Artificial Intelligence and Machine Learning…

The traction over the last few years in the artificial intelligence (AI) and machine learning (ML) space is remarkable, and I’m not just talking about consumer-based products like self-driving cars, or virtual assistants like Google Assistant, Alexa, or Siri. While those products get the headlines, AI/ML is rapidly spreading across the enterprise IT space. I feel like I can’t go a day without a company mentioning AI or ML as part of their product or forward-looking strategy. It’s not just for crazy, sci-fi predictive analytics projects in a bunker somewhere. While that definitely still happens, AI and ML (and deep learning too) are being used across all aspects of IT: big data, cloud, IoT, security, infrastructure, systems management, etc.While AI/ML is a top priority for businesses that expect it to have a significant (positive) business impact as they continue to digitally transform, investments remain modest because of its sheer impact on all aspects of the infrastructure. Challenges associated with infrastructure cost, lack of in-house expertise, and insufficient data quality are just the start. To read the complete article, CLICK...

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Think Economics, Not Features, When Evaluating Big Data Value

Traditional enterprise data warehouse solutions helped to open the eyes of many organizations to the value of their data. Although these are significant systems, organizations quickly learned to monetize the actionable insight extracted from these systems, which led the rampant growth of the industry. Big data did not get big just from data growth. It got big because of its potential value, opportunities, and savings. The more cost-efficiently you can capture a lot of data, plus the number of ways you can analyze it, equals the more worthwhile all that data could become. Value is results divided by costs. These (pseudo-)equations of big data value now extend not only to the disruptive power of transformative technologies like Hadoop, but also to increasingly popular cloud services for databases and data warehouses. To read the complete article, CLICK...

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Database Forecast: Cloudy with Increasing Chances

ESG has recently published an overview on IT market adoption of cloud-based databases. Shall we just call them cloudbases? Perhaps not. A major trend is emerging. While relatively few are choosing cloud as their primary mode of deployment, majorities are currently running at least some of their production workload in the public cloud. Attitudes and adoption vary considerably by age of company (and age of respondent!), reflecting how deeply entrenched traditional on-premises offerings and processes may be for different businesses. How many, how many, and how much, you ask? ESG research subscribers can read the full report here. To read the complete article, CLICK...

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IBM DataWorks… the Next Big Data Thing

How vendors support open source technologies doesn’t follow a set path. Many start-ups and smaller companies (along with once-small, now-prosperous businesses) make near or complete commitments to open source for both philosophical and financial reasons. Many leverage Linux and other specific platforms at their developers’ or customers’ behest while others attempt to bend the technology to their own will or competitive advantage by creating proprietary forks. That’s a critical issue because of the natural ways in which open source development evolves along with and very often leads innovative industry trends. For example, the big data technologies and initiatives roiling IT over the past half-decade have been driven by open source Apache Software Foundation projects, including Hadoop, MapReduce and Spark. In fact, vendors that ignore or fail to support these products find themselves falling quickly behind the curve technologically and competitively. IBM has pursued a singular path in its own open source efforts. In the late 1990s, the company committed $1B to develop Linux solutions for its z Systems mainframes and other server platforms. That was followed by substantial investments in a range of platform and community-building projects, along with open sourcing technologies the company developed in-house with Eclipse and the POWER chip architecture among the most notable of these. The company also proactively invested in Apache Spark and other complementary advanced analytics and big data projects. What does any of this have to do with anything? IBM’s newly announced Project DataWorks qualifies as both the culmination of and a natural next step for the company’s open source big data strategies and goals. As such, it’s worth close consideration. To read the complete article, CLICK HERE NOTE: This column was originally published in the Pund-IT...

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Teradata Positioned To Weather IT’s ‘Perfect Storm’?

ATLANTA: For perhaps the first time in IT’s history, outcomes, not technology, are driving the industry, and data analytics specialist Teradata Corporation (TDC) is looking to take advantage of this ‘perfect storm’. The confluence of the latest IT tools and applications — social, cloud, mobility, big data, analytics, Internet of Things (IoT) and security — together with the substantial benefits of a data-driven approach, a business phenomenon with a body count, represents a huge opportunity for the company, which is trying to change its focus from technology to outcomes. “We have gone through the process of how we are actually changing: we are going to be business focused, not technology focused,” said President & CEO Vic Lund at last week’s Partners conference. Still, technology was a large part of the annual event’s agenda, where Teradata made a number of product and service announcements, including Borderless Analytics, RACE (Rapid Analytic Consulting Engagement), Customer Journey Analytic Solution, and Teradata Everywhere. They were significant announcements that represent a major shift for Teradata, said Oliver Ratzesberger, EVP and Chief Product Officer. “We’re focusing on business solutions more than we ever have before.” Digitalization of the enterprise is a big topic for most companies around the world, said Oliver Ratzesberger, EVP and CPO, Teradata. Companies are looking for “high-impact outcomes that benefit the bottom line”, an optimized analytical ecosystem with “flexibility and agility” to most effectively run  your organization. “Business-led outcomes is really what companies are focused on.” That has significant implications for Teradata, he said. “We’re focusing on business solutions more than we ever have before.” One of the biggest — if not the biggest — implications of the onrushing changes is the speed of these changes. I talk to customers every day and they’re terrified of their inability to change fast enough, said Teradata’s John Thuma, Data Scientist & Director of Analytics. In addition to the rate of change, is the need for a new set of KPIs (key performance indicators), he added. “It’s not the technology, it’s the people and processes that matter.” Not only can companies not rest on “past glories”, even if they can change, are the changes they’re making successful, he asked? “That’s also a big factor.” The paradigm that people were expendable is changing. Now organizations must not only look at how — and with who — they can continue to disrupt their competition, but also how to disrupt themselves. Ratzesberger agrees that the companies who are doing well with the digital transformation “have realized that the biggest problem is people, process in organizations.” It’s all about how you use it and reshape the company, empowering the...

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