IBM Cognos Analytics With Watson

Post: What Does Watson Do?


IBM Cognos Analytics has been tattooed with the Watson name in version 11.2.1.  His full name is now IBM Cognos Analytics with Watson 11.2.1, formerly known as IBM Cognos Analytics.  But where exactly is this Watson and what does it do?    


In short, Watson brings AI-infused self-service capabilities.  Your new “Clippy”, actually AI Assistant, offers guidance in data preparation, analysis, and report creation.  Watson Moments chimes in when it thinks it has something useful to contribute about its analysis of the data.  Cognos Analytics with Watson offers a guided experience that interprets an organization’s intent and supports them with a suggested path, leading to improved decision-making.


Meet the new Watson

Watson, the fictional doctor invented by Dr. Arthur Connan Doyle, played a foil to detective Sherlock Holmes.  Watson, who was educated and intelligent, often observed the obvious and asked questions about seeming inconsistencies.  His powers of deduction, however, were no match for those of Holmes.


That’s not the Watson we’re talking about.  Watson is also IBM’s AI (artificial intelligence) project named after its founder.  Watson was introduced to the world in 2011 as a Jeopardy contestant.  So, at its roots, Watson is a computer system which can be queried and responds with natural language.  Since that time, the Watson label has been applied by IBM to a number of different initiatives related to machine learning and what it calls AI.  


IBM asserts, “IBM Watson is AI for business.  Watson helps organizations predict future outcomes, automate complex processes, and optimize employees’ time.”  Strictly speaking, Artificial Intelligence is a computer system which can mimic human thinking or cognition.  Most of what passes for AI today is actually problem solving, Natural Language Processing (NLP) or Machine Learning (ML).    


IBM has a number of different software applications infused with Watson’s ability for Natural Language Processing, searching and decision-making.  This is Watson as a chatbot using NLP.  This is one area in which Watson excels.  IBM Cognos Analytics With Watson Chatbot


What was once known as Cognos BI, is now branded IBM Cognos Analytics with Watson 11.2.1, formerly known as IBM Cognos Analytics.    


IBM Cognos Analytics With Watson At A Glance


As a summary of the unwieldy named ICAW11.2.1FKAICA, 

Cognos Analytics with Watson is a business intelligence solution that empowers users with AI-infused self-service capabilities.  It accelerates data preparation, analysis, and report creation. Cognos Analytics with Watson makes it easier to visualize data and share actionable insights across your organization to foster more data-driven decisions. Its capabilities enable users to reduce or eliminate IT intervention for many previous tasks, providing more self-service options, advancing the analytic expertise of the enterprise, and enabling organizations to capture insights more efficiently.


Cognos Analytics with Watson offers a guided experience that interprets an organization’s intent and supports them with a suggested path, leading to improved decision-making. Additionally, Cognos Analytics with Watson can be deployed on-premises, in the cloud, or both.

Where’s Watson?


What are these “AI-infused self-service capabilities?”  What is the Watson part?  The Watson part is the “guided experience,” “[interpreting] an organization’s intent,” and providing a “suggested path.”  This is the start of AI — synthesizing data and making recommendations. 


What is Watson and what is not?  Where does Watson start and the product formerly known as IBM Cognos Analytics end?  To be honest, it’s hard to tell.  Cognos Analytics is “infused” with Watson.  It’s not a bolt-on or a new menu item.  There’s not a Watson button.  IBM is saying that Cognos Analytics, now that it’s branded as Watson-powered, benefits from design philosophy and organizational learning that other business units within IBM have been evolving.


That being said, Watson Studio — a separate licensed product — is integrated, so that, once configured, you now can embed notebooks from Watson Studio into reports and dashboards.  This allows you to leverage the power of ML, SPSS Modeler, and AutoAI for advanced analytics and data science.


In Cognos Analytics with Watson, you will find Watson influence in the AI Assistant that allows you to ask questions and discover insights in natural language.  The AI Assistant uses NLM to parse sentences, including grammar, punctuation and spelling. IBM Watson Insights I’ve found that, like Amazon’s Alexa and Apple’s Siri, it is necessary to compose or sometimes rephrase your question to include appropriate context.  Some of the actions the Assistant can assist you with include:

  • Suggest questions – provides a list of questions via Natural Language Query that you can ask
  • View data sources – shows data sources that you have access to
  • Show data source (column) details
  • Show column influencers – displays fields that influence the result of the initial column
  • Create a chart or visualization – recommends an appropriate chart or visualization to bests represent two columns, for example
  • Create a dashboard – given a data source, does just that
  • Annotates dashboards via Natural Language Generation


Yes, some of this was available in Cognos Analytics 11.1.0, but it is more advanced in 11.2.0.  


Watson is also used behind the scenes in “Learning Resources” on the Cognos Analytics 11.2.1 home page which assists searching for assets in IBM and the broader community. 


In the 11.2.0 release, “Watson Moments” made its debut.  Watson Moments are new discoveries in the data that Watson “thinks” you might be interested in.  In other words, while you’re building a dashboard using the Assistant, it may detect that there is a related field to the one you asked about.  It may then offer a relevant visualization comparing the two fields.   This does appear to be an early implementation and it sounds like there is going to be more development in this area in the near future.


