one reason why communication and knowledge sharing are more often seen as a key benefit after deployment rather than before is that agreement on big data analytics terminology is often lacking within organizations. Participants from fewer than half (44%) of organizations said that the people making business technology decisions mostly agree or completely agree on the meaning of big data analytics, while the same number said there are many different opinions about its meaning. To address this particular challenge, companies should pay more attention to setting up internal communication structures prior to the launch of a big data analytics project, and we expect collaborative technologies to play a larger role in these initiatives going forward.

vr_Big_Data_Analytics_02_defining_big_data_analyticsA second finding of our research is that integration of distributed data is the most important enabler of big data analytics. Asked the meaning of big data analytics in terms of capabilities, the largest percentage (76%) of participants said it involves analyzing data from all sources rather than just one, while for 55 percent it means analyzing all of the data rather than just a sample of it. (We allowed multiple responses.) More than half (56%) told us they view big data as finding patterns in large and diverse data sets in Hadoop, which indicates the continuing influence of this original big data technology. A second tier of percentages emphasizes timeliness as an aspect of big data: doing real-time processing on streams of data (44%), visualizing large structured data sets in seconds (40%) and doing real-time scoring against a database record (36%).

The implications here are that the primary characteristic of big data analytics technology is the ability to analyze data from many data sources. This shows that companies today are focused on bringing together multiple information sources and secondarily being able to process all data rather than just a sample, as well as being able to do machine learning on especially large data sets. Fast processing and the ability to analyze streams of data are relegated to third position in these priorities. That suggests that the so-called three Vs of big data are confusing the discussion by prioritizing volume, velocity and variety all at once. For companies engaged in big data analytics today, sourcing and integration of various data sources in an expedient manner is the top priority, followed by the ideas of size and then speed of arrival of data.

Third, we found that usage is not relegated to particular industries, vr_Big_Data_Analytics_09_use_cases_for_big_data_analyticscertain types of companies or certain functional areas. From among 25 uses for big data analytics those that participants are personally involved with, three of the four most often mentioned involve customers and sales: enabling cross-selling and up-selling (38%), understanding the customer better (32%) and optimizing pricing (28%). Meanwhile, optimizing IT operations ranked fifth (24%) though it was most often chosen by those in IT roles (76%). What is particularly fascinating, however, is that 17 of the 25 use cases were named by more than 10 percent, which indicates many uses for big data analytics.

The primary implication of this finding is that big data analytics is not following the famous technology adoption curves outlined in books such as Geoffrey Moore’s seminal work, “Crossing the Chasm.” That is, companies are not following a narrowly defined path that solves only one particular problem. Instead, they are creatively deploying technological innovations en route to a diverse set of outcomes. And this is occurring across organizational functions and industries, including conservative ones, which conflicts with conventional wisdom. For this reason, companies are more often looking across industries and functional disciplines as part of their due diligence on big data analytics to come up with unique applications that may yield competitive advantage or organizational efficiencies.

In summary, it has been difficult for companies to define what big data analytics actually means and how to prioritize their investments accordingly. Research such as ours can help organizations address this issue. While the above discussion outlines a few of the interesting findings of this research, it also yields many more insights, related to aspects as diverse as big data in the cloud, sandbox environments, embedded predictive analytics, the most important data sources in use, and the challenges of choosing an architecture and deploying big data analytic products. For a copy of the executive summary download it directly from the Ventana Research community.

Regards,

Tony Cosentino

VP and Research Director

Ventana Research recently completed the most comprehensiveVRMobileBIVI evaluation of mobile business intelligence products and vendors available anywhere today. The evaluation includes 16 technology vendors’ offerings on smartphones and tablets and use across Apple, Google Android, Microsoft Surface and RIM BlackBerry that were assessed in seven key categories: usability, manageability, reliability, capability, adaptability, vendor validation and TCO and ROI. The result is our Value Index for Mobile Business Intelligence in 2014. The analysis shows that the top supplier is MicroStrategy, which qualifies as a Hot vendor and is followed by 10 other Hot vendors: IBM, SAP, QlikTech, Information Builders, Yellowfin, Tableau Software, Roambi, SAS, Oracle and arcplan.

Our expertise, hands on experience and the buyer research from our benchmark research on next-generation business intelligence and on information optimization informed our product evaluations in this new Value Index. The research examined business intelligence on mobile technology to determine organizations’ current and planned use and the capabilities required for successful deployment.

What we found was wide interest in mobile business intelligence and a desire to improve the use of information in 40 percent of organizations, though adoption is less pervasive than interest. Fewer than half of organizations currently access BI capabilities on mobile devices, but nearly three-quarters (71%) expect their mobile workforce to be able to access BI capabilities in the next 12 months. The research also shows strong executive support: Nearly half of executives said that mobility is very important to their BI processes.

