Tag Archive for: Cognos Analytics

In the first installment of this series, we discussed the origins of Mode 2 Analytics, and in the second installment we focused on how to enable this capability in your organization using Cognos. Now that we’ve learned all about how Mode 2 works, let’s walk through a sample use case that highlights the Bimodal Analytics Lifecycle as well as the technical capabilities of Cognos Analytics and how they fit together.

In this example, you are the manager of a Healthcare Call Center system that is comprised of seven (7) regional centers across the country. Each call center handles contact (phone calls and online chats) from the customers that are located within the states that make up those regions.

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What is the most common, most vital need of any business? Speed.

Speed to adapt, to respond, to evolve. It is important, not just at the big picture level, but on a daily basis as well. If you’re constantly waiting for information, then you’re spending less time analyzing data and making decisions that help the organization. Speed has been vitally important in the acceptance of Mode 2 style content such as Dashboards and Stories. Response time is key when viewing daily high-level metrics or when creating that “single use” asset to analyze a potential issue.

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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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In the first installment of our series on bimodal analytics, we talked about the origins of Mode 2 analytics. We looked at some of the challenges around implementing true bimodal analytics within IBM Cognos Analytics 11 and touched on some of the vendors who were born as Mode 2 platforms. This second installment will focus specifically on how to enable Mode 2 analytics within the organization using Cognos.

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Last week at Analytics University, IBM formally announced the release of the next major version of Cognos Analytics, v11.1.

IBM has hinted at the inclusion of “smarts” for “augmented analytics” and improvements in the usability of this new version over the past year. Our expectation was that these improvements would continue to “modernize” Cognos and help address some of the competitive pressures that organizations with legacy investments have been encountering in recent years. Read more

Well in advance of the IBM acquisition of Cognos, the Cognos name was synonymous with powerful, trusted enterprise business intelligence and managed reporting. Between its ability to scale to meet the needs of the largest enterprises, its robust, governed metadata layer that made it possible to report against a vast array of different data sources, powerful reporting capabilities churning out highly complex managed BI reporting solutions, and ad-hoc reporting and analysis against those governed data sources, IBM Cognos was the answer for almost all enterprise reporting needs. Until it wasn’t.

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Maintaining system availability is an important aspect of every Cognos Analytics administrator’s job. Monitoring your Cognos Analytics environment is a key part of maintaining this availability. You’re likely already monitoring the IBM Cognos service for Start/Stopped status, but this doesn’t always give an accurate representation of what’s going on with your servers. Often times, a user will report that Cognos isn’t available but the service is running. What else can you do? Fortunately, Cognos Analytics provides some informative URLs you can use to check the status of your content managers and dispatchers. Read more

The last several years have represented an interesting journey for organizations and teams leveraging Cognos for analytics. During that time, visual data discovery tools have made a significant impact. However, as of late, we have seen the pendulum swing back to concepts introduced by enterprise BI tools long ago.¹ What’s old is new again.

When these new tools arrived, they challenged both the status quo and what many of us saw as an ideal solution to the localized, ungoverned, manually-intensive, and often error-prone data manipulation (i.e. “shadow analytics”) processes of the past. If we think back to the dawn of the modern business intelligence age in the mid 1990’s, we realize that these challenges are what tools like Cognos were developed to solve. 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