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In previous releases of Cognos Analytics, we have seen a trend of integrating many of the features of metadata modeling in Framework Manager into the Cognos Analytics interface. This trend is continuing with new or improved modeling capabilities being incorporated into Cognos Analytics 11.1 Data Modules.

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Customer segmentation is defined as “the process of dividing customers into groups based on common characteristics so companies can market to each group effectively and appropriately.” By using the correct attributes to define the customer segment, it allows companies to identify the right customers for targeted and relevant offers. Those who successfully define and maintain customer segmentation can derive a competitive advantage from the implementation by improving customer experience.

However, there are potential pitfalls that can reduce the effectiveness of a customer segmentation initiative. This article will identify the pitfalls and propose solutions in order to improve the chances of a beneficial customer segmentation project.

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Data democratization is the ability of an organization to provide information to end users in an easy and effective way. The goal is to provide self-service of information to end users with minimal IT support. There are many things that can go wrong when rolling out data democratization projects. The purpose of this article is to identify potential issues and provide guidance on how to avoid them in the democratization process.

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You’ve undoubtedly heard the term “Self-Service Analytics” thrown around, but what does self-service analytics actually look like in practice? What does a self-service user look like? And what prep work is needed to enable these people to serve themselves?

I spoke with Crystal Meyers, our resident Tableau guru and self-service analytics advocate to learn more. The following is a conversation with Crystal, where she explained some of the nuances of self-service analytics. Read more

At least weekly, I am granted the opportunity to meet and work alongside experienced professionals who serve in a corporate business intelligence (BI) leadership function. When they describe their role upon introduction, there is a common thread to the scope of influence and control which usually intersects one or more of these domains: Read more

One size does not fit all. Try as they might, there is not a single BI platform that can offer every capability that users require. With organizational complexity increasing, and the growing demand for self-service analytics, it has become commonplace, even recommended, for organizations to maintain multiple BI platforms to meet the needs of people in diverse roles with differing needs across the organization. Read more

You walk down the long hall, and tentatively knock on the side of a cubicle, just loud enough to be heard over the din of keyboard clicking. The occupant of the chair slowly spins around. “Yes?”

“So, um, I was wondering about that report I requested…”

“Yes I remember” comes a flat reply. “We’re still looking into it, I’ll let you know in a week, OK?”

The chair spins back around, keyboard sounds resume. Read more

In the world of data and analytics, technical debt is what happens when organizations make conscious decisions to solve short-term problems, even when they know there could be long-term and potentially negative implications around their actions. It exists in every organization, including yours. Not all technical debt is bad, as long as it is strategically planned and tactically paid off. Read more

In the last few weeks, I’ve spoken with three different clients who are struggling with the issue of end user adoption. Each described a routine set of activities, either completed or in process, that you would expect to see in a thoughtful change management program. Nonetheless, all three customers expressed frustration at the lack of user adoption.

Why is this? Do we need to rethink our traditional process for engaging users as we embark on our data and analytics journey?

In a word, yes. Read more