Mobile_BI_Weighted_OverallEase of access and use are an important criteria in this Value Index because the largest percentage of organizations identified usability as an important factor in evaluations of mobile business intelligence applications. This is an emphasis that we find in most of our research, and in this case it also may reflect users’ experience with first-generation business intelligence on mobile devices; not all those applications were optimized for touch-screen interfaces and designed to support gestures. It is clear that today’s mobile workforce requires the ability to access and analyze data simply and in a straightforward manner, using an intuitive interface.

The top five companies’ products in our 2014 Mobile Business Intelligence Value Index all provide strong user experiences and functionality. MicroStrategy stood out across the board, finishing first in five categories and most notably in the areas of user experience, mobile application development and presentation of information. IBM, the second-place finisher, has made significant progress in mobile BI with six releases in the past year, adding support for Android, advanced security features and an extensible visualization library. SAP’s steady support for the mobile access to SAP BusinessObjects platform and support for access to SAP Lumira, and its integrated mobile device management software helped produce high scores in various categories and put it in third place. QlikTech’s flexible offline deployment capabilities for the iPad and its high ranking in assurance-related category of TCO and ROI secured it the fourth spot. Information Builders’ latest release of WebFOCUS renders content directly with HTML5 and its Active Technologies and Mobile Faves, the company delivers strong mobile capabilities and rounds out the top five ranked companies. Other noteworthy innovations in mobile BI include Yellowfin’s collaboration technology, Roambi’s use of storyboarding in its Flow application.

Although there is some commonality in how vendors provide mobile access to data, there are many differences among their offerings that can make one a better fit than another for an organization’s particular needs. For example, companies that want their mobile workforce to be able to engage in root-cause discovery analysis may prefer tools from Tableau and QlikTech. For large companies looking for a custom application approach, MicroStrategy or Roambi may be good choices, while others looking for streamlined collaboration on mobile devices may prefer Yellowfin. Many companies may base the decision on mobile business intelligence on which vendor they currently have installed. Customers with large implementations from IBM, SAP or Information Builders will be reassured to find that these companies have made mobility a critical focus.

To learn more about this research and to download a free executive summary, please visit http://www.ventanaresearch.com/bivalueindex/.

Regards,

Tony Cosentino

Vice President and Research Director

Our recently released benchmark research on information optimization shows that 97 percent of organizations find it important or very important to make information available to the business and customers, Ventana_Research_Benchmark_Research_Logoyet only 25 percent are satisfied with the technology they use to provide that access. This wide gap between importance and satisfaction reflects the complexity of preparing and presenting information in a world where users need to access many forms of data that exist across distributed systems.

Information optimization is a new focus in the enterprise software market. It builds on existing investments in business applications, business intelligence and information management and also benefits from recent advances in business analytics and big data, lifting information to higher levels of use and greater value in organizations. Information optimization also builds on information management and information applications, areas Ventana Research has previously researched. For more on the background and definition of information optimization, please see my colleague Mark Smith’s foundational analysis.

vr_Info_Optimization_01_whos_responsible_for_information_availabilityThe drive to improve information availability derives from a need for greater operational efficiency, according to two-thirds (67%) of organizations. The imperative is so strong that 43 percent of all organizations currently are making changes to how they design and deploy information, while another 37 percent plan to make changes in the next 12 months. The pressure for such change is being directed toward the IT group, which is involved with the task of optimizing information in more than four-fifths of organizations with or without line of business support. IT, however, is in an untenable position, as demands are far outstripping its available resources and technology to deal with the problem, which leads to dissatisfaction with the IT department in two out of five organizations, according to our research. Internally, many organizations try to optimize information using manual spreadsheet processes and are confident in their ability to get by 73% of the time. But when the focus turns to the ability to make information available to partners or customers, an increasingly important capability in today’s information-driven economy, the confidence rate drops dramatically to 62% and 55% respectively.

A large part of the information optimization challenge is users’ vr_Info_Optimization_09_most_important_end_user_capabilitiesdifferent requirements. For instance, the top needs of analysts are extracting information, designing and integrating metrics, and developing access policies. In contrast, the top needs of business users are drilling into information (37%), search capabilities (36%) and collaboration (27%). IT must also consider multiple points of integration such as security frameworks and information modeling, as well as integration with operational and content management systems. This is complicated further by multiple new standards coming into play as customer and financial data – still the most important information systems in the organization – append less structured sources of data that add context and value. SQL is still the dominant standard when it comes to information platforms, but less structured approaches such as XML and JSON are emerging fast. Furthermore, innovations in the collaborative and mobile workforce are driving standards such as HTML5 and must be considered carefully when optimizing information. Platform considerations are also affected by the increasing use of analytic databases, in-memory approaches and Hadoop. Traditional approaches like an RDBMS on standard hardware and flat files are still the most common, but the most growth is with in-memory systems and Hadoop. This is interesting because these technologies allow for multiple new approaches to analysis such as visual discovery and machine learning on large data sets.  Adding to the impetus for change is that organizations using an RDBMS on standard hardware and flat files are less satisfied than those using the more innovative approaches to big data.

