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Our benchmark research into predictive analytics shows that lack of resources, including budget and skills, is the number-one business barrier to the effective deployment and use of predictive analytics; awareness – that is, an understanding of how to apply predictive analytics to business problems – is second. In order to secure resources and address awareness problems a business case needs to be created and communicated clearly wherever appropriate across the organization. A business case presents the reasoning for initiating a project or task. A compelling business case communicates the nature of the proposed project and the arguments, both quantified and unquantifiable, for its deployment.

The first steps in creating a business case for predictive analytics are to understand the audience and to communicate with the experts who will be involved in leading the project. Predictive analytics can be transformational in nature and therefore the audience potentially is broad, including many disciplines within the organization. Understand who should be involved in business case creation a list that may include business users, analytics users and IT. Those most often primarily responsible for designing and deploying predictive analytics are data scientists (in 31% of organizations), the business intelligence and data warehouse team (27%), those working in general IT (16%) and line of business analysts (13%), so be sure to involve these groups. Understand the specific value and challenges for each of the constituencies so the business case can represent the interests of these key stakeholders. I discuss the aspects of the business where these groups will see predictive analytics most adding value here and here.

For the business case for a predictive analytics deployment to be persuasive, executives also must understand how specifically the deployment will impact their areas of responsibilityvr_NG_Predictive_Analytics_01_front_office_functions_use_predictive_anal.._ and what the return on investment will be. For these stakeholders, the argument should be multifaceted. At a high level, the business case should explain why predictive analytics is important and how it fits with and enhances the organization’s overall business plan. Industry benchmark research and relevant case studies can be used to paint a picture of what predictive analytics can do for marketing (48%), operations (44%) and IT (40%), the functions where predictive analytics is used most.

A business case should show how predictive analytics relates to other relevant innovation and analytic initiatives in the company. For instance, companies have been spending money on big data, cloud and visualization initiatives where software returns can be more difficult to quantify. Our research into big data analytics and data and analytics in the cloud show that the top benefit for these initiatives are communication and knowledge sharing. Fortunately, the business case for predictive analytics can cite the tangible business benefits our research identified, the most often identified of which are achieving competitive advantage (57%), creating new revenue opportunities (50%), and increasing profitability vr_NG_Predictive_Analytics_03_benefits_of_predictive_analytics(46%). But the business case can be made even stronger by noting that predictive analytics can have added value when it is used to leverage other current technology investments. For instance, our big data analytics research shows that the most valuable type of analytics to be applied to big data is predictive analytics.

To craft the specifics of the business case, concisely define the business issue that will be addressed. Assess the current environment and offer a gap analysis to show the difference between the current environment and the future environment). Offer a recommended solution, but also offer alternatives. Detail the specific value propositions associated with the change. Create a financial analysis summarizing costs and benefits. Support the analysis with a timeline including roles and responsibilities. Finally, detail the major risk factors and opportunity costs associated with the project.

For complex initiatives, break the overall project into a series of shorter projects. If the business case is for a project that will involve substantial work, consider providing separate timelines and deliverables for each phase. Doing so will keep stakeholders both informed and engaged during the time it takes to complete the full project. For large predictive analytics projects, it is important to break out the due-diligence phase and try not to make any hard commitments until that phase is completed. After all, it is difficult to establish defensible budgets and timelines until one knows the complete scope of the project.

Ensure that the project time line is realistic and addresses all the key components needed for a successful deployment.  In particular with predictive analytics projects, make certain that it reflects a thoughtful approach to data access, data quality and data preparation. We note that four in 10 organizations say vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_processthat the most time spent in the predictive analytics process is in data preparation and another 22 percent say that they spend the most time accessing data sources. If data issues have not been well thought through, it is next to impossible for the predictive analytics initiative to be successful. Read my recent piece on operationalizing predictive analytics to show how predictive analytics will align with specific business processes.

If you are proposing the implementation of new predictive analytics software, highlight the multiple areas of return beyond competitive advantage and revenue benefits. Specifically, new software can have a total lower cost of ownership and generate direct cost savings from improved operating efficiencies. A software deployment also can yield benefits related to people (productivity, insight, fewer errors), management (creativity, speed of response), process (shorter time on task or time to complete) and information (easier access, more timely, accurate and consistent). Create a comprehensive list of the major benefits the software will provide compared to the existing approach, quantifying the impact wherever possible. Detail all major costs of ownership whether the implementation is on-premises or cloud-based: these will include licensing, maintenance, implementation consulting, internal deployment resources, training, hardware and other infrastructure costs. In other words, think broadly about both the costs and the sources of return in building the case for new technology. Also, read my recent piece on procuring predictive analytics software.

