Planning Analytics Reporting Cubes

Creating a dedicated reporting cube, you remove the potential for data changing as it is a static load versus a cube that users can input data into and data can change throughout the day. This is accomplished using TurboIntegrator (TI) processes to move data between cubes. A reporting cube can combine data from multiple Planning Analytics cubes into a single cube.

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Creating Custom Data Visualizations in Cognos 11.1.5

The ability to add your own custom visualizations came with Cognos 11.1.4 and has only improved in the 11.1.5 release. Being able to create or adapt an existing chart created with any JavaScript library means we're no longer tied to what IBM provides. If you see a chart in another tool that you like, then I'm sure there's a way to create it in Cognos.

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Why the Cloud Was Made for Data & Analytics

The benefits of cloud computing are widely accepted these days, but we’re still in the early stages of organizations actually moving systems to the cloud. While all sorts of workloads can benefit from cloud computing, your data and analytics systems are a great place to start—or to look at next if you’re already on the cloud journey.

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The Power of Data Module Column Dependencies

When Cognos 11.1 was released, it introduced enough new Data Modules features that a realistic case could be made to consider moving from Framework Manager packages to data modules for metadata modeling. One of the essential pieces that were missing from earlier releases was the ability to deal with multi-grain issues (as addressed by determinants in Framework Manager). To address this issue, Cognos has recently added something called column dependencies.

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5 Ways Your AI Projects Fail, Part 5

The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies at the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.

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5 Ways Your AI Projects Fail, Part 4

The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies at the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.

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Creating Custom Calendars in Cognos Analytics 11.1s

One of my favorite features of Data Modules since version 11.1 is the ability to easily do relative date analysis. What used to take a bunch of coding in Framework Manager or several queries within a report, you can now do by pointing your measure to a calendar in a Data Module and automatically have several relative time data items and filters available.

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5 Ways Your AI Projects Fail, Part 2

The recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies in the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.

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Introducing Incorta: A Game-Changer for Data Prep

I am constantly evaluating new technologies and approaches to solving age old data problems. It’s given me a certain perspective on what truly drives change (hint: two parts culture for every one part technology) and a healthy dose of skepticism about silver bullet solutions, particularly to longstanding challenges around data management.

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