Information optimization also encounters challenges associated with data preparation and data presentation. In our research, 47 percent of organizations said that  they spend the largest portion of their time in data preparation, but less than half said they are satisfied with their process of creating information. Contributing to this dissatisfaction are lack of resources, lack of flexibility and speed of integration. Lack of resources and speed of integration tend to move together. That is, when more financial and human resources are dedicated to the integration efforts, satisfaction is higher. Adding more human and financial resources does not necessarily increase flexibility. That is a function of both tools and processes, and we see it as a result of divergent data preparation workflows occurring in organizations. One is a more structured approach that follows more traditional ETL paths that can lead to timely integration of data once everything is defined and the system is in place, but is less flexible. Another data preparation approach is to merge internal and external information on the fly in a sandbox environment or in response to sudden market challenges. These different information flows ultimately have to support specific forms of information presentation for users, whether that be the creation of an analytic data set for a complex statistical procedure by a data scientist within the organization or a single number with qualitative context for an executive on a mobile device.

Thus it is clear that information optimization is a critical focus for organizations; it’s also an important area of study for Ventana Research in 2014. Our latest benchmark research shows that the challenges are complex and involve the entire organization. As new technologies come to market and information processes must be aligned with the needs of the lines of business and the functional roles within organizations, companies that are able to simplify access to information and analytics through the information optimization approaches discussed above will provide an edge on competitors.

Regards,

Tony Cosentino

VP & Research Director

Paxata, a new data and analytics software provider says it wants to address one of the most pressing challenges facing today’s analyst performing analytics: simplifying data preparation. This trend toward simplification is well aligned with the market’s desire for improving usability, which our benchmark research into Next-Generation Business Intelligence shows is a primary buying consideration in two-thirds (64%) of companies. This trend is driving significant adoption of business-friendly-front-end visual and data discovery tools and is part of my research agenda for 2014.

On the back end, however, there is still considerable complexity. VR_Benchmark_Research_logoNon-traditional relational database systems such as Hadoop and big data appliances address the need to store and to some degree query massive amounts of structured and unstructured data. But the ability to efficiently and effectively blend these data sources and any third-party cloud-based data is still a challenge.

To address this challenge, the front end analytics tools that are being adopted by analysts and the multitude of back-end database systems must be integrated to deliver high quality analytic data sets. Today, this is no easy task. My latest benchmark research into Information Optimization recently released finds that when companies create and deploy information, the largest portions of time are spent on preparing data for analysis (49%) and reviewing data for quality and consistency issues (47%). In fact, our research shows that analysts consistently spend anywhere from 40 percent to 60 percent of their time in the data preparation phase that precedes actual analysis of the data.

Paxata and its Adaptive Data Preparation platform aims to solve the challenge of data preparation by improving the data vr_ss21_spreadsheets_arent_easily_replacedaggregation, enrichment, quality and governance processes. It does this using a spreadsheet paradigm, a choice of approach that should resonate well with business analysts; our research into spreadsheet use in today’s enterprises finds that the majority of them (56%) are resistant to a move away from spreadsheets.

In Paxata’s design, once the data is loaded the software displays the combined dataset in a spreadsheet format and the user then manipulates the rows and columns to accomplish the various data preparation tasks. For instance, to profile the data, the analyst can first use a search box and an autocomplete query to find the data of interest and then use color-coded cells and visualization techniques to highlight patterns in the data. For data that may include multiple duplicate records such as addresses, the company includes services that help to sort through these records and make suggestions on what records to combine. This last task may be of particular interest for marketers attempting to combine multiple third-party data sources that list several addresses and names for the same individual.

Another key aspect of Paxata’s software is a history function that allows users to return to any step in the data preparation process and make changes on the fly. This ability to explore the lineage of the data enables another interesting function: “Paxata Share.” This collaborative capability enables multiple users to collaboratively evaluate the differences between data sets by looking at different assumptions that went into the processing of the data. This function is particularly interesting as it has the potential to solve the challenge of “battling boardroom facts” – the situation in which people come to a meeting with different versions of the truth based on the same data sources but different data preparation assumptions.

Under the covers, Paxata’s offering boasts a cloud-based multi-tenant architecture hosted on Rackspace and leveraging the OpenStack platform. The company says its product can comfortably handle big data, processing millions of rows (or about a terabyte) of data in real time. If data sets are larger than this, a batch process can replace the real-time analysis.