Understanding the audience, painting the vision, crafting the specific case, outlining areas of return, specifying software, noting risk factors, and being as comprehensive as possible are all part of a successful business plan process. Sometimes, the initial phase is really just a pitch for project funding and there won’t be any dollar allocation until people are convinced that the program will get them what they need.  In such situations multiple documents may be required, including a short one- to two-page document that outlines vision and makes a high-level argument for action from the organizational stakeholders. Once a cross functional team and executive support is in place, a more formal assessment and design plan following the principles above will have to be built.

Predictive analytics offers significant returns for organizations willing pursue it, but establishing a solid business case is the first step for any organization.

Regards,

Ventana Research

Our research into next-generation predictive analytics shows that along with not having enough skilled resources, which I discussed in my previous analysisNGPA AP #4 image 1the inability to readily access and integrate data is a primary reason for dissatisfaction with predictive analytics (in 62% of participating organizations). Furthermore, this area consumes the most time in the predictive analytics process: The research finds that preparing data for analysis (40%) and accessing data (22%) are the parts of the predictive analysis process that create the most challenges for organizations. To allow more time for actual analysis, organizations must work to improve their data-related processes.

Organizations apply predictive analytics to many categories of information. Our research shows that the most common categories are customer (used by 50%), marketing (44%), product (43%), financial (40%) and sales (38%). Such information often has to be combined from various systems and enriched with information from new sources. Before users can apply predictive analytics to these blended data sets, the information must be put into a common form and represented as a normalized analytic data set. Unlike in data warehouse systems, which provide a single data source with a common format, today data is often located in a variety of systems that have different formats and data models. Much of the current challenge in accessing and integrating data comes from the need to include not only a variety of relational data sources but also less structured forms of data. Data that varies in both structures and sizes is commonly called big data.

To deal with the challenge of storing and computing big data, organizations planning to use predictive analytics increasingly turn to big data technology. While flat files and relational databases on standard hardware, each cited by almost two-thirds (63%) of participants, are still the most commonly used tools for predictive analytics, more than half (52%) of organizations now use data warehouse appliances for Using Big Data with Predictive Analytics predictive analytics, and 31 percent use in-memory databases, which the second-highest percentage (24%) plan to adopt in the next 12 to 24 months. Hadoop and NoSQL technologies lag in adoption, currently used by one in four organizations, but in the next 12 to 24 months an additional 29 percent intend to use Hadoop and 20 percent more will use other NoSQL approaches. Furthermore, more than one-quarter (26%) of organizations are evaluating Hadoop for use in predictive analytics, which is the most of any technology.

 Some organizations are considering moving from on-premises to cloud-based storage of data for predictive analytics; the most common reasons for doing so are to improve accessing data (for 49%) and preparing data for analysis (43%). This trend speaks to the increasing importance of cloud-based data sources as well as cloud-based tools that provide access to many information sources and provide predictive analytics. As organizations accumulate more data and need to apply predictive analytics in a scalable manner, we expect the need to access and use big data and cloud-based systems to increase.

While big data systems can help handle the size and variety of data, they do not of themselves solve the challenges of data access and normalization. This is especially true for organizations that need to blend new data that resides in isolated systems. How to do this is critical for organizations to consider, especially in light of the people using predictive analytic system and their skills. There are three key considerations here. One is the user interface, the most common of which are spreadsheets (used by 48%), graphical workflow modeling tools (44%), integrated development environments (37%) and menu-driven modeling tools (35%). Second is the number of data sources to deal with and which are supported by the system; our research shows that four out of five of organizations need to access and integrate five or more data sources. The third consideration is which analytic languages and libraries to use and which are supported by the system; the research finds that Microsoft Excel, SQL, R, Java and Python are the most widely used for predictive analytics. Considering these three priorities both in terms of the resident skills, processes, current technology, and information sources that need to be accessed are crucial for delivering value to the organization with predictive analytics.

While there has been an exponential increase in data available to use in predictive analytics as well as advances in integration technology, our research shows that data access and preparation are still the most challenging and time-consuming tasks in the predictive analytics process. Although technology for these tasks has improved, complexity of the data has increased through the emergence of different data types, large-scale data and cloud-based data sources. Organizations must pay special attention to how they choose predictive analytics tools that can give easy access to multiple diverse data sources including big data stores and provide capabilities for data blending and provisioning of analytic data sets. Without these capabilities, predictive analytics tools will fall short of expectations.

