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IBM redesigned its business intelligence platform, now called IBM Cognos Analytics. Expected to be released by the end of 2015, the new version includes features to help end users model their own data without IT assistance while maintaining the centralized governance and security that the platform already has. Our benchmark research into information optimization shows that simplifying access to information is important to vr_Info_Optimization_01_whos_responsible_for_information_availabilityvirtually all (97%) participating organizations, but it also finds that only one in four (25%) are satisfied with their current software for doing that. Simplification is a major theme of the IBM Cognos redesign.

The new IBM Cognos Analytics provides a completely Web-based environment that is consistent in the user interface and security across multiple devices and browsers. The redesigned interface follows IBM’s internal cultural shift to base product development first on the user experience and second on features and functionality. This may be a wise move as our research across multiple analytic software categories finds usability to be organizations’ most often important buying criterion.

The redesign is based on the same design and self-service principles as IBM Watson Analytics which we did award a Ventana Research Technology Innovation Award for 2015 in business analytics. The redesign is most evident in the IBM Cognos Analytics authoring mode. The Report Studio and Cognos Workspace Advanced modules have been replaced with a simplified Web-based modeling environment. The extended capabilities of IBM Cognos 10.2.2 are still available, but now they are hidden and more logically arranged to provide easier user access. For example, the previous version of Cognos presented an intimidating display of tools with which to do tasks such as fine-grain manipulation of reports; now these features are hidden but still easily accessible. If a user is having difficulty finding a particular function, a “smart search” feature helps to find the correct menu to add it.

The new system indexes objects, including metadata, as they are created, providing a more robust search function suitable for nontechnical users in the lines of business. The search feature works with what IBM calls “intent-based modeling” so users can search for words or phrases – for example, revenue by unit or product costs – and be presented with only relevant tables and columns. The system can then automatically build a model by inferring relationships in the data. The result is that the person building the report need not manually design a multidimensional model of the data, so less skilled end users can serve themselves to build their own data models that underpin dashboards and reports. Previously, end users were limited to parameterized reporting in which they could work only within the context of models previously designed by IT. Many vendors of analytics have been late in exploiting the power of search and therefore may be missing a critical feature that customers desire. Ventana Research is a proponent of such capabilities; my colleague Mark Smith has written about them in the context of data discovery technology. Search is fundamental to user-friendly discovery systems, as is reflected in the success of companies such as Google and Splunk. With search becoming more sophisticated, being based on machine-learning algorithms, we expect it to become a key requirement for new analytics and business intelligence systems.

Furthering the self-service aspect is the ability for end users to access and combine multiple data sets. The previous version of IBM Cognos (10.2.2) allowed users to work with “personal data sets” such as .csv files, but they needed an IBM DB2 back end to house the files. Now such data sets can be uploaded and managed directly on the IBM Cognos Analytics server and accessed with the new Web-based authoring tool. Once data sets are uploaded they can be accessed and modeled like any other object to which the user has access. In this way, IBM Cognos Analytics addresses the “bring your own data” challenge in which data sources such as personal spreadsheets must be integrated into enterprise analytics and business intelligence systems.

After modeling the data, users can lay out new dashboards using drag-and-drop capabilities like those found in IBM Watson Analytics. Dashboards can be previewed and put into service for one-time use or put into production mode if the user has such privileges. As is the case with IBM Watson Analytics, newly designed dashboard components such as tables, charts and maps are automatically linked so that changes in one part of the dashboard automatically relate to other parts. This feature facilitates ease of use in designing dashboards. Some other tools in the market require widgets to be connected manually, which can be time-consuming and is an impediment to prototyping of dashboards.

The move to a more self-service orientation has long been in the works for IBM Cognos and so this release is an important one for IBM. The ability to automatically integrate and model data gives the IT department a more defensible position as other self-service tools are introduced into the organization and are challenging data access and preparation built within tools like IBM Cognos. vr_DAC_20_justification_for_data_preparationThis is becoming especially important as the number and complexity of data sources increases and are needed more rapidly by business. Our research into information optimization shows that most organizations need to integrate at least six data sources and some have 20 or more sources they need to bring together. All of which confirms what our data and analytics in the cloud benchmark research finds data preparation to be a top priority in over half (55%) of organizations.

Over time, IBM intends to integrate the capabilities of Cognos Analytics with those of Watson Analytics. This is an important plan because IBM Watson Analytics has capabilities beyond those of self-service tools in the market today. In particular, the ability to explore unknown data relationships and do advanced analysis is a key differentiator for IBM Watson Analytics, as I have written. IBM Watson Analytics enables users to explore relationships in data that otherwise would not be noticeable, whereas IBM Cognos Analytics enables them to explore and put into production information based on predefined assumptions.

Going forward, I will be watching how IBM aligns Cognos Analytics with Watson Analytics, and in particular, how Cognos Analytics will fit into the IBM cloud ecosystem. Currently IBM Cognos Analytics is offered both on-premises and in a hosted cloud, but here also IBM is working to align it VR_AnalyticsandBI_VI_HotVendor_2015more closely with IBM Watson Analytics. Bringing in data preparation, data quality and MDM capabilities from the IBM DataWorks product could also benefit IBM Cognos Analytics users. IBM should emphasize the breadth of its portfolio of products including IBM Cognos TM1, IBM SPSS, IBM Watson Analytics and IBM DataWorks as it faces stiff competition in enterprise analytics and business intelligence from a host of analytics companies including new cloud-based ones. IBM is rated a Hot Vendor in our Ventana Research Analytics and Business Intelligence Value Index in part because of its overall portfolio.

For organizations already using IBM Cognos, the redesign addresses the need of end users to create their own dashboards while maintaining IT governance and control. The new interface may take some getting used to, but it is modern and more intuitive than previously. For companies new to IBM Cognos, as well as departments wanting to take a look at the platform, cloud options offer less risk. For those wanting early access to the new IBM Cognos Analytics, IBM has provided access to it on www.analyticszone.com. The changes I have noted move IBM Cognos Analytics closer to the advances in analytics as a whole, and I recommend that all these groups examine the new version.