In my view, the main value of Paxata’s technology lies in the data analyst time it potentially can save. Much of the functionality it offers involves data discovery driven by the kinds of machine learning algorithms that my colleague Mark Smith discussed Four types of Discovery Technology. For instance, the Paxata software will recommend data and metric definitions based on the business context in which the analyst is working – a customer versus a supply chain context, for example – and these recommendations will sharpen as more data runs through the system.

Paxata is off to a great start, though the data connectors its product offers currently are limited; this will improve as it builds out connectors for more data sources. The company will also need to sort through a very noisy marketplace of companies that provide similar services, on-premises or in the cloud, and that all are adapting their messages to address the data preparation challenge. On its website, Paxata lists Cloudera, Qlik Technologies and Tableau as technology partners. The company also lists dozens of information enrichment partners including government organizations and data companies such as Acxiom, DataSift, and Esri. The list of information partners is extensive, which reflects a thoughtful focus on the value of third-party data sources.

Utilizing efficient cloud computing technology, Paxata is able to come out of the gate with aggressive pricing listed on the company site that is about $300 per month which is pretty small amount for the time that is saved on daily, weekly and monthly basis. Such pricing should help adoption especially with business analysts that the company targets. Organizations that are struggling with the time they put into the data preparation phase of analytics and those that are looking to leverage outside data sources in new and innovative ways should look into Paxata.

Regards,

Tony Cosentino

VP and Research Director

Our benchmark research shows that analytics is the top businessvr_bti_br_technology_innovation_priorities technology innovation priority; 39% of organizations rank it first. This is no surprise as new information sources and new technologies in data processing, storage, networking, databases and analytic software are combining to offer capabilities for using information never before possible. For businesses, the analytic priority is heightened by intense competition on several fronts; they need to know as much as possible about pricing, strategies, customers and competitors. Within the organization, the IT department and the lines of business continue to debate issues around the analytic skills gap, information simplification, information governance and the rise of time-to-value metrics. Given this backdrop, I expect 2014 to be an exciting year for  studying analytic technologies and how they apply to business.

Three key focus areas comprise my 2014 analytics research agenda. The first includes a specific focus on business analytics and methods like discovery and exploratory. This area will be covered in depth in our new research on next-generation business analytics commencing in the first half of 2014. At Ventana Research, we break discovery analytics into visual discovery, data discovery, event discovery and information discovery. The definitions and uses of each type appear in Mark Smith’s analysis of the four discovery technologies. As part of this research, we will examine these exploratory tools and techniques in the context of the analytic skills gap and the new analytic process flows in organizations. The people and process aspects of the research will include how governance and controls are being implemented alongside these innovations. The exploratory analytics space includes business intelligence, which our research shows is still the primary method of deploying information and analytics in organizations. Two upcoming Value Indexes, Mobile Business Intelligence, due out in the first quarter, and Business Intelligence, starting in the second, will provide up-to-date and in-depth evaluations and ranking of vendors in these categories.

Ventana_Research_Value_Index_LogoMy second agenda area is big data and predictive analytics. The first research on this topic will be released in the first quarter of the year as benchmark research on big data analytics. This fresh and comprehensive research maps to my analysis of the four pillars of big data Analytics, a framework for thinking about big data and the associated analytic technologies. This research also has depth in the areas of predictive analytics and big data approaches in use today. In addition to that benchmark research, we will conduct a first of its kind, the Big Data Analytics Value Index, which will assess the major players applying analytics to big data. Real-time and right-time big data also is called operational intelligence, an area Ventana Research has pioneered over the years. Our Operational Intelligence Value Index, which will be released in the first quarter, evaluates vendors of software that helps companies do real-time analytics against large streams of data that builds on our benchmark research on the topic.

The third focus area is information simplification and cloud-based business analytics including business intelligence. In our benchmark research on information optimization, recently released, Ventana_Research_Benchmark_Research_Logonearly all (97%) organizations said it is important or very important to simplify informa­tion access for both their business and their customers. Paradoxically, at the same time the technology landscape is getting more fragmented and complex; in order to simplify, software design will need innovative uses of analytic technology to mask the underlying complexity through layers of abstraction. In particular, users need the areas of sourcing data and preparing data for analysis to be simplified and made more flexible so they can devote less time to these tasks and more the actual analysis. Part of the challenge in information optimization and integration is to analyze data that originates in the cloud or has been moved there. This issue has important implications for debates around information presentation, the semantic web, where analytics are executed, and whether business intelligence will move to the cloud in any more than a piecemeal fashion. We’ll explore these topics in benchmark research on business intelligence and analytics in the cloud, which is planned for the second half of 2014. We released in 2013 research on location analytics and the use of geography for presentation and processing of data which we refer to as location analytics.