Regards,

Ventana Research

The Performance Index analysis we performed as part of our next-generation predictive analytics benchmark research shows that only one in four organizations, those functioning at the highest Innovative level of performance, can use predictive analytics to compete effectively against others that use this technology less well. We analyze performance in detail in four dimensions (People, Process, Information and Technology), and for predictive analytics we find that organizations perform best in the Technology dimension, with 38 percent reaching the top Innovative level. This is often the case in our analyses, as organizations initially perform better in the details of selectingvr_NG_Predictive_Analytics_performance_06_dimensions and managing new tools than in the other dimensions. Predictive analytics is not a new technology per se, but the difference is that it is becoming more common in business units, as I have written.

In contrast to organizations’ performance in the Technology dimension, only 10 percent reach the Innovative level in People and only 11 percent in Process. This disparity uncovered by the research analysis suggests there is value in focusing on the skills that are used to design and deploy predictive analytics. In particular, we found that one of the two most-often cited reasons why participants are not fully satisfied with the organization’s use of predictive analytics is that there are not enough skilled resources (cited by 62%). In addition, 29 percent said that the need for too much training or customized skills is a barrier to changing their predictive analytics.

The challenge for many organizations is to find the combination of domain knowledge, statistical and mathematical knowledge, and technical knowledge that it needs to be able to integrate predictive analytics into other technology systems and into operations in the lines of business, which I also have discussed. The need for technical knowledge is evident in the research findings on the jobs held by individual participants: Three out of four require technical sophistication. More than one-third (35%) are data scientists who have a deep understanding of predictive analytics and its use as well as of data-related technology; one-fourth are data analysts who understand the organization’s data and systems but have limited knowledge of predictive analytics; and 16 percent described themselves as predictive analytics experts who have a deep understanding of this topic but not of technology in general. The research also finds that those most often primarily responsible for designing and deploying predictive analytics are data scientists (in 31% of organizations) or members of the business intelligence and data warehouse team (27%). This focus on business intelligence and data warehousing vr_NG_Predictive_Analytics_16_why_users_dont_produce_predictive_analysesrepresents a shift toward integrating predictive analytics with other technologies and indicates a need to scale predictive analytics across the organization.

In only about half (52%) of organizations are the people who design and deploy predictive analytics the same people who utilize the output of these processes. The most common reasons cited by research participants that users of predictive analytics don’t produce their own analyses are that they don’t have enough skills training (79%) and don’t understand the mathematics involved (66%). The research also finds evidence that skills training pays off: Fully half of those who said they received adequate training in applying predictive analytics to business problems also said they are very satisfied with their predictive analytics; percentages dropped precipitously for those who said the training was somewhat adequate (8%) and inadequate (6%). It is clear that professionals trained in both business and technology are necessary for an organization to successfully understand, deploy and use predictive analytics.

To determine the technical skills and training necessary for predictive analytics, it is important to understand which languages and libraries are used. The research shows that the most common are SQL (used by 67% of organizations) and Microsoft Excel (64%), with which many people are familiar and which are relatively easy to use. The three next-most commonly used are much more sophisticated: the open source language R (by 58%), Java (42%) and Python (36%). Overall, many languages are in use: Three out of five organizations use four or more of them. This array reflects the diversity of approaches to predictive analytics. Organizations must assess what languages make sense for their uses, and vendors must support many languages for predictive analytics to meet the demands of all customers.

The research thus makes clear that organizations must pay attention to a variety of skills and how to combine them with technology to ensure success in using predictive analytics. Not all the skills necessary in an analytics-driven organization can be combined in one person, as I discussed in my analysis of analytic personas. We recommend that as organizations focus on the skills discussed above, they consider creating cross-functional teams from both business and technology groups.

Regards,

Ventana Research

To impact business success, Ventana Research recommends viewing predictive analytics as a business investment rather than an IT investment.  Our recent benchmark research into next-generation predictive analytics  reveals that since our previous research on the topic in 2012, funding has shifted from general business budgets (previously 44%) to line of business IT budgets (previously 19%). Now more than vr_NG_Predictive_Analytics_15_preferences_in_purchasing_predictive_analy.._  half of organizations fund such projects from business budgets: 29 percent from general business budgets and 27 percent from a line of business IT budget. This shift in buying reflects the mainstreaming of predictive analytics in organizations,  which I recently wrote about .