Regards,

Ventana Research

Tableau Software’s annual conference, which company spokespeople reported had more than 10,000 attendees, filled the MGM Grand in Las Vegas. Various product announcements supported the company’s strategy to deliver value to analysts and users of visualization tools. Advances include new data preparation and integration features, advanced analytics and mapping. The company also announced the release of a stand-alone mobile application called Vizable . One key message management aimed to promote is that Tableau is more than just a visualization company.

Over the last few years Tableau has made strides in the analytics and business intelligence market with a user-centric philosophy and the ability to engage younger analysts who work in the lines of business rather than in IT. Usability continues to rank as the top criteria for selecting analytic and business intelligence software in all of our business analytics benchmark research. In this area Tableau has introduced innovations such as VizQL, originally developed at Stanford University, which links capabilities to query a database and to visualize data. This combination enables users not highly skilled in languages such as SQL or using proprietary business intelligence tools to create and share visually intuitive dashboards. The effect is to provide previously unavailable visibility into areas of their operations. The impact of being able to see and compare performance across operations and people often increases communication and knowledge sharing.

Tableau 9, released in April 2015, which I discussed, introduced advances including analytic ease of use and performance, new APIs, data preparation, storyboarding and Project Elastic, the precursor to this year’s announcement of Vizable. Adoption of 9.x appears to be robust given both the number of conference attendees and increases in third-quarter revenue ($170 million) and new customers (3,100) reported to the financial markets.

As was the case last year, conference announcements included some developments already on the market as well as some still to come. Among data preparation capabilities introduced are integration and automated spreadsheet cleanup. For the former, being able to join two data sets through a union function, which adds rows to form a single data set, and to do integration across databases by joining specific data fields gives users flexibility in combining, analyzing and visualizing multiple sets of data. For the latter, to automate the spreadsheet cleanup process Tableau examined usage patterns of Tableau Public to learn how users manually clean their spreadsheets. Then it used machine-learning algorithms to help users automate the tasks. Being able to automatically scan Excel files to find subtables and automatically transform data without manual calculations and parsing will save time for analysts who vr_LA_most_important_location_analytics_capabilitiesotherwise would have to do these tasks manually. Our benchmark research into information optimization shows that data preparation consumes the largest portion of time spent on analytics by nearly half (47%) of organizations and even higher in our latest data and analytics in the cloud benchmark research by 59 percent of organizations.

Advanced analytics is another area of innovation for Tableau. The company demonstrated developments in outlier detection and clustering analysis natively integrated with the software. Use of these features is straightforward and visually oriented, replacing the need for statistical charts with drag-and-drop manipulation. The software does not enable users to identify numbers of segments or filter the degree of the outliers, but the basic capability can reduce data sets to more manageable analytic sets and facilitate exploration of anomalous data points within large sets. The skill necessary for these tasks, unlike the interpretation of box plots introduced at last year’s conference, is more intuitive and better suited for business users of information.

The company also demonstrated new mapping and geospatial features at the conference. Capabilities to analyze down to the zip code on a global basis, define custom territories, support geospatial files, integrate with vr_LA_most_important_location_analytics_capabilitiesthe open source mapping platform MapBox and perform calculations within the context of a digital map are all useful features for location analytics, which is becoming more important in areas such as customer analytics and digital devices connected in the emerging Internet of things (IoT). Tableau is adding capabilities that participants most often cited as important in our research on location analytics: to provide geographic representation (72%), visualize metrics associated with locations (65%) and directly select and analyze locations on maps (61%).

Tableau insists that its development of new capabilities is guided by customer requests. This provides a source of opportunities to address user needs especially in the areas of data preparation, advanced analytics and location analytics. However, this strategy raises the question of whether it will ultimately put the company in conflict with the partners that have helped build the Tableau ecosystem and feed the momentum of the company thus far. Tableau is positioning its product as a fully featured analytic platform of the sort that I have outlined, but to achieve that eventually it will have to encroach on the capabilities that partners such as Alteryx, Datawatch, Informatica, Lavastorm, Paxata and Trifacta offer today. Another question is whether Tableau will continue its internal development strategy or opt to acquire companies that can broaden its capabilities that has hampered its overall value rating as identified in our 2015 Analytics and Business intelligence Value Index. In light of announcements at the conference, the path seems to be to develop these capabilities in-house. While there appears to be no immediate threat to the partnerships the continuation of development of some of these capabilities eventually will impact the partner business model in a more material way. Given that the majority of the deals for its partner ecosystem flows through Tableau itself, many of the partners are vulnerable to these development efforts. In addition I will be watching how aggressively Tableau helps to market Spark, the open source big data technology that I wrote about, as compared to some of the partner technologies that Spark threatens. Tableau has already built on Spark while some of its competitors have not, which may give Tableau a window of opportunity.

Going forward, integration with transactional systems and emerging cloud ecosystems is an area for Tableau that I will be watching. Given its architecture it’s not easy for Tableau to participate in the new generation of service-oriented architectures that characterize part of today’s cloud marketplace. For this reason, Tableau will need to continue to build out its own platform and the momentum of its ecosystem – which at this point does not appear to be a problem.

Finally, it will be interesting to see how Tableau eventually aligns its stand-alone data visualization application Vizable with its broader mobile strategy. We will be looking closely at the mobile market in our upcoming Mobile Analytics and Business Intelligence Value Index in the first half of 2016 where in our last analysis found Tableau was in the middle of the pack with other providers but they have made more investments since our last analysis.

We recommend that companies exploring analytics platforms, especially for on-premises and hosted cloud use, include Tableau on their short lists. Organizations that consider deploying Tableau on an enterprise basis should look closely at how it aligns with their broader user requirements and if their cloud strategy will meet its future needs. Furthermore, while the company has made improvements in manageability and performance, these can still be a concern in some circumstances. Tableau should be evaluated also with specific business objectives in mind and in conjunction with its partner ecosystem.