Analytics as a business discipline is getting hotter as we move forward in the 21st century, and I am thrilled to be part of the analytics community. I welcome any feedback you have on my research agenda and look forward to continuing to providing research, collaborating and educating with you in 2014.

Regards,

Tony Cosentino

VP and Research Director

Like every large technology corporation today, IBM faces an innovator’s dilemma in at least some of its business. That phrase comes from Clayton Christensen’s seminal work, The Innovator’s Dilemma, originally published in 1997, which documents the dynamics of disruptive markets and their impacts on organizations. Christensen makes the key point that an innovative company can succeed or fail depending on what it does with the cash generated by continuing operations. In the case of IBM, it puts around US$6 billion a year into research and development; in recent years much of this investment has gone into research on big data and analytics, two of the hottest areas in 21st century business technology. At the company’s recent Information On Demand (IOD) conference in Las Vegas, presenters showed off much of this innovative portfolio.

At the top of the list is Project Neo, which will go into beta release early in 2014. Its purpose to fill the skills gap related to big data analytics, which our benchmark research into big data shows is held back most by lack of knowledgeable staff (79%) and lack of training (77%). The skills situation can be characterized as a three-legged stool of domain knowledge (that is, line-of-business knowledge), statistical knowledge and technological knowledge. With Project Neo, IBM aims to reduce the technological and statistical demands on the domain expert and empower that person to use big data analytics in service of a particular outcome, such as reducing customer churn or presenting the next best offer. In particular, Neo focuses on multiple areas of discovery, which my colleague Mark Smith outlined. Most of the industry discussion about simplifying analytics has revolved around visualization rather than data discovery, which applies analytics that go beyond visualization, or information discovery, which addresses how we find and access information in a highly distributed environment. These areas are the next logical steps after visualization for software vendors to address, and IBM takes them seriously with Neo.

At the heart of Neo are the same capabilities found in IBM’s SPSSUntitled 1 Analytic Catalyst, which won the 2013 Ventana Research Innovation Award for analytics and which I wrote about. It also includes IBM’s BLU acceleration against the DB2 database, an in-memory optimization technique, which I have discussed as well, that provides access to the analysis of large data sets. The company’s Vivisimo acquisition, which is now called InfoSphere Data Explorer, adds information discovery capabilities. Finally, the Rapid Adaptive Visualization Engine (RAVE), which is IBM’s visualization approach across its portfolio, is layered on top for fast, extensible visualizations. Neo itself is a work in progress currently offered only over the cloud and back-ended by the DB2 database. However, following the acquisition earlier this year of SoftLayer, which provides a cloud infrastructure platform. I would expect to also have IBM make Neo to allow it to access more sources than just loaded data into IBM DB2.

IBM also recently started shipping SPSS Modeler 16.0. IBM bought SPSS in 2009 and has invested in Modeler heavily. Modeler Untitled 2(formerly SPSS Clementine) is an analytic workflow tool akin to others in the market such as SAS Enterprise Miner, Alteryx and more recent entries such as SAP Lumira. SPSS Modeler enables analysts at multiple levels to interact on analytics and do both data exploration and predictive analytics. Analysts can move data from multiple sources and integrate it into one analytic workflow. These are critical capabilities as our predictive analytics benchmark research shows: The biggest challenges to predictive analytics are architectural integration (for 55% of organizations) and lack of access to necessary source data (35%).

IBM has made SPSS the centerpiece of its analytic portfolio and offers it at three levels, Professional, Premium and Gold. With the top-level Gold edition, Modeler 16.0 includes capabilities that are ahead of the market: run-time integration with InfoSphere Streams (IBM’s complex event processing product), IBM’s Analytics Decision Management (ADM) and the information optimization capabilities of G2, a skunks-works project by led by Jeff Jonas, chief scientist of IBM’s Entity Analytics Group.

Integration with InfoSphere Streams that won a Ventana Research Technology Innovation award in 2013 enables event processing to occur in an analytic workflow within Modeler. This is a particularly compelling capability as the so-called “Internet of things” begins to evolve and the ability to correlate multiple events in real time becomes crucial. In such real-time environments, often quantified in milliseconds, events cannot be pushed back into a database and wait to be analyzed.