This shift in funding of initiatives coincides with a change in the preferred format for predictive analytics. The research reveals that 15 percent fewer organizations prefer to purchase predictive analytics as stand-alone technology today than did in the previous research (29% now vs. 44% then). Instead we find growing demand for predictive analytics tools that can be integrated with operational environments such as business intelligence or transaction applications. More than two in five (43%) organizations now prefer predictive analytics embedded in other technologies. This integration can help businesses respond faster to market opportunities and competitive threats without having to switch applications.

  vr_NG_Predictive_Analytics_14_considerations_in_evaluating_predictive_an.._ The features most often sought in predictive analytics products further confirm business interest. Usability (very important to 67%) and capability (59%) are the top buying criteria, followed by reliability (52%) and manageability (49%). This is consistent with the priorities of organizations three years ago with one important exception: Manageability was one of the two least important criteria then (33%) but today is nearly tied with reliability for third place. This change makes sense in light of a broader use of predictive analytics and the need to manage an increasing variety of models and input variables.

Further, as a business investment predictive analytics is most often used in front-office functions, but the research shows that IT and operations are closely associated with these functions. The top four areas of predictive analytics use are marketing (48%), operations (44%), IT (40%) and sales (38%). In the previous research operations ranked much lower on the list.

To select the most useful product, organizations must understand where IT and business buyers agree and disagree on what matters. The research shows that they agree closely on how to deploy the tools: Both expressed a greater preference to deploy on-premises (business 53%, IT 55%) but also agree in the number of those who prefer it on demand through cloud computing (business 22%, IT 23%). More than 90 percent on both sides said the organization plans to deploy more predictive analytics, and they also were in close agreement (business 32%, IT 33%) that doing so would have a transformational impact, enabling the organization to do things it couldn’t do before.

However, some distinctions are important to consider, especially when looking at the business case for predictive analytics. Business users more often focus on the benefit of achieving competitive advantage (60% vs. 50% of IT) and creating new revenue opportunities (55% vs. 41%), which are the two benefits most often cited overall. On the other hand, IT professionals more often focus on the benefits of in­creased upselling and cross-selling (53% vs. 32%), reduced risk (26% vs. 21%) and better compliance (26% vs. 19%); the last two reflect key responsibilities of the IT group.

Despite strong business involvement, when it comes to products, IT, technical and data experts are indispensable for the evaluation and use of predictive analytics. Data scientists or the head of data management are most often involved in recommending (52%) and evaluating (56%) predictive analytics technologies. Reflecting the need to deploy predictive analytics to business units, analysts and IT staff are the next-most influential roles for evaluating and recommending. This involvement of technically sophisticated individuals combined with the movement away from organizations buying stand-alone tools indicates an increasingly team-oriented approach.

Purchase of predictive analytics often requires approval from high up in the organization, which underscores the degree of enterprise-wide interest in this technology. The CEO or president is most likely to be involved in the final decision in small (87%) and midsize (76%) companies. In contrast, large companies rely most on IT management (40%), and very large companies rely most on the CIO or head of IT (60%). We again note the importance of IT in the predictive analytics decision-making process in larger organizations. In the previous research, in large companies IT management was involved in approval in 9 percent of them and the CIO was involved in only 40 percent.

As predictive analytics becomes more widely used, buyers should take a broad view of the design and deployment requirements of the organization and specific lines of business. They should consider which functional areas will use the tools and consider issues involving people, processes and information as well as technology when evaluating such systems. We urge business and IT buyers to work together during the buying process with the common goal of using predictive analytics to deliver value to the enterprise.

Regards,

Ventana Research

Our recently released benchmark research into next-generation predictive analytics  shows that in this increasingly important area many organizations are moving forward in the dimensions of information and technology, but most are challenged to find people with the right skills and to align organizationalVentanaResearch_NextGenPredictiveAnalytics_BenchmarkResearch processes to derive business value from predictive analytics.

For those that have done so, the rewards can be significant. One-third of organizations participating in the research said that using predictive  analytics leads to transformational change – that is, it enables them to do things they couldn’t do before – and at least half said that it provides competitive advantage or creates new revenue opportunities. Reflecting the  vr_NG_Predictive_Analytics_03_benefits_of_predictive_analytics momentum behind predic­tive analytics today, virtually all participants (98%) that have engaged in predictive analytics said that they will be rolling out more of it.

Our research shows that predictive analytics is being used most often in the front offices of organizations, specifically in marketing (48%), operations (44%) and IT (40%). While operations and IT are not often considered front-office functions, we find that they are using predictive analytics in service to customers. For instance, the ability to manage and impact the customer experience by applying analytics to big data is an increasingly important approach that  I recently wrote about . As conventional channels of communication give way to digital channels, the use of predictive analytics in operations and IT becomes more valuable for marketing and customer service.

