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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

The emerging Internet of Things (IoT) extends digital connectivity to devices and sensors in homes, businesses, vehicles and potentially almost anywhere. This innovation enables devices designed for it to generate and transmit data about their operations; analytics using this data can facilitate monitoring and a range of automatic functions.vr_oi_goals_of_using_operational_intelligence_updated

To perform these functions IoT requires what Ventana Research calls Operational Intelligence (OI), a discipline that has evolved from the capture and analysis of instrumentation, networking and machine-to-machine interactions of many types. We define operational intelligence as a set of event-centered information and analytic processes operating across an organization that enable people to use that event information to take effective actions and make optimal decisions. Our benchmark research into Operational Intelligence shows that organizations most often want to use such event-centric architectures for defining metrics (37%) and assigning thresholds for alerts (35%) and for more action-oriented processes of sending notifications to users (33%) and linking events to activities (27%).

In many industries, organizations can gain competitive advantage if they can reduce the elapsed time between an event occurring and actions taken or decisions made in response to it. Existing business intelligence (BI) tools provide useful analysis of and reporting on data drawn from previously recorded transactions, but to improve competitiveness and maximize efficiencies organizations are concluding that employees and processes – in IT, business operations and front-line customer sales, service and support – also need to be able to detect and respond to events as they happen. Our research into big data integration shows that nearly one in four companies currently integrate data into big data stores in real time. The challenge is to go further and act upon both the data that is stored and the data that is streaming in a timely manner.

The evolution of operational intelligence, especially in conjunction with IoT, is encouraging companies to revisit their priorities and spending for information technology and application management. However, sorting out the range of options poses a challenge for both business and IT leaders. Some see potential value in expanding their network infrastructure to support OI. Others are implementing event processing (EP) systems that employ new technology to detect meaningful patterns, anomalies and relationships among events. Increasingly, organizations are using dashboards, visualization and modeling to notify nontechnical people of events and enable them to understand their significance and take appropriate and immediate action.

As with any innovation, using OI for IoT may require substantial changes. These are among the challenges organizations face as they consider adopting operational intelligence:

  • They find it difficult to evaluate the business value of enabling real-time sensing of data and event streams using identification tags, agents and other systems embedded not only in physical locations like warehouses but also in business processes, networks, mobile devices, data appliances and other technologies.
  • They lack an IT architecture that can support and integrate these systems as the volume and frequency of information increase.
  • They are uncertain how to set reasonable business and IT expectations, priorities and implementation plans for important technologies that may conflict or overlap. These can include business intelligence, event processing, business process management, rules management, network upgrades and new or modified applications and databases.
  • They don’t understand how to create a personalized user experience that enables nontechnical employees in different roles to monitor data or event streams, identify significant changes, quickly understand the correlation between events and develop a context in which to determine the right decisions or actions to take.

Ventana Research has announced new benchmark research on The Internet of Things and Operational Intelligence that will identify trends and best practices associated with this technology and these processes. It will explore organizations’ experiences with initiatives related to events and data and with attempts to align IT projects, resources and spending with new business objectives that demand real-time intelligence and event-driven architectures. The research will investigate how organizations are increasing their responsiveness to events by rebalancing the roles of networks, applications and databases to reduce latency; it also will explore ways in which they are using sensor data and alerts to anticipate problematic events. We will benchmark the performance of organizations’ implementations, including IoT, event stream processing, event and activity monitoring, alerting, event modeling and workflow, and process and rules management.

As operational intelligence evolves as the core of IoT platforms, it is an important time to take a closer look at this emerging opportunity and challenge. For those interested in learning more or becoming involved in this upcoming research, please let me know.

Regards,

Ventana Research

As I discussed in the state of data and analytics in the cloud recently, usability is a top evaluation criterion for organizations in selecting cloud-based analytics software. Data access of cloud and on-premises systems are essential antecedents of usability. They can help business people perform analytic tasks themselves without having to rely on IT. Some tools allow data integration by business users on an ad hoc basis, but to provide an enterprise integration process and a governed information platform, IT involvement is often necessary. Once that is done, though, using cloud-based data for analytics can help, empowering business users and improving communication and process .

vr_DAC_16_dealing_with_multiple_data_sourcesTo be able to make the best decisions, organizations need access to multiple integrated data sources. The research finds that the most common data sources are predictable: business applications (51%), business intelligence applications (51%), data warehouses or operational data stores (50%), relational databases (41%) and flat files (33%). Increasingly, though, organizations also are including less structured sources such as semistructured documents (33%), social media (27%) and nonrelational database systems (19%). In addition there are important external data sources, including business applications (for 61%), social media data (48%), Internet information (42%), government sources (33%) and market data (29%). Whether stored in the cloud or locally, data must be normalized and combined into a single data set so that analytics can be performed.