Decision management is another part of SPSS Modeler. Once models are built, users need to deploy them, which often entails steps such as integrating with rules and optimizing parameters. In a next best offer situation in a retail banking environment, for instance, a potential customer may score highly on propensity want to take out a mortgage and buy a house, but other information shows that the person would not qualify for the loan. In this case, the model itself would suggest telling the customer about mortgage offers, but the rules engine would override it and find another offer to discuss. In addition, there are times when optimization exercises are needed such as Monte Carlo simulations to help to figure out parameters such as risk using “what-if” modelling. In many situations, to gain competitive advantage, all of these capabilities must be rolled into a production environment where individual records are scored in real time against the organization’s database and integrated with the front-end system such as a call center application. The net capability that IBM’s ADM  brings is the ability to deploy analytical models into the business without consuming significant resources.

G2 is a part of Modeler and developed in IBM’s Entity Analytics Group. The group is garnering a lot of attention both internally and externally for its work around “entity analytics” – the idea that each information entity has characteristics that are revealed only in contextual information – charting innovative methods in the areas of data integration and privacy. In the context of Modeler this has important implications for bringing together disparate data sources that naturally link together but otherwise would be treated separately. A core example is that an individual may have multiple email addresses in different databases, has changed addresses or changed names perhaps due to a new marital status. Through machine-learning processes and analysis of the surrounding data, G2 can match records and attach them with some certainty to one individual. The system also strips out personally identifiable information (PII) to meet privacy and compliance standards. Such capabilities are critical for business as our latest benchmark research on information optimization shows that two in five organizations have more than 10 different data sources that they need to integrate and that the ability to simplify access to these systems is important to virtually all organizations (97%).

With the above capabilities, SPSS Modeler Gold edition achieves  market differentiation, but IBM still needs to show the advantage of base editions such as Modeler Professional. The marketing issue for SPSS Modeler is that it is considered a luxury car in a market being infiltrated by compacts and kit cars. In the latter case there is the R programming language, which is open-source and ostensibly free, but the challenge here is that companies need R programmers to run everything. SPSS Modeler and other such visually oriented tools (many of which integrate with open source R) allow easier collaboration on analytics, and ultimately the path to value is shorter. Even at its base level Modeler is an easy-to-use and capable statistical analysis tool that allows for collaborative workgroups and is more mature than many others in the market.

Companies must consider predictive analytics capabilities or Untitledrisk being left behind. Our research into predictive analytics shows that two-thirds of companies see predictive analytics as providing competitive advantage (68%) and particularly important in revenue-generating functions such as marketing (for 70%) and forecasting (72%). Companies currently looking into discovery analytics may want to try Neo, which will be available in beta in early 2014. Those interested in predictive analytics should consider the different levels of SPSS 16.0 as well as IBM’s flagship Signature Solutions, which I have covered. IBM has documented use cases that can give users guidance in terms of leading-edge deployment patterns and leveraging analytics for competitive advantage. If you have not taken a look at the depth of the analytic technology portfolio at IBM, I would make sure to do so, as you might miss some fundamental advancements to the processing of data and analytics to provide the valuable insights required to operate effectively in the global marketplace.

Regards,

Tony Cosentino

VP and Research Director

At its Teradata Partners conference in Dallas, a broader vision for big data and analytics was articulated clearly. Their pitch centered on three areas – data warehousing, big data analytics and integrated marketing – that to some degree reflect Teradata’s core market and acquisitions in the last few years of companies like Aprimo who provides integrated marketing technology and Aster in big data analytics. The keynote showcased the company’s leadership position in the increasingly complex world of open source database software, cloud computing and business analytics.

As I discussed in writing about the 2013 Hadoop Summit, Teradata has embraced technologies such as Hadoop that can be seen as vr_bigdata_big_data_technologies_plannedboth a threat and an opportunity to its status as a dominant database provider over the past 20 years. Its holistic architectural approach appropriately named Unified Data Architecture (UDA) reflects an enlightened vision, but relies on the ideas that separate database workloads will drive a unified logical architecture and that companies will continue to rely on today’s major database vendors to provide leadership for the new integrated approach. Our big data benchmark research finds support for this overall position since most big data strategies still rely on a blend of approaches including data warehouse appliances (35%), in-memory databases (34%), specialized databases (33%) and Hadoop (32%).

Teradata is one of the few companies that has the capability to produce a truly integrated platform, and we see evidence of this by its advances in UDA, the Seamless Network Analytics Processing (SNAP) Framework and the Teradata Aster 6 Discovery platform. I want to note that the premise behind UDA is that the complexities of the different big data approaches is abstracted from the user which may access the data through tools such as Aster or other BI or visualization tool. This is important because it means that organizations and their users do not need to understand the complexities of the various types of emerging database approaches prior using them for competitive advantage.