However, the most widespread barrier to making changes in predictive analytics is lack of resources (cited by 52% of organizations), which includes finding the necessary skills to design and deploy programs. The research shows that currently consultants and data scientists are those most often needed. Half the time those designing the system are also the end users of it, which indicates that using predictive analytics still requires advanced skills. Lack of awareness (cited by 48%) is the second-most common barrier; many organizations fail to understand the vr_NG_Predictive_Analytics_06_technical_challenges_to_predictive_analyti.._  value of predictive analytics in their business. Some of the reluctance to implement predictive analytics may be because doing so can require significant change. Predictive analytics often represents a new way of thinking and can necessitate revamping of key organizational processes.

From a technical perspective, the most common deployment challenge is difficulty in integrating predictive analytics into the information architecture, an issue cited by half of participants. This is not surprising given the diversity of tools and databases involved in big data. Problems with accessing source data (30%), inappropriate algorithms (26%) and inaccurate results (21%) also impede use. Accessing and normalizing data sources is a significant issue as many different types of data must be incorporated to use predictive analytics optimally. Blending this data and turning it into a clean analytic data set often takes significant effort. Confirming this is the finding that data preparation is the most challenging part of the analytic process for half of the organizations in the research.

Regarding interaction with other established systems, business intelligence is most often the integration point (for 56% of companies). However, it also is increasingly embedded in databases and middleware. The ability to perform modeling in databases is important since it enables analysts to work with large data sets and do more timely model updates and scoring. Embedding into middleware has grown fourfold since our previous research on predictive analytics in 2012; this has implications for the emerging Internet of Things (IoT), through which people will interact with an increasing array of devices.

Another sign of the broader adoption of predictive analytics is how and where buying decisions are made. Budgets for  vr_NG_Predictive_Analytics_07_funding_improvement_in_predictive_analytic.._ predictive analytics are shifting. Since the previous research, funding sourced from general business budgets has declined 9 percent and increased 8 percent in line-of-business IT budgets. This comports with a shift in the form in which organizations prefer to buy predictive analytics, which now is less as a stand-alone product and more embedded in other systems. Usability and functionality are still the top buying criteria, reflecting needs to simplify predictive analytics tools and address the skills gap while still being able to access a range of capabilities.

Overall the research shows that the application of predictive analytics to business processes sets high-performing organizations apart from others. Companies more often achieve competitive advantage with predictive analytics when they support the deployment of predictive analytics in business processes (66% vs. 57% overall), use business intelligence and data warehouse teams to design and deploy predictive analytics (71% vs. 58%) and fund predictive analytics as a shared service (73% vs. 58%). Similarly, those that train employees in the application of predictive analytics to business problems achieve more satisfaction and better outcomes.

Organizations looking to improve their business through predictive analytics should examine what others are doing. Since the time of our previous research, innovation has expanded and there are more peer organizations across industries and business functions that can be emulated. And the search for such innovation need not be limited to within one’s industry; cross-industry examples also can be enlightening. More concretely, the research finds that people and processes are where organizations can improve most in predictive analytics. We advise them to concentrate on streamlining processes, acquiring necessary skills and supporting both with technology available in the market. To begin, develop a practical predictive analytics strategy and enlist all stakeholders in the organization to support initiatives.

Regards,

Ventana Research

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

Users of big data analytics are finally going public. At the Hadoop Summit last June, many vendors were still speaking of a large retailer or a big bank as users but could not publically disclose their partnerships. Companies experimenting with big data analytics felt that their proof of concept was so innovative that once it moved into production, it would yield a competitive advantage to the early mover. Now many companies are speaking openly about what they have been up to in their business laboratories. I look forward to attending the 2013 Hadoop Summit in San Jose to see how much things have changed in just a single year for Hadoop centered big data analytics.

