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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 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.


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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.


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