Given the distributed nature of data sources as well as the diversity of data types, information platforms and integration approaches are changing. While more than three in five companies (61%) still do integration primarily between on-premises systems, significant percentages are now doing integration from the cloud to on-premises (47%) and from on-premises to the cloud (39%). In the future, this trend will become more pronounced. According to our research, 85 percent of companies eventually will integrate cloud data with on-premises sources, and 84 percent will do the reverse. We expect that hybrid architectures, a mix of on-premises and cloud data infrastructures, will prevail in enterprise information architectures for years to come while slowly evolving to equality of bidirectional data transfer between the two types.

Further analysis shows that a focus on integrating data for cloud analytics can give organizations competitive advantage. Those who said it is very important to integrate data for cloud-based analytics (42% of participants) also said they are very confident in their ability to use the cloud for analytics (35%); that’s three times more often than those who said integrating data is important (10%) or somewhat important (9%). Those saying that integration is very important also said more often that cloud-based analytics helps their customers, partners and employees in an array of ways, including improved presentation of data and analytics (62% vs. 43% of those who said integration is important or somewhat important), gaining access to many different data sources (57% vs. 49%) and improved data quality and data management (59% vs. 53%). These numbers indicate that organizations that neglect the integration aspects of cloud analytics are likely to be at a disadvantage compared to their peers that make it a priority.

Integration for cloud analytics is typically a manual task. In particular, almost half (49%) of organizations in the research use spreadsheets to manage the integration and preparation of cloud-based data. Yet doing so poses serious challenges: 58 percent of those using spreadsheets said it hampers their ability to manage processes efficiently. While traditional methods may suffice for integrating relatively small and well-defined data sets in an on-premises environment, they have limits when dealing with the scale and complexity of cloud-based data. vr_DAC_02_satisfaction_with_data_integration_toolsThe research also finds that organizations utilizing newer integration tools are satisfied with them more often than those using older tools. More than three-fourths (78%) of those using tools provided by a cloud applications  provider said they are satisfied or somewhat satisfied with them, as are even more (86%) of those using data integration tools designed for cloud computing; by comparison, fewer of those using spreadsheets (56%) or traditional enterprise data integration tools (71%) are satisfied.

This is not surprising. Modern cloud connectors are designed to connect via loosely coupled interfaces that allow cloud systems to share data in a flexible manner. The research thus suggests that for organizations needing to integrate data from cloud-based data sources, switching to modern integration tools can streamline the process.

Overall three-quarters of companies in our research said that it is important or very important to access data from cloud-based sources for analysis. Cloud-based analytics isn’t useful unless the right data can be fed into the analytic process. But without capable tools this is not easy to do. A substantial impediment is that analysts spend the majority of their time in accessing and preparing the data rather than in actual analysis. Complicating the task, each data source can represent a different, possibly complex, data model. Furthermore, the data sets may have varying data formats and interface requirements, which are not easily addressed with legacy integration tools.

Such complexity is the new reality, and new tools and approaches have come to market to address these complexities. For organizations looking to integrate their data for cloud-based analytics, we recommend exploring these new integration processes and technologies.

Regards,

Ventana Research

Our recently completed benchmark research on data and analytics in the cloud shows that analytics deployed in cloud-based systems is gaining widespread adoption. Almost half (48%) of vr_DAC_04_widespread_use_of_cloud_based_analyticsparticipating organizations are using cloud-based analytics, another 19 percent said they plan to begin using it within 12 months, and 31 percent said they will begin to use cloud-based analytics but do not know when. Participants in various areas of the organization said they use cloud-based analytics, but front-office functions such as marketing and sales rated it important more often than did finance, accounting and human resources. This front-office focus is underscored by the finding that the categories of information for which cloud-based analytics is most often deemed important are forecasting (mentioned by 51%) and customer-related (47%) and sales-related (33%) information.