Regards,

Ventana Research

PentahoWorld 2015, Pentaho’s second annual user conference, held in mid-October, centered on the general availability of release 6.0 of its data integration and analytics platform and its acquisition by Hitachi Data Systems (HDS) earlier this year. Company spokespeople detailed the development of the product in relation to the roadmap laid out in 2014 and outlined plans for its integration with those of HDS and its parent Hitachi. They also discussed Pentaho’s and HDS’s shared intentions regarding the Internet of Things (IoT), particularly in telecommunications, healthcare, public infrastructure and IT analytics.

Pentaho competes on the basis of what it calls a “streamlined data refinery” that enables a flexible way to access, transform and integrate data and embed and present analytic data sets in usable formats without writing new code. In addition, it integrates a visual analytic workflow interface with a business intelligence front end including customization extensions; this is a differentiator for the company since much of the self-serve analytics market in which it competes is still dominated by separate point products.

Pentaho 6 aims to provide manageable and scalable self-service analytics. A key advance in the new version is what Pentaho calls “virtualized data sets” that logically aggregate multiple data sets according to transformations and integration specified by the Pentaho Data Integration (PDI) analytic workflow interface. This virtual approach allows the physical processing to be executed close to the data in various systems such as Hadoop or an RDBMS, which relieves users of the burden of having to continually move data back and forth between the vr_oi_factors_impeding_ol_implementationquery and the response systems. In this way, logical data sets can be served up for consumption in Pentaho Analytics as well as other front-end interfaces in a timely and flexible manner.

One challenge that emerges when accessing multiple integrated and transformed data sets is data lineage. Tracking its lineage is important to establish trust in the data among users by enabling them to ascertain the origin of data prior to transformation and integration. This is particularly useful in regulated industries that may need access to and tracking of source data to prove compliance. This becomes even more complicated with events and completely sourcing them along with the large number of them as found in over a third of organizations in our operational intelligence benchmark research that examined operational centric analytics and business intelligence.

Similarly, Pentaho 6 uses Simple Network Management Protocol (SNMP) to deliver application programming interface (API) extensions so that third-party tools can help provide governance lower in the system stack to further enable reliability of data. Our benchmark research consistently shows that manageability of systems is important for user organizations and in particular for big data environments.

The flexibility introduced with virtual tables and improvements in Pentaho 6.0 around in-line modeling (a concept I discussed after last year’s event are two critical means to building self-service analytic environments. Marrying various data systems with different data models, sometimes referred to as big data integration, has proven to be a difficult challenge in such environments. Pentaho’s continued focus on vr_BDI_01_automating_big_data_integrationbig data integration and providing an integration backbone to the many business intelligence tools (in addition to its own) are potential competitive differentiators for the company. While analysts and users prefer integrated tool sets, today’s fragmented analytics market is increasingly dominated by separate tools that prepare data and surface data for consumption. Front-end tools alone cannot automate the big data integration process, which Pentaho PDI can do.Our research into big data integration shows the importance of eliminating manual tasks in this process: 78 percent of companies said it is important or very important to automate their big data integration processes. Pentaho’s ability to integrate with multiple visual analytics tools is important for the company, especially in light of the HDS accounts, which likely have a variety of front-end tools. In addition, the ability to provide an integrated front end can be attractive to independent software vendors, analytics services providers and certain end-user organizations that would like to embed both integration and visualization without having to license multiple products.

Going forward, Pentaho is focused on joint opportunities with HDS such as the emerging Internet of Things. Pentaho cites established industrial customers such as Halliburton, Intelligent Mechatonic Systems and Kirchoff Datensysteme Software as reference accounts for IoT. In addition, a conference participant from Caterpillar Marine Asset Intelligence shared how it embeds Pentaho to help analyze and predict equipment failure on maritime equipment. Pentaho’s ability to integrate and analyze multiple data sources is key to delivering business value in each of these environments, but the company also possesses a little-known asset in the Weka machine learning library, which is an integrated part of the product suite. Our research on next-generation predictive analytics finds that Weka is used by 5 percent of organizations, and many of the companies that use it are large or very large, which is Pentaho’s target market. Given the importance of machine learning in the IoT category, it will be interesting to see how Pentaho leverages this asset.

Also at the conference, an HDS spokesperson discussed its target markets for IoT or what the company calls “social innovation.” These markets include telecommunications, healthcare, public infrastructure and IT analytics and reflect HDS’s customer base and the core businesses of its parent company Hitachi. Pentaho Data Integration is currently embedded within major customer environments such as Caterpillar, CERN, FINRA, Halliburton, NASDAQ, Sears and Staples, but not all of these companies fit directly into the IoT segments HDS outlined. While Hitachi’s core businesses provide a fertile ground in which grow its business, Pentaho will need to develop integration with the large industrial control systems already in place in those organizations.

The integration of Pentaho into HDS is a key priority. The 2,000-strong global sales force of HDS is now incented to sell Pentaho, and it will be important for the reps to include it as they discuss their accounts’ needs. While Pentaho’s portfolio can potentially broaden sales opportunities for HDS, big data software is a more consultative sale than the price-driven hardware and systems that the sales force may be used to. Furthermore, the buying centers, which are shifting from IT to lines of business, can be significantly different based on the type of organization and their objectives. To address this will require significant training within the HDS sales force and with partner consulting channels. The joint sales efforts will be well served by emphasizing the “big data blueprints” developed by Pentaho over the last couple of years and developing of new ones that speak to IoT and the combined capabilities of the two companies.

HDS says it will begin to embed Pentaho into its product portfolio but has promised to leave Pentaho’s roadmap intact. This is important because Pentaho has done a good job of listening to its customers and addressing the complexities that exist in big data and open source environments. As the next chapter unfolds, I will be looking at how the company integrates its platform with the HDS portfolio and expands it to deal with the complexities of IoT, which we will be investigating in upcoming benchmark research study.

