One could argue that many of the world’s problems can be solved with data. While I won’t be able to save the world just yet, I’d like to explain how statistical analysts and data experts use tools to understand data and how this data can then be managed to influence our environment.
The event formerly known as IBM Analytics University is happening again this fall. Join us at the Data and AI Forum on October 21–24 in Miami, Florida. Here’s a preview of what you can get out of this exciting event.
For 2019, we’ve expanded the curriculum to include the entire IBM Data and AI
In my last blog, I stressed the need for a modern data architecture (MDA) to underpin the next generation of the cognitive enterprise, fully harness data using the latest technologies, and sustain a platform-centric business model that supports people, process and technology optimized around
Before making any major purchase decision, most of us read reviews to learn about the experiences of other users and get an understanding of a product from the perspective of the marketplace. This is especially important for when evaluating options for a major investment like planning software.
IBM Cloud Pak for Data System is an integrated end-to-end platform that is cloud native by design, architected as microservices and containerized workloads. It offers instant pre-assembled provisioning and has capabilities to collect, organize and analyze data. It takes the IBM Cloud Pak for Data
A modern data architecture (MDA) must support the next generation cognitive enterprise which is characterized by the ability to fully exploit data using exponential technologies like pervasive artificial intelligence (AI), automation, Internet of Things (IoT) and blockchain. In addition, an MDA
A few competitors are trying to sow doubt about IBM’s commitment to IBM TM1 and IBM Planning Analytics – which is powered by IBM TM1 – as well as the product’s future, and the implications of the latest upgrade. Let me set the record straight—IBM Planning Analytics isn’t going anywhere. In fact,
Predictive modeling and analytics have long been the domain of the data scientist and only the data scientist. But with modern tools, data science is becoming a team sport—business analysts and subject matter experts can join the analysis. While the players may have different skill sets and
It's well-established that customer retention is much less expensive than customer acquisition. Therefore, preventing churn is a priority for nearly every company. A vital part of a viable strategy for preventing churn is the effective use of data. Let’s look at the role predictive analytics can