Introducing COGNOS ANALYTICS 11 Introducing COGNOS ANALYTICS 11.1 November 8th 2018 -- Michael Peter Offering Manager BA Ecosystem Growth and Strategy IBM Business Analytics

Factors shaping the future IBM Analytics © 2014 IBM Corporation Factors shaping the future Self Service for business users to create personalized content using corporate and personal data Ease of use to allow users to efficiently achieve their goals Governance for performance, security and scalability Operationalize user created assets

Data modules & ad hoc reports Static reports Interactive reports This slide describes how we see the user community today. There is still a large portion of the overall number who are consumers who will run daily reports and consume dashboards and stories. However, there is also a growing number of Data Explorers who want to be empowered to do more….perform their own data exploration and create ad-hoc dashboards and reports. Of course, there are also still that portion of the community who will be focused on the more technical aspects like professional reporting, data modeling, etc. Lastly there is a small but growing Data Scientist community that is starting to evolve. Personalized reports Data Consumers 60% Dashboards & Stories Casual Users Power Users Data Explorers 30% Data modules & ad hoc reports Data Analysts 8% Data model & professional reports Data Scientists 2% Source Eckerson Group 2017

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Six Key Investment Areas 1 2 3 Learning (machine learning and deep learning) is under the covers /underpinning most of our areas of investment. For example - smarts to understand user behavior and preferences in how they like to do analytics – eg. Generating visualizations, stories, and dashboards - automatically guide the user. Data prep, modeling and content creation can also make use of machine learning. Six key area of investment that we are pursuing to enable it are: Smarts/Learning – eg. more immersive user experiences, automatic insights, less biased discovery, more advanced analytics techniques, invest in context and learning, NLP/NLG enhancements, understanding domains and recommending data sources Ease of Use – We are continuing to put a lot of focus on ease of use. The experience we are building will be a guided process that will help users create content, explore and visualize data and be able to easily identify relevant data and insights that perhaps weren’t obvious at the start. Data Exploration - We are continuing to build out our data exploration capabilities. In the coming slides, you’ll see a sneak peak of a new exploration interface that will incorporate some of the algorithms and smarts that are currently available in Watson Analytics and that will really provide a best in class data exploration and discovery experience. Data prep and modeling – Many of our users still spend too much time on the modelling and data preparation phase. Our goal is to make this step as intuitive and as “smart” as possible so that users can spend less time modeling and preparing their data and quickly get into the data exploration and content creation phase, which is really what they want to be doing. Collaboration – eg. work with 3rd party tools, annotate options, workflow, automatically bring insights based on context Ecosystem – integration of IBM products, theming/branding of the product and content, embedability, extensibility, digital, storybooks and business workbooks, lead with offering (domain smarts… Smarts Ease of Use Data Exploration 4 6 5 Data Prep & Modelling Collaboration Ecosystem Learning CONFIDENTIAL

Personalized analytics that learns and adapts to the individual user User-driven narrative with natural language Remove technical barriers Remove bias Automate analysis (work faster) Contextual recommendations Smarts

A flexible workspace where users can explore their data, or explore an existing asset in a Dashboard or Report Low barrier to entry: make it easy for any user to get started exploring Surface advanced analytics insights in a subtle way, so as not to overwhelm the user Provide contextual recommendations Exploration

Ease of Use / Dashboards Users have flexibility to quickly assemble very attractive dashboards. Users can build from existing assets without having to start from scratch. Meaningful visualizations are automatically generated with little authoring experience needed. Customized look and feel can be achieved and reused. Corporate standards can also be easily followed. Highly interactive and performant visualizations Ease of Use / Dashboards

Users can quickly assemble a report using existing assets without having to start from scratch. Meaningful visualizations are automatically generated with little authoring experience needed. Customized look and feel can be achieved and reused. Corporate standards can also be easily followed. Highly interactive and performant visualizations Ease of Use / Reporting

Data modules Ease of use yet powerful when needed Graduated Experience – business analysts to experienced modelers Files to insights experience Data Prep in the experience Data blending & Set operations Relative Dates Multi-grain Analysis Security by Value Data modules

5 Collaboration IBM Analytics © 2014 IBM Corporation Integrate with third party collaboration platforms (ex. Slack, Microsoft Teams, etc.) Share insights in context Messaging & discussion Share assets (dashboard, report, story or individual visualization) as an image Follow assets and other people Tailor notifications to personal needs Leverage smarts to suggest new content and users to follow Collaboration is another area where we will be investing. In many cases, there are multiple people or a team working together on a business problem or project. Instead of trying to build a new messaging or chat platform, we envision integrating with existing platforms that customers are already using in their organizations. You can see on the screen here a chat and messaging panel where users would be able to drag and drop into the chat to share content. We see allowing users to communicate in real time to share ideas and feedback on content they are creating as well as allowing them to annotate content to support teams that are spread across time zones or for historical tracking purposes. IBM Analytics © 2014 IBM Corporation CONFIDENTIAL

Building Community | Always-on IBM Big Data & Analytics © 2013 IBM Corporation Building Community | Always-on N New! Launch of new online Community and embedded link in product Collaborative effort across IBM functions Access how to videos, product information, contact support etc. Interact with other users Consistent experience across Watson Analytics, Planning Analytics, Cognos Analytics Product specific Groups How-to Videos Expert Blogs Discussion Forums Self-service Support Event Calendar https://www.ibm.com/communities/analytics/