For organizations that need to use large-scale integrated data sets, Pentaho provides one of the most flexible yet mature tools in the market, and they should consider it. The analytics tool provides an integrated and embeddable front end that should be of particular interest to analytics services providers and independent software vendors seeking to make information management and data analytics core capabilities. For existing HDS customers, the Pentaho portfolio will open conversations in new areas of those organizations and potentially add considerable value within accounts.

Regards,

Ventana Research

Ventana Research recently completed the most comprehensive evaluation of analytics and business intelligence products and vendors available anywhere. As I discussed recently, such research is necessary and timely as analytics and business intelligence is now a fast-changing market. Our Value Index for Analytics and Business Intelligence in 2015 scrutinizes 15 top vendors and their product offerings in seven key
categories: Usability, Manageability, Reliability, Capability, Adaptability, Vendor Validation and TCO/ROI. The analysis shows that the top supplier is Information Builders, which qualifies as a Hot vendor and is followed by 10 other Hot vendors: SAP, IBM, MicroStrategy, Oracle, vr_VI_BI_2015_Weighted_OverallSAS, Qlik, Actuate (now part of OpenText) and Pentaho.

The evaluations drew on our research and analysis of vendors’ and products along with their responses to our detailed RFI or questionnaire, our own hands-on experience and the buyer-related findings from our benchmark research on next-generation business intelligence, information optimization and big data analytics. The benchmark research examines analytics and business intelligence from various perspectives to determine organizations’ current and planned use of these technologies and the capabilities they require for successful deployments.

We find that the processes that comprise business intelligence today have expanded beyond standard query, reporting, analysis and publishing capabilities. They now include sourcing and integration of data and at later stages the use of analytics for planning and forecasting and of capabilities utilizing analytics and metrics for collaborative interaction and performance management. Our research on big data analytics finds that new technologies collectively known as big data vr_Big_Data_Analytics_15_new_technologies_enhance_analyticsare influencing the evolution of business intelligence as well; here in-memory systems (used by 50% of participating organizations), Hadoop (42%) and data warehouse appliances (33%) are the most important innovations. In-memory computing in particular has changed BI because it enables rapid processing of even complex models with very large data sets. In-memory computing also can change how users access data through data visualization and incorporate data mining, simulation and predictive analytics into business intelligence systems. Thus the ability of products to work with big data tools figured in our assessments.

In addition, the 2015 Value Index includes assessments of their self-service tools and cloud deployment options. New self-service approaches can enable business users to reduce their reliance on IT to access and use data and analysis. However, our information optimization research shows that this change is slow to proliferate. In four out of five organizations, IT currently is involved in making information available to end users vr_Info_Optimization_01_whos_responsible_for_information_availabilityand remains entrenched in the operations of business intelligence systems.

Similarly, our research, as well as the lack of maturity of the cloud-based products evaluated, shows that organizations are still in the early stages of cloud adoption for analytics and business intelligence; deployments are mostly departmental in scope. We are exploring these issues further in our benchmark research into data and analytics in the cloud, which will be released in the second quarter of 2015.

The products offered by the five top-rated com­pa­nies in the Value Index provide exceptional functionality and a superior user experi­ence. However, Information Builders stands out, providing an excep­tional user experience and a completely integrated portfolio of data management, predictive analytics, visual discovery and operational intelligence capabilities in a single platform. SAP, in second place, is not far behind, having made significant prog­ress by integrating its Lumira platform into its BusinessObjects Suite; it added pre­dictive analytics capabilities, which led to higher Usability and Capability scores. IBM, MicroStrategy and Oracle, the next three, each provide a ro­bust integrated platform of capabilities. The key differentiator between them and the top two top is that they do not have superior scores in all of the seven categories.

In evaluating products for this Value Index we found some noteworthy innovations in business intelligence. One is Qlik Sense, which has a modern architecture that is cloud-ready and supports responsive design on mobile devices. Another is SAS Visual Analytics, which combines predictive analytics with visual discovery in ways that are a step ahead of others currently in the market. Pentaho’s Automated Data Refinery concept adds its unique Pentaho Data Integration platform to business intelligence for a flexible, well-managed user experience. IBM Watson Analytics uses advanced analytics and VR_AnalyticsandBI_VI_2015natural language processing for an interactive experience beyond the traditional paradigm of business intelligence. Tableau, which led the field in the category of Usability, continues to innovate in the area of user experience and aligning technology with people and process. MicroStrategy’s innovative Usher technology addresses the need for identity management and security, especially in an evolving era in which individuals utilize multiple devices to access information.

The Value Index analysis uncovered notable differences in how well products satisfy the business intelligence needs of employees working in a range of IT and business roles. Our analysis also found substantial variation in how products provide development, security and collaboration capabilities and role-based support for users. Thus, we caution that similar vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well suited for use by every organization or for a specific process.

To learn more about this research and to download a free executive summary, please visit.

Regards,

Ventana Research

Ventana Research recently completed the most comprehensive evaluation of analytics and business intelligence products and vendors available anywhere. As I discussed recently, such research is necessary and timely as analytics and business intelligence is now a fast-changing market. Our Value Index for Analytics and Business Intelligence in 2015 scrutinizes 15 top vendors and their product offerings in seven keyvr_VI_BI_2015_Weighted_Overall categories: Usability, Manageability, Reliability, Capability, Adaptability, Vendor Validation and TCO/ROI. The analysis shows that the top supplier is Information Builders, which qualifies as a Hot vendor and is followed by 10 other Hot vendors: SAP, IBM, MicroStrategy, Oracle, SAS, Qlik, Actuate (now part of OpenText) and Pentaho.

The evaluations drew on our research and analysis of vendors’ and products along with their responses to our detailed RFI or questionnaire, our own hands-on experience and the buyer-related findings from our benchmark research on next-generation business intelligence, information optimization and big data analytics. The benchmark research examines analytics and business intelligence from various perspectives to determine organizations’ current and planned use of these technologies and the capabilities they require for successful deployments.