The developments in Aster 6 show the power of the platform to access new and different workloads for new analytic solutions. Teradata announced three key developments about the Aster platform just before the Partners conference. A graph engine is added to complement the existing SQL and MapReduce engines. Graph analytics has not had as much exposure as other NoSQL technologies such as Hadoop or document databases, but it is beginning to gain traction for specific use cases where relationships are difficult to analyze with traditional analytics. For instance, any relationship network, including those in social media, telecommunications or healthcare, can use graph engines, but they also are being applied for basket analysis in retail or behind the scenes in areas such as master data management. The reason that the graph approach can be considered better in these situations is that it is more efficient. For example, it is easier to look at a graph of a social network and understand existing relationships and what is occurring, than trying to understand this same type of data looking at rows and columns. Similarly, using the ideas of nodes and edges, a graph database helps discover very complex patterns in the data that may not be obvious otherwise.

An integrated storage architecture compatible with Apache Hadoop HDFS, its file system, is another important development in Aster 6. It accommodates fast ingestion and preprocessing of multi-structured data. Perhaps the most important development for Aster 6 is the SNAP Framework, which integrates and optimizes execution of SQL queries across the different analytic engines. That is, Teradata has provided a layer of abstraction that removes the need for expertise in different flavors of NoSQL and puts it into SQL, a language that many data-oriented professionals understand.

vr_bigdata_obstacles_to_big_data_analytics %282%29Our big data benchmark research shows that staffing and training are major challenges to big data analytics for three-fourths of organizations today and advances in Aster 6 address multiple analytical access points needed in today’s big data environment. These three analytic access points which are the focus for Teradata are the data scientist, the analyst and the knowledge worker, as described in my recent post on the analytic personas that matter. For the first group of data scientists, there are open APIs and an integrated development environment (IDE) in which they can develop services directly against the unified data architecture. Analysts, who typically are less familiar with procedural programming approaches, can use the declarative paradigm of SQL to access data and call up functions within the unified data architecture. Some advanced algorithms are included now within Aster, as are a few big data visualizations such as sankey; on that topic, I think the best interactive sankey visualization for Aster is from Qlik Technologies, and was showcased at the company’s booth at the Teradata conference. The third persona and access point is the role of the knowledge worker, who accesses big data through BI and visualization tools. Ultimately, the Aster 6 platform brings an impressively integrated access approach to big data analytics; we have not yet seen its equal elsewhere in the market.

A key challenge that Teradata faces as it repositions itself from a best-in-class database provider for data warehousing to a big data and big data analytics provider is to articulate clearly how everything fits together to serve the business analyst. For instance, Teradata relies on its partner’s tools like visual discovery tools and analytical workflow tools such as Alteryx to tap into the power of its database, but it is hard to see how all of these tools use Aster 6. We saw the Aster 6 n-path analysis nicely displayed in an interactive sankey by QlikTech who I recently assessed, and an n-path node within the context of the Alteryx who I also analyzed advanced analytics workflow, but it is unclear how an analyst without specific SQL skills can do more than that. Furthermore, Teradata announced that its database integrates with the full library of advanced analytics algorithms through Fuzzy Logix, and through partnership with Revolution Analytics, R algorithms can run directly in the parallelized environment of Teradata, but again it is unclear how this plays with the Aster 6 approach. This is not to downplay the integration with Fuzzy Logix and Revolution Analytics because these are major announcements and they should not be underestimated especially for big data analytics. However, how these advancements align with the Aster approach and the usability of advanced analytics is still unclear. Our research shows that usability is becoming the most important buying criterion across categories of software and types of purchasers. In the case of next-generation business intelligence, usability is the number-one buying criterion for nearly two out of three (64%) organizations. Nevertheless, Aster 6 provides an agile, powerful and multifaceted data discovery platform that addresses the skills gap especially at the upper end of the analyst skills curve.

In an extension of this exploratory analytics position, Teradata also introduced cloud services. While we have seen vendors of BI and analytics as laggards in the cloud, it is increasingly difficult for them to ignore. Particular use cases are analytic sandboxes and exploratory analytics; in the cloud users can add or reduce resources as needed to address the analytic needs of the organization. Teradata introduced its cloud approach as TCO neutral which means that once you include all of the associated expense of running the service, it will be no more or less expensive than if it was to be run on premise. This runs counter to a lot of industry talk about the inexpensive nature of Amazon’s Redshift platform (based on the Paraccel MPP database that I wrote about). However, IT professionals who actually run databases are sophisticated enough to understand the cost drivers and know that a purely cost-based argument is a red herring. Network infrastructure costs, data governance, security and compliance all come into play since these issues are similar in the cloud as they are on-premises. TCO neutral is a reasonable position for Teradata since it shows that the company knows what it takes to deploy and run big data and analytics in the cloud. Although cloud players market themselves as less expensive, there still are plenty of expenses associated with it. The big differences are in the elasticity of the resources as well as the way the cost is distributed in the form of operational expenditures rather than capital expenditures. Buyers should consider all factors before making the datawarehouse cloud decision, but overall cloud strategy and use case are two critical criterions.