The research also shows that while adoption is high, organizations face challenges as they seek to realize full value from their cloud-based data and analytics initiatives. Our Performance Index analysis reveals that only one in seven organizations reach the highest Innovative level of the four levels of performance in their use of cloud-based analytics. Of the four dimensions we use to further analyze performance, organizations do better in Technology and Process than in Information and People. That is, the tools and analytic processes used for data and analytics in the cloud have advanced more rapidly than users’ abilities to work with their information. The weaker performance in People and Information is reflected in findings on the most common barriers to deployment of cloud-based analytics: lack of confidence about the security of data and analytics, mentioned by 56 percent of organizations, and not enough skills to use cloud-based analytics (42%).

Given the top barrier of perceived data security issues, it is not surprising the research finds that the largest percentage of organizations (66%) use a private cloud, which by its nature ostensibly is more secure, to deploy analytics; fewer use a public cloud (38%) or a hybrid cloud (30%), although many use more than one type today. We know from tracking analytics and business intelligence software providers that operate in the public cloud that this is changing quite rapidly. Comparing vr_DAC_06_how_to_deploy_cloud_based_analyticsdeployment by industry sector, the research analysis shows that private and hybrid clouds are more prevalent in the regulated areas of finance, insurance and real estate and government than in services and manufacturing. The research suggests that private and hybrid cloud deployments are used more often for analytics where data privacy is a concern.

Furthermore, organizations said that access to data for analytics is easier with private and hybrid clouds (29% for public cloud vs. 58% for private cloud and 67% for hybrid cloud). In addition, organizations using private and hybrid cloud more often said they have improved communication and information sharing (56% public vs. 72% private and 70% hybrid). Thus, the research data makes clear that organizations feel more comfortable implementing analytics in a private or hybrid cloud in many areas.

Private and hybrid cloud implementations of data and analytics often coincide with large data integration efforts, which are necessary at some point to benefit from such deployments. Those who said that integration is very important also said more often than those giving it less importance that cloud-based analytics helps their customers, partners and employees in an array of ways, including improved presentation of data and analytics (62% vs. 43% of those who said integration is important or somewhat important), gaining access to many different data sources (57% vs. 49%) and improved data quality and data management (59% vs. 53%). We note that the focus on data integration efforts correlates more with private and hybrid cloud approaches than with public cloud approaches, thus the benefits cannot be directly assigned to the various cloud approaches nor the integration efforts.

Another key insight from the research is that data and analytics often are considered in conjunction with mobile and collaboration initiatives which have different priorities for business than IT or in consumer markets. Nine out of 10 organizations said they use or intend to use collaboration technology to support their cloud-based data and analytics, and 83 percent said they need to support data access and analytics on mobile devices. Two-thirds said they support both tablets and smartphones and multiple mobile operating systems, the most important of which are Apple iOS (ranked first by 60%), Google Android (ranked first by 26%) and Microsoft Windows Mobile (ranked first by 13%). We note that Microsoft has a higher percentage of importance here than its reported market share (approximately 2.5%) would suggest. Similarly, Google Android has greater penetration than Apple in the consumer market (51% vs. 41%). We expect that the influence of mobile operating systems related to data and analytics in the cloud will continue to evolve and be impacted by upcoming corporate technology refreshment cycles, the consolidation of PCs and mobile devices, and the “bring your own device” (BYOD) trend.

The research finds that usability (63%) and reliability (57%) arevr_DAC_20_evaluation_criteria_for_cloud_based_analytics the top technology buying criteria, which is consistent with our business technology innovation research conducted last year. What has changed is that manageability is cited as very important as often as functionality, by approximately half of respondents, a stronger showing than in our previous research.  We think it likely that manageability is gaining prominence as cloud providers and organizations sort out issues in who manages deployments along with usage and licensing, along with who actually owns your data in the cloud which my colleague Robert Kugel has discussed.

As the research shows, the importance of cloud data and analytics is continuing to grow. The importance of this topic makes me eager to discuss further the attitudes, re­quire­­ments and future plans of organizations that use data and analytics in the cloud and to identify the best prac­tices of those that are most proficient in it. For more information on this topic, and learn more on best practices for data and analytics in the cloud, and download the executive summary of the report to improve your readiness.