We find that the processes that comprise business intelligence today have expanded beyond standard query, reporting, analysis and publishing capabilities. They now include sourcing and integration of data and at later stages the use of analytics for planning and forecasting and of capabilities utilizing analytics and metrics for collaborative interaction and performance management. Our research on big data analytics finds that new technologies collectively known as big data vr_Big_Data_Analytics_15_new_technologies_enhance_analyticsare influencing the evolution of business intelligence as well; here in-memory systems (used by 50% of participating organizations), Hadoop (42%) and data warehouse appliances (33%) are the most important innovations. In-memory computing in particular has changed BI because it enables rapid processing of even complex models with very large data sets. In-memory computing also can change how users access data through data visualization and incorporate data mining, simulation and predictive analytics into business intelligence systems. Thus the ability of products to work with big data tools figured in our assessments.

In addition, the 2015 Value Index includes assessments of their self-service tools and cloud deployment options. New self-service approaches can enable business users to reduce their reliance on IT to access and use data and analysis. However, our information optimization research shows that this change is slow to proliferate. In four out of five organizations, IT currently is involved in making information available to end users vr_Info_Optimization_01_whos_responsible_for_information_availabilityand remains entrenched in the operations of business intelligence systems.

Similarly, our research, as well as the lack of maturity of the cloud-based products evaluated, shows that organizations are still in the early stages of cloud adoption for analytics and business intelligence; deployments are mostly departmental in scope. We are exploring these issues further in our benchmark research into data and analytics in the cloud, which will be released in the second quarter of 2015.

The products offered by the five top-rated com­pa­nies in the Value Index provide exceptional functionality and a superior user experi­ence. However, Information Builders stands out, providing an excep­tional user experience and a completely integrated portfolio of data management, predictive analytics, visual discovery and operational intelligence capabilities in a single platform. SAP, in second place, is not far behind, having made significant prog­ress by integrating its Lumira platform into its BusinessObjects Suite; it added pre­dictive analytics capabilities, which led to higher Usability and Capability scores. IBM, MicroStrategy and Oracle, the next three, each provide a ro­bust integrated platform of capabilities. The key differentiator between them and the top two top is that they do not have superior scores in all of the seven categories.

In evaluating products for this Value Index we found some noteworthy innovations in business intelligence. One is Qlik Sense, which has a modern architecture that is cloud-ready and supports responsive design on mobile devices. Another is SAS Visual Analytics, which combines predictive analytics with visual discovery in ways that are a step ahead of others currently in the market. Pentaho’s Automated Data Refinery concept adds its unique Pentaho Data Integration platform to business intelligence for a flexible, well-managed user experience. IBM Watson Analytics uses advanced analytics and VR_AnalyticsandBI_VI_2015natural language processing for an interactive experience beyond the traditional paradigm of business intelligence. Tableau, which led the field in the category of Usability, continues to innovate in the area of user experience and aligning technology with people and process. MicroStrategy’s innovative Usher technology addresses the need for identity management and security, especially in an evolving era in which individuals utilize multiple devices to access information.

The Value Index analysis uncovered notable differences in how well products satisfy the business intelligence needs of employees working in a range of IT and business roles. Our analysis also found substantial variation in how products provide development, security and collaboration capabilities and role-based support for users. Thus, we caution that similar vendor scores should not be taken to imply that the packages evaluated are functionally identical or equally well suited for use by every organization or for a specific process.

To learn more about this research and to download a free executive summary, please visit.

Regards,

Ventana Research

Just a few years ago, the prevailing view in the software industry was that the category of business intelligence (BI) was mature and without room for innovation. Vendors competed in terms of feature parity and incremental advancements of their platforms. But since then business intelligence has grown to include analytics, data discovery tools and big data capabilities to process huge volumes and new types of data much faster. As is often the case with change, though, this one has created uncertainty. For example, only one in 11 participants in our benchmark research on big data analytics said that their organization fully agrees on the meaning of the term “big data analytics.”

There is little question that clear definitions of analytics and business intelligence as they are used in business today would be of value. But some IT analyst firms have tried to oversimplify the process of updating these definitions by merely combining a market basket of discovery capabilities under the label of analytics. In our estimation, this attempt is neither accurate nor useful. Discovery tools are only components of business intelligence, and their capabilities cannot accomplish all the tasks comprehensive BI systems can do. Some firms seem to want to reduce the field further by overemphasizing the visualization aspect of discovery. While visual discovery can help users solve basic business problems, other BI and analytic tools are available that can attack more sophisticated and technically challenging problems. In our view, visual discovery is one of four types of analytic discovery that can help organizations identify and understand the masses of data they accumulate today. But for many organizations visualization alone cannot provide them with the insights necessary to help make critical decisions, as interpreting the analysis requires expertise that mainstream business professionals lack.

In Ventana Research’s view, business intelligence is a technology managed by IT that is designed to produce information and reports from business data to inform business about the performance of activities, people and processes. It has provided and will continue to provide great value to business, but in itself basic BI will not meet the new generation of requirements that businesses face; they need not just information but guidance on how to take advantage of opportunities, address issues and mitigate the risks of subpar performance. Ventana_Research_Value_Index_LogoAnalytics is a component of BI that is applied to data to generate information, including metrics. It is a technology-based set of methodologies used by analysts as well as the information gained through the use of tools designed to help those professionals. These thoughtfully crafted definitions inform the evaluation criteria we apply in our new and comprehensive 2015 Analytics and Business Intelligence Value Index, which we will publish soon. As with all business tools, applications and systems we assess in this series of indexes, we evaluate the value of analytic and business intelligence tools in terms of five functional categories – usability, manageability, reliability, capability and adaptability – and two customer assurance categories – validation of the vendor and total cost of ownership and return on investment (TCO/ROI). We feature our findings in these seven areas of assessment in our Value Index research and reports. In the Analytics and Business Intelligence Value Index for 2015 we assess in depth the products of 15 of the leading vendors in today’s BI market.

The Capabilities category examines the breadth of functionality that products offer and assesses their ability to deliver the insights today’s enterprises need. For our analysis we divide this category into three subcategories for business intelligence: data, analytics and optimization. We explain each of them below.