Our benchmark research into operational intelligence, which I argue is another name for real-time big data analytics, shows diversity in big data analytics use cases by industry. The goals of operational intelligence are an interesting mix as the research shows relative parity among managing performance (59%), detecting fraud and security (59%), complying with regulations (58%) and managing risk (58%), but when we drill down into different industries there are some interesting nuances. For instance, healthcare and banking are driven much more by risk and regulatory compliance, services such as retail are driven more by performance, and manufacturing is driven more by cost reduction. All of these make sense given the nature of the businesses. Let’s look at them in more detail.

vr_oi_goals_of_using_operational_intelligenceThe retail industry, driven by market forces and facing discontinuous change, is adopting big data analytics out of competitive necessity. The discontinuity comes in the form of online shopping and the need for traditional retailers to supplement their brick-and-mortar locations. JCPenney and Macy’s provide a sharp contrast in how two retailers approached this challenge. A few years ago, the two companies eyed a similar competitive space, but since that time, Macy’s has implemented systems based on big data analytics and is now sourcing locally for online transactions and can optimize pricing of its more than 70 million SKUs in just one hour using SAS High Performance Analytics. The Macy’s approach has, in Sun-Tzu like fashion, made the “showroom floor” disadvantage into a customer experience advantage. JCPenney, on the other hand, used gut-feel management decisions based on classic brand merchandising strategies and ended up alienating its customers and generating law suits and a well-publicized apology to its customers. Other companies including Sears are doing similarly innovative work with suppliers such as Teradata and innovative startups like Datameer in data hub architectures build around Hadoop.

Healthcare is another interesting market for big data, but the dynamics that drive it are less about market forces and more about government intervention and compliance issues. Laws around HIPPA, the recent Healthcare Affordability Act, OC-10 and the HITECH Act of 2009 all have implications for how these organizations implement technology and analytics. Our recent benchmark research on governance, risk and compliance indicates that many companies have significant concerns about compliance issues: 53 percent of participants said they are concerned about them, and 42 percent said they are very concerned. Electronic health records (EHRs) are moving them to more patient-centric systems, and one goal of the Affordable Care Act is to use technology to produce better outcomes through what it calls meaningful use standards.  Facing this title wave of change, companies including IBM analyze historical patterns and link it with real-time monitoring, helping hospitals save the lives of at-risk babies. This use case was made into a now-famous commercial by advertising firm Ogilvy about the so-called data babies. IBM has also shown how cognitive question-and-answer systems such as Watson assist doctors in diagnosis and treatment of patients.

Data blending, the ability to mash together different data sources without having to manipulate the underlying data models, is another analytical technique gaining significant traction. Kaiser Permanente is able to use tools from Alteryx, which I have assessed, to consolidate diverse data sources, including unstructured data, to streamline operations to improve customer service. The two organizations made a joint presentation similar to the one here at Alteryx’s user conference in March.

vr_grc_worried_about_grcFinancial services, which my colleague Robert Kugel covers, is being driven by a combination of regulatory forces and competitive market forces on the sales end. Regulations produce a lag in the adoption of certain big data technologies, such as cloud computing, but areas such as fraud and risk management are being revolutionized by the ability, provided through in-memory systems, to look at every transaction rather than only a sampling of transactions through traditional audit processes. Furthermore, the ability to pair advanced analytical algorithms with in-memory real-time rules engines helps detect fraud as it occurs, and thus criminal activity may be stopped at the point of transaction. On a broader scale, new risk management frameworks are becoming the strategic and operational backbone for decision-making in financial services.

On the retail banking side, copious amounts of historical customer data from multiple banking channels combined with government data and social media data are providing banks the opportunity to do microsegmentation and create unprecedented customer intimacy. Big data approaches to micro-targetting and pricing algorithms, which Rob recently discussed in his blog on Nomis, enable banks and retailers alike to target individuals and customize pricing based on an individual’s propensity to act. While partnerships in the financial services arena are still held close to the vest, the universal financial services providers – Bank of America, Citigroup, JPMorgan Chase and Wells Fargo – are making considerable investments into all of the above-mentioned areas of big data analytics.

Industries other than retail, healthcare and banking are also seeing tangible value in big data analytics. Governments are using it to provide proactive monitoring and responses to catastrophic events. Product and design companies are leveraging big data analytics for everything from advertising attribution to crowdsourcing of new product innovation. Manufacturers are preventing downtime by studying interactions within systems and predicting machine failures before they occur. Airlines are recalibrating their flight routing systems in real time to avoid bad weather. From hospitality to telecommunications to entertainment and gaming, companies are publicizing their big data-related success stories.

Our research shows that until now, big data analytics has primarily been the domain of larger, digitally advanced enterprises. However, as use cases make their way through business and their tangible value is accepted, I anticipate that the activity around big data analytics will increase with companies that reside in the small and midsize business market. At this point, just about any company that is not considering how big data analytics may impact its business faces an unknown and uneasy future. What a difference a year makes, indeed.

