What Exactly The Heck Are Prescriptive Analytics?

Prescriptive analyticsPrescriptive 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 prescriptive analytics as the what comes after predictive analytics. Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions. Think of the term “prescriptive” as the goal of all these analytics — to make more effective decisions — rather than a specific analytical technique. Forrester formally defines prescriptive analytics as:

"Any combination of analytics, math, experiments, simulation, and/or artificial intelligence used to improve the effectiveness of decisions made by humans or by decision logic embedded in applications."

Prescriptive Analytics Inform And Evolve Decision Logic Whether To Act (not not act) And What Action To Take

Prescriptive analytics can be used in two ways:

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Scifi Version Of AI Is Not Available Yet

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.

AI Makers Will Squelch Free Speech

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 toArtificial Intelligence 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 impact society. Stephen Hawking, Elon Musk, and even the Woz warned that "...artificial intelligence can potentially be more dangerous than nuclear war." In a nutshell, they are concerned about AI that may evolve to outsmart humans and kill people - a valid concern. But, I have another more terrifying concern that would likely be an insidious precursor to runaway, killer AI.

Billionaires And Tech Giants Will Censor AI

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AI Is The Sincerest Form of Flattery ... And Fear

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 both beautiful and brutal:

  • The beauty of ingenuity, survival, exploration, art, and kindness.
  • The brutality of crime, war, and pettiness.
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Micro Explanations For Nine Essential AI Technologies

Artificial Intelligence is rampant in the movie Ex MachinaArtificial 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 under one of these building blocks. This is a follow-on to my post Artificial Intelligence: Fact, Fiction, How Enterprises Can Crush It

Here are the nine pragmatic AI technology building blocks that enterprises can leverage now:

■        Knowledge engineering. Knowledge engineering is a process to understand and then represent human knowledge in data structures, semantic models, and heuristics (rules). AD&D pros can embed this engineered knowledge in applications to solve complex problems that are generally associated with human expertise. For example, large insurers have used knowledge engineering to represent and embed the expertise of claims adjusters to automate the adjudication process. IBM Watson Health uses engineered knowledge in combination with a corpus of information that includes over 290 medical journals, textbooks, and drug databases to help oncologists choose the best treatment for their patients.

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Artificial Intelligence: Fact, Fiction. How Enterprises Can Crush It

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 build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models.

But the burning question is: how can your enterprise use AI today to crush it? To answer this question we first must bring clarity to the nebulous definition of AI.Let’s break it down further:

■        “Artificial” is the opposite of organic. Artificial simply means person-made versus occurring naturally in the universe. Computer scientists, engineers, and developers research, design, and create a combination of software, computers, and machine to manifest AI technology.

■        “Intelligence” is in the eye of the beholder.  Philosophers will have job security for a very long time trying to define intelligence precisely. That’s because, intelligence is much tougher to define because we humans routinely assign intelligence to all matter of things including well-trained dachshunds, self-driving cars, and “intelligent” assistants such as Amazon Echo. Intelligence is relative. For AI purists, intelligence is more akin to human abilities. It means the ability to perceive its environment, take actions that satisfy a set of goals, and learn from both successes and failures. Intelligence among humans varies greatly and so too does it vary among AI systems.

Temper Your Expectations, But Don’t Give Up On AI

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On-Premise Hadoop Just Got Easier With These 8 Hadoop-Optimized Systems

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 install, tune, and optimize Hadoop. Hadoop-optimized systems (aka appliances) make on-premises deployments virtually instant and blazing fast to boot. Unlike generic hardware infrastructure, Hadoop-optimized systems are preconfigured and integrated hardware and software components to deliver optimal performance and support various big data workloads. They also support one or many of the major distros such as Cloudera, Hortonworks, IBM BigInsights, and MapR.  As a result, organizations spend less time installing, tuning, troubleshooting, patching, upgrading, and dealing with integration- and scale-related issues.

Choose From Among 8 Hadoop-Optimized Systems Vendors

Noel Yuhanna and me published Forrester Wave: Big Data Hadoop-Optimized Systems, Q2 2016  where we evaluated 7 of the 8 options in the market. HP Enterprise's solution was not evaluated in this Wave, but Forrester also considers HPE a key player in the market for Hadoop-Optimized Systems along with the 7 vendors we did evaluate in the Wave. 

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15 "True" Streaming Analytics Platforms For Real-Time Everything

Streaming Analytics Captures Real-Time Intelligence

Streaming AnalyticsMost 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 analyzes data right now, when it can be analyzed and put to good use to make applications of all kinds (including IoT) contextual and smarter. Forrester defines streaming analytics as:

Software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple, disparate live data sources and in any data format to identify simple and complex patterns to provide applications with context to detect opportune situations, automate immediate actions, and dynamically adapt.

Forrester Wave: Big Data Streaming Analytics, Q1 2016

To help enterprises understand what commercial and open source options are available, Rowan Curran and I evaluated 15 streaming analytics vendors using Forrester's Wave methodology. Forrester clients can read the full report to understand the market category and see the detailed criteria, scores, and ranking of the vendors. Here is a summary of the 15 vendors solutions we evaluated listed in alphabetical order:

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Hadoop Is Data's Darling For A Reason

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, are a competitive necessity. Enterprises are responding by adopting what Forrester calls "Hadoop and friends" (friends such as Spark and Kafka and others). Get Hadoop, but choose the distribution that is right for your enterprise.

Solid Choices All Around Make For Tough Choices

Forrester's evaluated five key Hadoop distributions from vendors: Cloudera, Hortonworks, IBM, MapR Technologies, and Pivotal Software. Forrester's evaluation of big data Hadoop distributions uncovered a market with four Leaders and one Strong Performer:

  • Cloudera, MapR Technologies, IBM, and Hortonworks are Leaders. Enterprise Hadoop is a market that is not even 10 years old, but Forrester estimates that 100% of all large enterprises will adopt it (Hadoop and related technologies such as Spark) for big data analytics within the next two years. The stakes are exceedingly high for the pure-play distribution vendors Cloudera, Hortonworks, and MapR Technologies, which have all of their eggs in the Hadoop basket. Currently, there is no absolute winner in the market; each of the vendors focuses on key features such as security, scale, integration, governance, and performance critical for enterprise adoption.

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The Predictive Modeling Process Using Machine Learning

Predictive analytics uses statistical and machine learning algorithms to find aptterns in data that might predict similar outcomes in the future. Check out this less than 3 minute, fun and fruity video to understand the six steps of predictive modeling.  For tools that use machine learning to build predictive models, Forrester clients can read The Forrester Wave: Big Data Predictive Analytics Solutions, Q2 2015 and A Machine Learning Primer For BT Professionals.