The data subcategory of Capabilities examines data access and preparation along with supporting integration and modeling. New data sources are coming into being continually; for example, data now is generated in sensors in watches, smartphones, cars, airplanes, homes, utilities and an assortment of business, network, medical and military equipment. In addition, organizations increasingly are interested in behavioral and attitudinal data collected through various communication platforms. Examples include Web browser behavior, data mined from the Internet, social media and various survey and community polling data. The data access and integration process identifies each type of data, integrates it with all other relevant types, checks it all for quality issues, maps it back to the organization’s systems of record and master data, and manages its lineage. Master data management in particular, including newer approaches such as probabilistic matching, is a key component for creating a system that can combine data types across the organization and in the cloud to create a common organizational vernacular for the use of data.

Ascertaining which systems must be accessed and how is a primary challenge for today’s business intelligence platforms. A key part of data access is the user interface. Whether it appears in an Internet browser, a laptop, a smartphone, a tablet or a wearable device, data must be presented in a manner optimized for the interface. Examining the user interface for business intelligence systems was a primary interest of our 2014 Mobile Business Intelligence Value Index. In that research, we learned that vendors are following divergent paths and that it may be hard for some to change course as they continue. Therefore how a vendor manages mobile access and other new means impacts its products’ value for particular organizations.

Once data is accessed, it must be modeled in a useful way. Data models in the form of OLAP cubes and predefined relationships of data sometimes grow overly complex, but there is value in premodeling data in ways that make sense to business people, most of whom are not up to modeling it for themselves. Defining data relationships and transforming data through complex manipulations is often needed, for instance, to define performance indicators that align with an organization’s business initiatives. These manipulations can include business rules or what-if analysis within the context of a model or external to it. Finally, models must be flexible so they do not hinder the work of organizational users. The value of premodeling data is that it provides a common view for business users so they need not redefine data relationships that have already been thoroughly considered.

The analytics subcategory includes analytic discovery, prediction and integration. Discovery and prediction roughly map to the ideas of exploratory and confirmatory analytics, which I have discussed. Analytic discovery includes calculation and visualization processes that enable users to move quickly and easily through data to create the types of information they need for business purposes. Complementing it is prediction, which typically follows discovery. Discovery facilitates root-cause and historical analysis, but to look ahead and make decisions that produce desired business outcomes, organizations need to track various metrics and make informed predictions. Analytic integration encompasses customization of both discovery and predictive analytics and embedding them in other systems such as applications and portals.

The optimization subcategory includes collaboration, organizational management, information optimization, action and automation. Collaboration is a key consideration for today’s analytic platforms. It includes the ability to publish, share and coordinate various analytic and business intelligence functions. Notably, some recently developed collaboration platforms incorporate many of the characteristics of social platforms such as Facebook or LinkedIn. Organizational management attempts to manage to particular outcomes and sometimes provides performance indicators and scorecard frameworks. Action assesses how technology directly assists decision-making in an operational context. This includes gathering inputs and outputs for collaboration before and after a decision, predictive scoring that prescribes action and delivery of the information in the correct form to the decision-maker. Finally, automation triggers alerts in circumstances based on statistical triggers or rules and should be managed as part of a workflow. Agent technology takes automation to a level that is more proactive and autonomous.

vr_Info_Optim_Maturity_06_oraganization_maturity_by_dimensionsThis broad framework of data, analytics and optimization fits with a process orientation to business analytics that I have discussed. Our benchmark research on information optimization indicates that the people and process dimensions of performance are less well developed than the information and technology aspects, and thus a focus on these aspects of business intelligence and analytics will be beneficial.

In our view, it’s important to consider business intelligence software in a broad business context rather than in artificially separate categories that are designed for IT only. We advise organizations seeking to gain a competitive edge to adopt a multifaceted strategy that is business-driven, incorporates a complete view of BI and analytics, and uses the comprehensive evaluation criteria we apply.

Regards,

Ventana Research

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

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

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

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

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

Regards,

Ventana Research

Actuate, a company known for powering BIRT, the open source business intelligence technology, has been delivering large-scale consumer and industrial applications for more than 20 years. In December the company announced it would be acquired by OpenText of Ontario, Canada. OpenText is Canada’s largest software vendor with more than 8,000 employees and a portfolio of enterprise information management products. It serves VR2014_Leadership_AwardWinnerprimarily large companies. The attraction of Actuate for such a company can be seen in a number of its legacy assets as well as more current acquisitions and developments but also its existing customer base. It was also awarded a 2014 Ventana Research Business Leadership Award.

Actuate’s foundational asset is BIRT (Business Intelligence and Reporting Tools) and its developer community. With more than 3.5 million developers and 13.5 million downloads, the BIRT developer environment is used in a variety of companies on a global basis. The BIRT community includes Java developers as well as sophisticated business intelligence design professionals, which I discussed in my outline of analytics personas. BIRT is a key project for the Eclipse Foundation, an open source integrated development environment familiar to many developers. BIRT provides a graphical interface to build reports at a granular level, and being Java-based, it provides ways to grapple with data and build data connections in a virtually limitless fashion. While new programming models and scripting languages, such as Python and Ruby, are gaining favor, Java remains a primary coding language for large-scale applications. One of the critical capabilities for business intelligence tools is to provide information in a visually compelling and easily usable format. BIRT can provide pixel-perfect reporting and granular adjustments to visualization objects. This benefit is coupled with the advantage of the open source approach: availability of skilled technical human resources on a global basis at relatively low cost.

Last year Actuate introduced iHub 3.1, a deployment server that integrates data from multiple sources and distributes content to end users. IHub has connectors to most database systems including modern approaches such as Hadoop. While Actuate provides the most common connectors out of the box, BIRT and the Java framework allow any data from any system to be brought into the fold. This type of approach to big data becomes particularly compelling for the ability to vr_Big_Data_Analytics_04_types_of_big_data_for_analyticsintegrate both large-scale data and diverse data sources. The challenge is that the work sometimes requires customization, but for large-scale enterprise applications, developers often do this to deliver capabilities that would not otherwise be accessible to end users. Our benchmark research into big data analytics shows that organizations need to access many data sources for analysis including transactional data (60%), external data (50%), content (49%) and event-centric data (48%).

