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I’ve got a lot of stuff in my office. Your house is filled with a lot of things you might not need. And then there’s that lot# on your beverage can. But what is a lot and why does it matter? How can something as arbitrary as a “lot” have real importance in our everyday lives? In the English language the word “lot” is used for describing a large quantity of items or some grouping or set of items. In operations we define “lots” as groups of goods received or produced.

Even with the onset of Artificial Intelligence (AI)’s recent advancements and perhaps new or at least reiterations of all that it promises, some organizations continue to wait or “put off” any serious investigation of the technology.

In an earlier post (IBM Planning Analytics Data Modelling with Context) I stated that when modeling data as part of a planning analytics solution design, context clues should be developed, through a process referred to as profiling and then “built in” to the data.

Planning Analytics Data Modelling with Context

In the past, data to be modeled came from a single source and was provided in the same format, typically transactions from a general ledger system. In today’s data driven world, project data can come from a variety of places which, potentially, can influence the data’s possible meaning or value, effect how you model and use it and ultimately, whether it will provide insights the business can in fact leverage.

So, what is model serviceability?

Often you hear about performing an application design review on a IBM Planning Analytics model where both coding and implementation “styles” are compared against “industry proven” practices. During the process, naming conventions, dimensionalities, rule-vs-process strategies, (just to name a few items) are studied and assessed.

The IBM Watson Visual Recognition Service is one of the many services available on the IBM Cloud platform designed to accelerate and automate the AI Lifecycle by simplifying the most complicated, time-consuming steps within a VR project. 

From the time when SAP founders Klaus Tschira, Hasso Plattner, Dietmar Hopp, and Hans-Werner Hector first introduced SAP in 1972, SAP has arguably offered the best ERP available.  It has certainly contributed to changing and improving the way that the best performing companies are managed today.  Many businesses that use SAP could not effectively operate without it.  SAP has delivered countless benefits to companies, but these benefits have not have come without a significant price tag.  One thing that you can comfortably say about SAP is, nothing about SAP comes cheaply!  So with the news that SAP has set a December 31, 2025 deadline, after which its ERP system will be built to run on just one single database platform; SAP’s own HANA; should have many CIOs, CFOs and CEOs quaking in the boots at the potential cost of moving to the new system. SAP describes S/4HANA as the “biggest launch in 23 years, if not in the entire history of the company”. 

Every year Gartner publishes the “Magic Quadrant for Analytics and Business Intelligence Platforms” and when it’s released the first thing people look for is where each technology sits within the square.  I have been working in this space for many years with many different technologies and here at QueBIT we are successfully implementing Cognos Analytics for our customers and I would like to share our thoughts on a product we believe in.  While Gartner takes a narrower approach to modern BI, Cognos Analytics takes a broader approach.  Our customers are telling us they still need some managed reporting, self-service, and data exploration in one tool to reduce the number of point-solutions that are disconnected from their enterprise systems.  Cognos Analytics fulfills these needs plus a lot more. 

We are very excited about all the new features in the latest release of Cognos Analytics 11.1, we believe these new features below in addition to what already existed puts us in a great position to help our clients make smart decisions about their businesses. 

• Introduced the AI Assistant, which employs machine learning, natural language generation (NLG), and advanced pattern detection to convey hidden relationships in data through stunning visualizations, plus distilled observations in plain English
• Introduced new visual exploration capabilities, like features in Watson Analytics, that make it easy and intuitive for users, even those with no expertise in business analysis, to uncover compelling insights and hard-to-find answers
• Incorporated many other advanced analytics features that make it faster and easier to discover not only what is happening, but why it happened
• Made data preparation easier and faster than ever, freeing up users to spend more of their time doing actual analysis
• Streamlined our overall approach to the BI market by sun-setting Watson Analytics and infusing augmented intelligence throughout Cognos Analytics to make finding answers easier and faster than ever before for users throughout the organization