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

Tibco’s recent acquisition of Jaspersoft helps the company fill out its portfolio of business intelligence (BI) and reporting software in an increasingly competitive marketplace. Tibco already offered a range of products in BI and analytics including Tibco Spotfire, an established product for visual data discovery. Jaspersoft and its open source Java reporting tool JasperReports have been around since 2001, and the company says it has 16 million product downloads worldwide, 140,000 production deployments and 2,000 commercial customers in 100 countries. Jaspersoft received attention recently for its partnership with Amazon Marketplace and the ability to embed its system into applications using a credit card and a few simple configuration steps. This example of embedding the technology is an area that Tibco knows well from its history of integrating its technology into enterprise architecture across the planet.

vr_Info_Optimization_08_most_important_analyst_capabilities_updatedThe acquisition is significant given today’s advancements in the Business Intelligence market and the need for tools to serve a variety of users. In some ways their technologies serve the same users – analysts and business users trying to make decisions with data but how they approach it and support a broad set of roles and responsibilities is different.  Tibco Spotfire, Tibco’s approach to business analytics, serves for analytics and visualization with specializing in visual discovery and data exploration while Jaspersoft addresses the query and analyze, reporting, dashboards and other aspects of BI. According to our benchmark research on information optimization, the capabilities business users most often need are to drill into information within applications (37%), search for data (36%) and collaborate (27%). For analysts, the most necessary capabilities are extracting data (39%), designing and integrating metrics (37%) and developing policies and rules for access (34%). With Jaspersoft, Tibco can address both groups and also can embed intelligence and reporting capabilities into operationally oriented environments across range of business applications.

vr_oi_challenges_using_bi_for_operational_intelligenceThe acquisition makes sense in that more capabilities are needed to address the expanding scope of business intelligence and analytics. In practice, it will be interesting to see how the open source community and culture of Jaspersoft meshes with the culture of Tibco’s Spotfire division. For now, Jaspersoft will continue as a separate division so business likely will continue as usual until management decides specific areas of integration. With respect to development efforts, it will be critical to blend the discovery capabilities of Tibco Spotfire with Jaspersoft’s reporting which will be a formidable challenge.  Another key to success will be how Tibco integrates both with the capabilities from Extended Results, a mobile business intelligence provider Tibco bought in 2013. Mobility is an area where Ventana Research found Jaspersoft significantly lacking, so the Extended Results capabilities should prove useful. Finally, Tibco’s event-enabled infrastructure will likely play a key role as the company continues to invest in operational intelligence for event-focused information gathering and delivery. Our operational intelligence research has found a lack of integration from business intelligence like that of Jaspersoft with event streams like from Tibco to be a major challenge in over half (51%) of organizations. This is a potential opportunity for Tibco as it looks at future integration of the technologies.

The Jaspersoft acquisition is not surprising given recent changes in the BI market. The category, which just a few years ago was vr_Info_Optimization_01_whos_responsible_for_information_availabilityconsidered mature and well-defined, is expanding to include areas such as analytic discovery tools, advanced analytics and big data. The union of Tibco Spotfire, which primarily targets line-of-business professionals from analysts to knowledge worksers, and Jaspersoft, a more IT-centered company, reflects the need for the industry to bridge a divide that exists in many organizations where IT is publishing dashboards and reports to business.  The challenge of using information across business and IT was found in our latest research, revealed in our information optimization benchmark research, shows that information management these days is most often (in 42% of organizations) a joint responsibility of IT and the lines of business , although IT is involved in some capacity in four-fifths of them. It remains to be seen whether the joint company can take on major competitors that have far more cash resources and take a similar approach.

Preliminary indicators show a good fit between these two organizations. Customers from each will be introduced to important new tools and capabilities from the other. One of the first likely moves for Tibco will be to introduce the 2,000 commercial customers and global presence of Jaspersoft to the broader portfolio. We advise those customers to evaluate what Tibco offers, especially those from Tibco Spotfire which continues to be a leader in the visual data discovery market. Before investing, however, customers and prospects should demand clarity on the company’s plans for technical integration of analytics and how these will fit with organizations long-term business intelligence and analytics roadmaps. Tibco customers migrating to the cloud should investigate the work Jaspersoft is doing with companies like Amazon and consider whether the embedded approach to interactive reporting can fit with their analytics, cloud and application strategies.

The opportunity for Tibco to advance business analytics is significant through this acquisition but it has historically not been as progressive in its marketing and sales of analytics compared to others in the market. The demand for visual discovery and big data analytics has grown dramatically with over three quarters of organizations according to our research has shown as overall important. Big data analytics and visualization is an area that Spotfire had innovated before Tibco acquisition but has not seen its fair share of growth with the buying trends. The opportunity for Tibco to provide analytics and BI that can further leverage the entire Tibco portfolio of integration, event processing, cloud and social collaboration software products is upon them, let’s see how they do. It now needs to supercharge its analytics efforts significantly with leveraging its new products from Jaspersoft.

Regards,

Tony Cosentino

VP and Research Director

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