In 2014, Actuate introduced iHub F-Type, which enables users to build reports, visualizations and applications and deploy them in the cloud. F-Type mitigates the need to build a separate deployment infrastructure and can act as both a “sandbox” for development and a broader production environment. Using REST-based interfaces, application developers can use F-Type to prototype and scale embedded reports for their custom applications. F-Type is delivered in the cloud, has full enterprise capabilities out of the box, and is free up to a metered output capacity of 50MB. The approach uses output metering rather than input metering used by some technology vendors. This output metering approach encourages scaling of data and focuses organizations on which specific reports they should deployed to their employees and customers.

Also in 2014, Actuate introduced BIRT Analytics 5.0, a self-service discovery platform that includes advanced analytic capabilities. In my review of BIRT Analytics, I noted its vr_predanalytics_benefits_of_predictive_analytics_updatedabilities to handle large data volumes and do intuitive predictive analytics. Organizations in our research said that predictive analytics provides advantages such as achieving competitive advantage (for 68%), new revenue opportunities (55%) and increased profitability (52%). Advances in BIRT Analytics 5.0 include integration with iHub 3.1 so developers can bring self-service discovery into their dashboards and public APIs for use in custom applications.

The combination of iHub, the F-Type freemium model, BIRT Analytics and the granular controls that BIRT provides to developers and users presents a coherent strategy especially in the context of embedded applications. Actuate CEO Pete Cittadini asserts that the company has the most APIs of any business intelligence vendor. The position is a good one especially since embedded technology is becoming important in the context of custom applications and in the so-called Internet-of-Things. The ability to make a call into another application instead of custom-coding the function itself within the workflow of an end-user application cuts developer time significantly. Furthermore, the robustness of the Actuate platform enables applications to scale almost without limit.

OpenText and Actuate have similarities, such as the maturity of the organizations and the types of large clients they vr_Info_Optimization_02_drivers_for_deploying_informationservice. It will be interesting to see how Actuate’s API strategy will impact the next generation of OpenText’s analytic applications and to what degree Actuate remains an independent business unit in marketing to customers. As a company that has been built through acquisitions, OpenText has a mature onboarding process that usually keeps the new business unit operating separately. OpenText CEO Mark Barrenechea outlines his perspective on the acquisition which will bolster its portfolio for information optimization and analytics or what it calls enterprise information management. In fact our benchmark research on information optimization finds that analytics is the top driver for deploying information in two thirds of organizations. The difference this time may be that today’s enterprises are asking for more integrated information which embeds analytics rather than having different interfaces for each of the applications or tools. The acquisition of Actuate by OpenText has now closed and now changes will occur to Actuate that should be watched closely to determine its path forward and it potential higher value for customers within OpenText.

Regards,

Ventana Research

In 2014, IBM announced Watson Analytics, which uses machine learning and natural language processing to unify and simplify the user experience in each step of the analytic processing: data acquisition, data preparation, analysis, dashboarding and storytelling.  After a relatively short beta testing period involving more than 22,000 users, IBM released Watson Analytics for general availability in December. There are two editions: the “freemium” trial version allows 500MB of data storage and access to file sizes less than 100,000 rows of data and 50 columns; the personal edition is a monthly subscription that enables larger files and more storage.

Its initial release includes functions to explore, predict and assemble data. Many of the features are based on IBM’s SPSS Analytic Catalyst, which I wrote about and which won the 2013 Ventana Research Technology Innovation Award for business analytics. Once data is uploaded, the explore function enables users to analyze data in an iterative fashion using natural language processing and simple point-and-click actions. Algorithms decide the best fit for graphics based on the data, but users may choose other graphics as needed. An “insight bar” shows other relevant data that may contain insights such as potential market opportunities.

The ability to explore data through visualizations with minimal knowledge is a primary aim of modern analytics tools. With the explore function incorporating natural language processing, which other tools in the market lack, IBM makes analytics accessible to users without the need to drag and drop dimensions and measures across the screen. This feature should not be underestimated; usability is the buying criterion for analytics tools most widely cited in our benchmark research on next-generation business intelligence (by 63% of organizations).

vr_ngbi_br_importance_of_bi_technology_considerations_updatedThe predict capability of Watson Analytics focuses on driver analysis, which is useful in a variety of circumstances such as sales win and loss, market lift analysis, operations and churn analysis. In its simplest form, a driver analysis aims to understand causes and effects among multiple variables. This is a complex process that most organizations leave to their resident statistician or outsource to a professional analyst. By examining the underlying data characteristics, the predict function can address data sets, including what may be considered big data, with an appropriate algorithm. The benefit for nontechnical users is that Watson Analytics makes the decision on selecting the algorithm and presents results in a relatively nontechnical manner such as spiral diagrams or tree diagrams. Having absorbed the top-level information, users can drill down into top key drivers. This ability enables users to see relative attribute influences and interactivity between attributes. Understanding interactivity is an important part of driver analysis since causal variables often move together (a challenge known as multicollinearity) and it is sometimes hard to distinguish what is actually causing a particular outcome. For instance, analysis may blame the customer service department for a product defect and point to it as the primary driver of customer defection. Accepting this result, a company may mistakenly try to fix customer service when a product issue needs to be addressed. This approach also overcomes the challenge of Simpson’s paradox, in which a trend that appears in different groups of data disappears or reverses when these groups are combined. This is a hindrance for some visualization tools in the market.

Once users have analyzed the data sufficiently and want to create and share their analysis, the assemble function enables them to bring together various dashboard visualizations in a single screen. Currently, Watson Analytics does such sharing (as well as comments related to the visualizations) via email. In the future, it would good to see capabilities such as annotation and cloud-based sharing in the product.

