Mike Gualtieri

Machine-learning models may occasionally suggest an undesirable action. The action might be either biased or unethical — or just bad for business. Most of the time, the suggestion won’t be wrong. How can developers protect against these bad outcomes? It’s Simple You don’t have to do what the model tells you to do. If someone told […]

Join us in Orlando on December 4, 2018 for Forrester’s first-ever Data Strategy & Insights 2018 Forum. We are super excited to bring you razor-sharp focus to the power of data, analytics, and machine learning. Back to dancing, because that is our conference theme — “Insights To Action: For Real This Time.” Data is too often […]

Enterprise Mindsets About Data Are Changing Give me a dashboard. Give me a report. Give me better insights. If that’s your approach, it’s old-school and you’re falling behind. Leading enterprises have shifted their data sensibilities to action-oriented insights. “Interesting” is no longer the standard for business insights efforts. Instead, insights projects must draw a straight […]

Machine learning is an elemental core competency. It is a fundamental building block to AI. It gives enterprises the power to predict. Most importantly, it can make enterprises gain the agility of disruptive upstarts by injecting scalable intelligence into customer experiences, business process applications, and employee decisions. That’s where predictive analytics and machine learning (PAML) solutions […]

One breakthrough of AI is deep learning: a branch of machine learning that can uncannily identify objects in images, recognize voices, and create other predictive models by analyzing data. Deep learning can use regular CPUs, but for serious projects, data science and AI engineering teams must use AI chips such as GPUs that can handle […]

One breakthrough of AI is deep learning: a branch of machine learning that can uncannily identify objects in images, recognize voices, and create other predictive models by analyzing data. Deep learning can use regular CPUs, but for serious projects, data science and AI engineering teams must use AI chips such as GPUs that can handle […]

Written by Emily Miller, Senior Research Associate Stop Wasting Time More than half (54%) of global information workers are interrupted from their work a few times or more per month to spend time looking for or trying to get access to information, insights, and answers. The problem: Old keyword-based enterprise search engines of the past are obsolete. […]

At the highest conceptual level, deep learning is no different from supervised machine learning. Data scientists start with a labeled data set to train a model using an algorithm and, hopefully, end up with a model that is accurate enough at predicting the labels of new data that is run through the model. For example, […]

Yes, AI will take jobs away from many workers – our relatives, friends, and neighbors. So too have all technologies created throughout human history. We invent things to make things easier and the impossible possible. The invention of the wheel made transport easier. Gutenberg’s printing press put lots of monk’s out of business. The chainsaw […]

The Power To Predict Is Mighty Yogi Berra once said, "It's tough to make predictions, especially about the future." It is tough indeed, but enterprises that can make probabilistic predictions about customers, business processes, and operations will have an edge over enterprises that can't. These predictions don't have to be macroscopic to be consequential. Predictions […]

Prescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn’t that what all analytics should be about? A hearty “yes” to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all? Many wrongly and incompletely define […]

Spark Summit East came to Boston this year and I was there to enjoy it including being interviewed by Dave Vallente and George Gilbert about Apache Spark and AI on The Cube. We talk about the waning of the term "Big Data" , but get quickly into the future of AI and Apache Spark.

Artificial intelligence (AI) is real, albiet maturing slowly. You experience it when you talk to Alexa, when you see a creepily-targeted online ad, and when Netxflix turns you on to Stranger Things. Oh yea, and that self-driving car over there is AI super-powered! AI is indeed cool, but many are scared about how it ultimatley may […]

Pure AI is true intelligence that can mimic or exceed the intelligence of human beings. It is still a long way off, if it can even ever be achieved. But what if AI became pure — could perceive, think, act, and even replicate as we do? Look to humanity for the answer. Humanity has been […]

Artificial Intelligence (AI) is not one big, specific technology. Rather, it is comprised of one or more building block technologies. So, to understand AI, you have to understand each of these nine building block technologies. Now, you could argue that there are more technologies than the ones listed here, but any additional technology can fit […]

Forrester surveyed business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems. This gap reflects growing interest in AI, but little actual use at this time. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and […]

Enterprises agree that speedy deployment of big data Hadoop platforms has been critical to their success, especially as use cases expand and proliferate. However, deploying Hadoop systems is often difficult, especially when supporting complex workloads and dealing with hundreds of terabytes or petabytes of data. Architects need a considerable amount of time and effort to […]

Streaming Analytics Captures Real-Time Intelligence Most enterprises aren’t fully exploiting real-time streaming data that flows from IoT devices and mobile, web, and enterprise apps. Streaming analytics is essential for real-time insights and bringing real-time context to apps. Don’t dismiss streaming analytics as a form of “traditional analytics” use for postmortem analysis. Far from it —  streaming analytics […]

Hadoop thoroughly disrupts the economics of data, analytics, and data-driven applications. That's cool because the unfortunate truth has been that the potential of most data lies dormant. On average, between 60% and 73% of all data within an enterprise goes unused for analytics. That's unacceptable in an age where deeper, actionable insights, especially about customers, […]