Posts

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.

Read more

2019 is the year that data science, machine learning and artificial intelligence for business will become ubiquitous. Most organizations large and small, across all industries, have recognized the benefits and competitive advantage that these capabilities bring to bear. If you have not already begun the journey, chances are this will be the year you begin to develop this competency. Whether you’re about to take your first step, you’re a team of one looking to scale, or even a more mature organization that is always seeking self-improvement, consider the following traits to maximize your chances of success with data science.

Read more

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

This is part five in our five part series on the essential capabilities of the competitive data-driven enterprise.

Over the last 20 years of doing business we have seen a number of different analytical data storage and query concepts fall in and out of favor. Throughout this time, a wave of digital transformation in business has dramatically increased the volume of collected data. Machine learning and other probabilistic methods benefit greatly from the law of large numbers so if by now it wasn’t already clear, all that talk about “big data” has really been about the analytics that it enables. As a result, today’s knowledge workers are predisposed to data hoarding, preferring to save everything including the data for which there are no known use cases, since its future value to the organization may still yet be discovered. Read more

This is part four in our five part series on the essential capabilities of the competitive data-driven enterprise.

Businesses have been deploying enterprise data governance (defining what the data should be) and master data management (ensuring the data is as defined) programs for decades. Even if your company doesn’t have a formal master data management program by name, chances are good that they are doing some form of master data management in your data warehouse, CRM or ERP systems. As the trend towards decentralized data analysis continues to progress we see a few forces in play that make the case for incorporating a master data management capability into your organizational roadmap: Read more

This is part three in our five part series on the essential capabilities of the competitive data-driven enterprise.

Most business analysts will reach for their favorite data visualization tool when it comes time to perform driver and correlation analysis when in search of a cause. While this technology is essential for communicating with data, and excellent at identifying new opportunities (i.e. visualizing gaps or data non-relationships), it is limited in its ability to produce reliable, accurate and conclusive results. This is mostly due to our own human limitations when visually processing more than two dimensions of analysis at a time (e.g. revenue over time by product line). Read more

This is part one in our five part series on the essential capabilities of the competitive data-driven enterprise.

The most common form of data-enabled business problem solving begins with a hypothesis around business drivers and relationships within the data. Typically, a well tenured business analyst will pull together the data they know about or have access to in their department and proceed to build their analysis. This standard approach assumes that: Read more

If your organization is seeking to better manage its information as a corporate asset that is to be valued and capitalized, you’re likely focused on implementing programs that will catalyze measurable business results from mountains of business information that may be the product of the last decade or more of digital transformation initiatives. 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

Being the sole data champion within your organization can present difficulties when you’re vying for limited company resources and attention from the “powers that be.” No doubt, you may find the role to be frustrating at times. Yet you may also find the role to be extremely rewarding, because it gives you a great deal of responsibility and offers you with the opportunity to achieve the goal that every data champion aspires to: Gaining user buy-in of the data insights you’ve unlocked. Read more