Regards,

Tony Cosentino

VP and Research Director

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

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

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

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

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

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

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

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

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

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

Regards,

Tony Cosentino
VP and Research Director

The challenge with discussing big data analytics is in cutting through the ambiguity that surrounds the term. People often focus on the 3 Vs of big data – volume, variety and velocity – which provides a good lens for big data technology, but only gets us part of the way to understanding big data analytics, and provides even less guidance on how to take advantage of big data analytics to unlock business value.

Part of the challenge of defining big data analytics is a lack of clarity vr_bigdata_big_data_capabilities_not_availablearound the big data analytics value chain – from data sources, to analytic scalability, to analytic processes and access methods. Our recent research on big data find many capabilities still not available including predictive analytics (41%) to visualization (37%). Moreover, organizations are unclear on how best to initiate changes in the way they approach data analysis to take advantage of big data and what processes and technologies they ought to be using. The growth in use of appliances, Hadoop and in-memory databases and the growing footprints of RDBMSes all add up to pressure to have more intelligent analytics, but the most direct and cost-effective path from here to there is unclear. What is certain is that as business analytics and big data increasingly merge, the potential for increased value is building expectations.

To understand the organizational chasm that exists with respect to big data analytics, it’s important to understand two foundational analytic approaches that are used in organizations today. Former Census Bureau Director Robert Grove’s ideas around designed data and organic data give us a great jumping off point for this discussion, especially as it relates to big data analytics.

In Grove’s estimation, the 20th century was about designed data, or what might be considered hypothesis-driven data. With designed data we engage in analytics by establishing a hypothesis and collecting data to prove or disprove it. Designed data is at the heart of confirmatory analytics, where we go out and collect data that are relevant to the assumptions we have already made. Designed data is often considered the domain of the statistician, but it is also at the heart of structured databases, since we assume that all of our data can fit into columns and rows and be modeled in a relational manner.

In contrast to the designed data approach of the 20th century, the 21st century is about organic data. Organic data is data that is not limited by a specific frame of reference that we apply to it, and because of this it grows without limits and without any structure other than that structure provided by randomness and probability. Organic data represents all data in the world, but for pragmatic reasons we may think of it as all the data we are able to instrument. RFID, GPS data, sensor data, sentiment data and various types of machine data are all organic data sources that may be characterized by context or by attributes such data sparsity (also known as low-density data). Much like the interpretation of silence in a conversation, analyzing big data is as much about interpreting that which exists between the lines as it is about what we can put on the line itself.

vr_predanalytics_adequacy_of_predictive_analytics_supportThese two types of data and the analytics associated with them reveal the chasm that exists within organizations and shed light on the skills gap that our predictive analytics benchmark research shows to be the primary challenge for analytics in organizations today. This research finds inadequate support in many areas including product training (26%) and how to apply to business problems (23%).

On one side of the chasm are the business groups and the analysts who are aligned with Grove’s idea of designed data. These groups may encompass domain experts in areas such as finance or marketing, advanced Excel users, and even Ph.D.-level statisticians. These analysts serve organizational decision-makers and are tied closely to actionable insights that lead to specific business outcomes. The primary way they get work done is through a flat file environment, as was outlined in some detail last week by my colleague Mark Smith. In this environment, Excel is often the lowest common denominator.

On the other side of the chasm exist the IT and database professionals, where a different analytical culture and mindset exist. The priority challenge for this group is dealing with the three Vs and simply organizing data into a legitimate enterprise data set. This group is often more comfortable with large data sets and machine learning approaches that are the hallmark of the organic data of 21st century. Their analytical environment is different from that of their business counterparts; rather than Excel, it is SQL that is often the lowest common denominator.

As I wrote in a recent blog post, database professionals and business analytics practitioners have long lived in parallel universes. In technology, practitioners deal with tables, joins and the ETL process. In business analysis, practitioners deal with datasets, merges and data preparation. When you think about it, these are the same things. The subtle difference is that database professionals have had a data mining mindset, or, as Grove calls it, an organic data mindset, while the business analyst has had a designed data or statistic-driven mindset.  The bigger differences revolve around the cultural mindset, and the tools that are used to carry out the analytical objectives. These differences represent the current conundrum for organizations.

In a world of big data analytics, these two sides of the chasm are being pushed together in a shotgun wedding because the marriage of these groups is how competitive advantage is achieved. Both groups have critical contributions to make, but need to figure out how to work together before they can truly realize the benefits of big data analytics. The firms that understand that the merging of these different analytical cultures is the primary challenge facing the analytics organization, and that develop approaches that deal with this challenge, will take the lead in big data analytics. We already see this as a primary focus area for leading professional services organizations.