Its cloud computing direction is emblematic of the analytics market position that Teradata is aspiring to occupy. For years it has under-promised and over-delivered. This company doesn’t introduce products with a lot of hype and bugs and then ask the market to help fix them. Its reputation has earned it some of the biggest clients in the world and has built a high level of trust, especially within IT departments. As companies become frustrated with a lack of governance and security and a proliferation of data silos that today’s business-driven use of analytics spawns, I expect that the pendulum of power will swing back toward IT. It’s hard to predict when this may happen, but Teradata will be particularly well positioned when it does. Until then, on the business side it will continue to compete with systems integration consulting firms and other giants vying for the high level trusted advisor position in today’s enterprise. In this effort, Teradata has both industry and technical expertise and has established a center of excellence populated by some of the smartest minds in the big data world including Scott Nau, Tasso Argyros and Bill Franks. I recommend Bill Franks’ Taming the Big Data Tidal Wave as one of the most comprehensive and readable books on big data and analytics.

For large and midsize companies that are already Teradata customers, midsize companies with a cloud-first charter and any established organization rethinking its big data architecture, Teradata should be on the list of vendors to consider.

Regards,

Tony Cosentino

VP and Research Director

While covering providers of business analytics software, it is also interesting for me to look at some that focus on the people, process and implementation aspects in big data and analytics. One such company is Nuevora, which uses a flexible platform to provide customized analytic solutions. I recently met the company’s founder, Phani Nagarjuna, when we appeared on a panel at the Predictive Analytics World conference in San Diego.

Nuevora focuses on big data and analytics from the perspective of the analytic life cycle; that is, it helps companies bring together data and process, visualize and model the data to reach specific business outcomes. Nuevora aims to package implementations of analytics for vertical industries by putting together data sources and analytical techniques, and designing the package to be consumed by a target user group. While the core of the analytic service may be the same within an industry category, each solution is customized to the particulars of the client and its view of the market. Using particular information sources and models depending on their industry, customers can take advantage of advances in big data and analytics including new data sources and technologies. For its part Nuevora does not have to reinvent the wheel for each engagement. It has established patterns of data processing and prebuilt predictive analytics apps that are based on best practices and designed to solve specific problems within industry segments.

The service is currently delivered via a managed service on Nuevora servers called the Big Data Analytics & Apps Platform (nBAAP), but the company’s roadmap calls for more of a software as a service (SaaS) delivery model. Currently nBAAP uses Hadoop for data processing, R for predictive analytics and Tableau for visualizations. This approach brings together best-of-breed point solutions to address specific business issues. As a managed service, it has flexibility in design, and the company can reuse existing SAS and SPSS code for predictive models and can integrate with different BI tools depending on the customer’s environment.

Complementing the nBAAP approach is the Big Data & Analytics Maturity (nBAM) Assessment Framework. This is an industry-based consulting framework that guides companies through their analytic planning process by looking at organizational goals and objectives, establishing a baseline of the current environment, and putting forward a plan that aligns with the analytical frameworks and industry-centric approaches in nBAAP.

From an operating perspective, Nagarjuna, a native of India, taps analytics talent from universities there and places strategic solution vr_predanalytics_usage_of_predictive_analyticsconsultants in client-facing roles in the United States. The company focuses primarily on big data analytics in marketing, which makes sense since, according to our benchmark research on predictive analytics, revenue-generating functions such as forecasting (cited by 72% of organizations) and marketing (67%) are the two primary use cases for predictive analytics. Nuevora has mapped multiple business processes related to processes such as gaining a 360-degree view of the customer. For example, at a high-level, it divides marketing into areas such as retention, cross-sell and up-sell, profitability and customer lifetime value. These provide building blocks for the overall strategy of the organization, and each can be broken down into finer divisions, linkages and algorithms based on the industry. These building blocks also serve as the foundation for the deployment patterns of raw data and preselected data variables, metrics, models, visuals, model update guidelines and expected outcomes.

By providing preprocessing capabilities that automatically produce the analytic data set, then providing updated and optimized models, and finally enabling consumption of these models through the relevant user paradigm, Nuevora addresses some of the key challenges in analytics today. The first is data preparation, which our research shows takes from 40 to 60 percent of analysts’ time. The second is addressing outdated models. Our research on predictive analytics shows that companies that update their models often are much more satisfied with them than are those that do not. While the appropriate timing of model updates is relative to the business context and market changes, our research shows that about one month is optimal.

Midsize or larger companies looking to take advantage of big data and analytics matched with specific business outcomes, without having to hire data scientists and build a full solution internally, should consider Nuevora.

Regards,

Tony Cosentino

VP and Research Director

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