Forrester, a leading IT analyst firm, has constructed a Total Economic Impact (TEI) framework with a focus on how IBM Planning Analytics compares to the competition across a variety of metrics—from ROI, to time, to value—and much more. They consulted several current IBM Planning Analytics customers
IBM’s Data and AI Forum was chock full of the latest news and trends from all over the Data and AI landscape. Importantly, it was as especially important event for IBM Business Analytics users as we unveiled the latest and greatest from IBM Planning Analytics and IBM Cognos Analytics.
A modern enterprise IT platform often needs to be all things to all people: to accommodate both new and legacy workloads and be managed by both internal and external experts. To achieve this breadth of capabilities, many businesses have augmented their existing platforms with cloud capabilities
From reading the news headlines of yet another retail chain closing its stores, one can easily be left with the impression that we’re in a retail apocalypse. But in reality, the overall retail industry is very strong and healthy—especially online.
What we’re witnessing however, is a transformation
Gartner analysts Merv Adrian and Donald Feinberg in a February 2018 report predict that “by 2022, more than 70 percent of new applications developed by corporate users will run on an open source database management system.” This isn’t surprising given the influx of multiple types of data from
The Internet and subscription-service businesses have changed how we access everything from news to shopping to music. So, is it any wonder that software has followed suit? In this blog, we’ll look at the differences between an SPSS Statistics Subscription and the traditional on-premises license
Proper use of time series and location data in prediction and optimization can considerably boost the yield of data science and AI initiatives. Using them properly in AI applications has been challenging, but spatiotemporal functions, implemented as part of Analytic Engines in Watson Studio, are
The conversation around data preparation has been evolving. What started as a push for self-service access for specific use cases has now expanded to operationalizing a data pipeline across the enterprise. The goal is to create efficiencies and eliminate workflow silos to propel data strategy
At IBM, we led the humans to the moon and coined the term machine learning 50 years ago. Now we are helping organizations scale the ladder to AI to reap rewards in growth, productivity and efficiency with IBM Watson. This journey to AI mirrors the history of travel. In this blog, I’ll describe how
This unified end-to-end platform, Cloud Pak for Data, delivers these data and AI capabilities as container-based microservices that help to power new and existing enterprise applications to run on cloud or on-premises. The platform makes it easy to implement data-driven processes and operations and
IBM is announcing the latest update to the IBM Cloud Pak for Data platform, Version 2.5. We are extremely excited for this release, as it brings to a head three key areas we’ve been building towards over the last year and a half: Red Hat integration, new key built-in capabilities and last but not
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.