In my next analyst perspective on big data I will lay out some pragmatic approaches companies are using to address this big data analytics chasm; these also represent the focus of the benchmark research we’re currently designing to understand organizational best practices in big data analytics.

Regards,

Tony Cosentino

VP and Research Director

Revolution Analytics is a commercial provider of software and services related to enterprise implementations of the open source language R. At its base level, R is a programming language built by statisticians for statistical analysis, data mining and predictive analytics. In a broader sense, it is data analysis software used by data scientists to access data, develop and perform statistical modeling and visualize data. The R community has a growing user base of more than two million worldwide, and more than 4,000 available applications cover specific problem domains across industries. Both the R Project and Revolution Analytics have significant momentum in the enterprise and in academia.

Revolution Analytics provides value by taking the most recent release from the R community and adding scalability and other functionality so that R can be implemented and seamlessly work in a commercial environment. Revolution R provides a development environment so that data scientists can write and debug R code more effectively, and web service APIs that integrate with other BI tools and dashboards so that R can work with business intelligence tools and visual discovery tools. In addition, Revolution Analytics makes money through professional and support services.

Companies are collecting enormous amounts of data, but few have activevr_bigdata_the_volume_of_big_data big data analytics strategies. Our big data benchmark research shows that more than 50 percent of companies in our sample maintain more than 10TB of data, but often they cannot analyze the data due to scale issues. Furthermore, our research into predictive analytics says that integrating into the current architecture is the biggest obstacle facing the implementation of predictive analytics.

Revolution Analytics helps address these challenges in a few ways. It can perform file-based analytics, where a single node orchestrates commands across a cluster of commodity servers and delivers the results back to the end user. This is an on-premise solution that runs on Linux clusters or Microsoft HPC clusters. A perhaps more exciting use case is alignment with the Hadoop MapReduce paradigm, where Revolution Analytics allows for direct manipulation of the HDFS file system, can submit a job directly to the Hadoop jobtracker, and can directly define and manipulate analytical data frames through Hbase database tables. When front-ended with a visualization tool such as Tableau, this ability to work with data directly in Hadoop becomes a powerful tool for big data analytics. A third use case has to do with the parallelization of computations within the database itself. This in-database approach is gaining a lot of traction for big data analysis primarily because it is the most efficient way to do analytics on very large structured datasets without moving a lot of data. For example, IBM’s PureData System for Analytics (IBM’s new name for its MPP Netezza appliance) uses the in-database approach with an R instance running on each processing unit in the database, each of which is connected to an R server via ODBC. The analytics are invoked as the data is served up to the processor such that the algorithms run in parallel across all of the data.

In the big data analytics context, speed and scale are critical drivers of success, and Revolution R delivers on both. It is built with the Intel Math Kernel Library, so that processing is streamlined for multithreading at the processor level and it can leverage multiple cores simultaneously. In test cases on a single node, R was only able to scale to observations of about 400,000 in a linear regression model, while Enterprise R was able to go into the millions. With respect to speed, Revolution R 6.1 was able to conduct a principal component analysis in about 11 seconds versus 142 seconds with version R-2 14.2. As similar tests are performed across multiple processors in a parallelized fashion, the observed performance difference increases.

vr_predanalytics_predictive_analytics_obstaclesSo why does all of this matter? Our benchmark research into predictive analytics shows that companies that are able to score and update their models more efficiently show higher maturity and gain greater competitive advantage. From an analytical perspective, we can now squeeze more value out of our large data sets. We can analyze all of the data and take a census approach instead of a sampling approach which in turn allows us to better understand the errors that exist in our models and identify outliers and patterns that are not linear in nature. Along with prediction, the ability to identify outliers is probably the most important capability since seeing the data anomalies often leads to the biggest insights and competitive advantage. Most importantly, from a business perspective, we can apply models to understand things such as individual customer behavior, a company’s risk profile or how to take advantage of market uncertainty through tactical advantage.

I’ve heard that Revolution R isn’t always the easiest software to use and that the experience isn’t exactly seamless, but it can be argued that in the cutting-edge field of big data analytics a challenging environment is to be expected. If Revolution Analytics can address some of these useability challenges, it may find its pie growing even faster than it is now. Regardless, I anticipate that Revolution Analytics will continue its fast growth (already its headcount is doubling year-over-year). Furthermore, I anticipate that in-database analytics (an area where R really shines) will become the de-facto approach to big data analytics and that companies that take full advantage of that trend will reap benefits.

Regards,

Tony Cosentino

VP and Research Director

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