We also see Watson in the AI-assisted data modules with intelligent data preparation features.  Watson helps with the important first step of data cleaning.   Algorithms help you discover related tables and which tables can be joined automatically.  


IBM says that the reason why we see Watson in the title of the software as well as features is that “the IBM Watson branding helps resonate how something significant has been automated by the AI.”


Cognos Analytics with Watson is borrowing from research teams and IBM Watson Services — concepts, if not code.  IBM introduces Watson cognitive computing in 7 volumes with the Building Cognitive Applications with IBM Watson Services Redbooks series.  Volume 1: Getting Started provides an excellent introduction to Watson and cognitive computing.  The first volume provides a very readable introduction to the history, basic concepts and characteristics of cognitive computing.

What’s Watson?


To understand what Watson is, it is useful to look at the characteristics that IBM ascribes to AI and cognitive systems. Humans and cognitive systems

  1. Extend human capabilities.  Humans are good at thinking deeply and solving complex problems; computers are better at reading, synthesizing, and processing huge amounts of data. 
  2. Natural interaction.  Thus, the focus on recognition and processing of natural language,
  3. Machine learning.  WIth additional data, predictions, decisions or recommendations will be improved.
  4. Adapt over time.  Similar to ML above, adapting represents improving recommendations based on the feedback loop of interactions.


In talking about Artificial Intelligence, it’s hard not to anthropomorphize the technology.  It is the intention to develop cognitive systems that have the capability to understand, reason, learn and interact.  This is IBM’s stated direction.  Expect IBM to bring more of these capabilities to Cognos Analytics now that it wears the Watson brand.

Not so elementary


We started this article talking about deductive reasoning.  Deductive reasoning is “if-this-then-that” logic which has no uncertainty.   “Inductive reasoning, however, allows Sherlock [Holmes] to extrapolate from the information observed in order to arrive at conclusions about events that have not been observed…His extensive catalogue of facts to help him make leaps with his inductive reasoning that others might not be able to conceive of.”


Considering IBM Watson’s skill at inferences and wealth of reference material, I think “Sherlock” might have been a more appropriate name.

Scroll to Top
As the BI space evolves, organizations must take into account the bottom line of amassing analytics assets.
The more assets you have, the greater the cost to your business. There are the hard costs of keeping redundant assets, i.e., cloud or server capacity. Accumulating multiple versions of the same visualization not only takes up space, but BI vendors are moving to capacity pricing. Companies now pay more if you have more dashboards, apps, and reports. Earlier, we spoke about dependencies. Keeping redundant assets increases the number of dependencies and therefore the complexity. This comes with a price tag.
The implications of asset failures differ, and the business’s repercussions can be minimal or drastic.
Different industries have distinct regulatory requirements to meet. The impact may be minimal if a report for an end-of-year close has a mislabeled column that the sales or marketing department uses, On the other hand, if a healthcare or financial report does not meet the needs of a HIPPA or SOX compliance report, the company and its C-level suite may face severe penalties and reputational damage. Another example is a report that is shared externally. During an update of the report specs, the low-level security was incorrectly applied, which caused people to have access to personal information.
The complexity of assets influences their likelihood of encountering issues.
The last thing a business wants is for a report or app to fail at a crucial moment. If you know the report is complex and has a lot of dependencies, then the probability of failure caused by IT changes is high. That means a change request should be taken into account. Dependency graphs become important. If it is a straightforward sales report that tells notes by salesperson by account, any changes made do not have the same impact on the report, even if it fails. BI operations should treat these reports differently during change.
Not all reports and dashboards fail the same; some reports may lag, definitions might change, or data accuracy and relevance could wane. Understanding these variations aids in better risk anticipation.

Marketing uses several reports for its campaigns – standard analytic assets often delivered through marketing tools. Finance has very complex reports converted from Excel to BI tools while incorporating different consolidation rules. The marketing reports have a different failure mode than the financial reports. They, therefore, need to be managed differently.

It’s time for the company’s monthly business review. The marketing department proceeds to report on leads acquired per salesperson. Unfortunately, half the team has left the organization, and the data fails to load accurately. While this is an inconvenience for the marketing group, it isn’t detrimental to the business. However, a failure in financial reporting for a human resource consulting firm with 1000s contractors that contains critical and complex calculations about sickness, fees, hours, etc, has major implications and needs to be managed differently.

Acknowledging that assets transition through distinct phases allows for effective management decisions at each stage. As new visualizations are released, the information leads to broad use and adoption.
Think back to the start of the pandemic. COVID dashboards were quickly put together and released to the business, showing pertinent information: how the virus spreads, demographics affected the business and risks, etc. At the time, it was relevant and served its purpose. As we moved past the pandemic, COVID-specific information became obsolete, and reporting is integrated into regular HR reporting.
Reports and dashboards are crafted to deliver valuable insights for stakeholders. Over time, though, the worth of assets changes.
When a company opens its first store in a certain area, there are many elements it needs to understand – other stores in the area, traffic patterns, pricing of products, what products to sell, etc. Once the store is operational for some time, specifics are not as important, and it can adopt the standard reporting. The tailor-made analytic assets become irrelevant and no longer add value to the store manager.