Full data preparation capabilities are not yet integrated into Watson Analytics. Currently, it includes a data quality report that gives confidence levels for the current data based on its cleanliness, and basic sort, transform and relabeling are incorporated as well. I assume that IBM has much more in the works here. For instance, its DataWorks cloud service offers APIs for some of the best data preparation and master data management available today. DataWorks can mask data at the source and do probabilistic matching against many sources including both cloud and on-premises addresses.  This is a major challenge organizations face when needing to conduct analytics across many data sets. For instance, in multichannel marketing, each individual customer may have many email addresses as well as different mailing addresses, phone numbers and identifiers for social media. A so-called “golden record” needs to be created so all such information can be linked together. Conceptually, the data becomes one long row of data related to that golden record, rather than multiple unassociated data in rows of shorter length. This data needs to be brought into a company’s own internal systems, and personally identifiable information must be stripped out before anything moves into a public domain. In a probabilistic matching system, data is matched not on one field but through associations of data which gives levels of certainty that records should be merged. This is different than past approaches and one of the reasons for significant innovation in the category. Multiple startups have been entering the data preparation space to address the need for a better user experience in data preparation. Such needs have been documented as one of the foundational issues facing the world of big data. Our benchmark research into information optimization shows that data preparation (47%) and quality and consistency (45%) are the most time-consuming tasks for organizations in analytics.

Watson Analytics is deployed on IBM’s SoftLayer cloud vr_Info_Optimization_04_basic_information_tasks_consume_timetechnology and is part of a push to move its analytic portfolio into the cloud. Early in 2015 the company plans to move its SPSS and Cognos products into the cloud via a managed service, thus offloading tasks such as setup, maintenance and disaster recovery management. Watson Analytics will be offered as a set of APIs much as the broader Watson cognitive computing platform has been. Last year, IBM said it would move almost all of its software portfolio to the cloud via its Bluemix service platform. These cloud efforts, coupled with the company’s substantial investment in partner programs with developers and universities around the world, suggest that Watson may power many next-generation cognitive computing applications, a market estimated to grow into the tens of billions of dollars in the next several years.

Overall, I expect Watson Analytics to gain more attention and adoption in 2015 and beyond. Its design philosophy and user experience are innovative, but work must be done in some areas to make it a tool that professionals use in their daily work. Given the resources IBM is putting into the product and the massive amounts of product feedback it is receiving, I expect initial release issues to be worked out quickly through the continuous release cycle. Once they are, Watson Analytics will raise the bar on self-service analytics.

Regards,

Ventana Research

It’s widely agreed that cloud computing is a major technology innovation. Many companies use cloud-based systems for specific business functions such as customer service, sales, marketing, finance and human resources. More generally, however, analytics and business intelligence (BI) have not migrated to the cloud as quickly. But now cloud-based data and analytics products are becoming more common. This trend is most popular among technology companies, small and midsize businesses, and departments in larger ones, but there are examples of large companies moving their entire BI environments to the cloud. Our research into big data analytics shows that more than one-fourth of analytics initiatives for companies of all sizes are cloud-based.

vr_bti_br_top_benefits_of_cloud_computingLike other cloud-based applications, cloud analytics offers enhanced scalability and flexibility, affordability and IT staff optimization. Our research shows that in general the top benefits are lowered costs (for 40%), improved efficiency (39%) and better communication and knowledge sharing (34%). Using the cloud, organizations can use a sophisticated IT infrastructure without having to dedicate staff to install and support it. There is no need for comprehensive development and testing because the provider is responsible for maintaining and upgrading the application and the infrastructure. The cloud can also provide flexible infrastructure resources to support “sandbox” testing environments for advanced analytics deployments. Multitenant cloud deployments are more affordable because costs are shared across many companies. When used departmentally, application costs need not be capitalized but instead can be made operational expenditures. Capabilities can be put to use quickly, as vendors develop them, and updates need not disrupt use. Finally, some cloud-based interfaces are more intuitive for end users since they have been designed with the user experience in mind. Regarding cloud technology, our business technology innovation research finds that usability is the most important technology evaluation criterion (for 64% of participants), followed by reliability (54%) and capability (%).

vr_bti_why_companies_dont_use_cloudFor analytics and BI specifically, there are still issues holding back adoption. Our research finds that a primary reason companies do not deploy cloud-based applications of any sort are security and compliance issues. For analytics and business intelligence, we can also include data related activities as another reason since cloud-based approaches often require data integration and transmission of sensitive data across an external network along with a range of data preparation. Such issues are especially prevalent for companies that have legacy BI tools using data models that have been distributed across their divisions. Often these organizations have defined their business logic and metrics calculations within the context of these tools. Furthermore, these tools may be integrated with other core applications such as forecasting and planning. To re-architect such data models and metrics calculations is a challenge some companies are reluctant to undertake.

In addition, despite widespread use of some types of cloud-based systems, for nontechnical business people discussions of business intelligence in the cloud can be confusing, especially when they involve information integration, the types of analytics to be performed and where the analytic processes will. The first generation of cloud applications focused on end-user processes related to the various lines of business and largely ignored the complexities inherent in information integration and analytics. Organizations can no longer ignore these complexities since doing so exacerbates the challenge of fragmented systems and distributed data. Buyers and architects should understand the benefits of analytics in the cloud and weigh these benefits against the challenges described above.

Our upcoming benchmark research into data and analytics in the cloud will examine the current maturity of this market as well opportunities and barriers to organizational adoption across line of business and IT. It will evaluate cloud-based analytics in the context of trends such as big data, mobile technology and social collaboration as well as location intelligence and predictive analytics. It will consider how cloud computing enables these and other applications and identify leading indicators for adoption of cloud-based analytics. It also will examine how cloud deployment enables large-scale and streaming applications. For example, it will examine real-time processing of vast amounts of data from sensors and other semistructured data (often referred to as the Internet of Things).

It is an exciting time to be studying this particular market as companies consider moving platforms to the cloud. I look forward to receiving any qualified feedback as we move forward to start this important benchmark research. Please get in touch if you have an interest in this area of our research.

Regards,

Ventana Research

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