Sat.May 19, 2018 - Fri.May 25, 2018

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5 fundamental questions for your data journey

IBM Big Data Hub

To accelerate its journey to AI, a data-driven organization needs a trusted data foundation that empowers information stakeholders. Stakeholders need the ability to discover, understand, integrate, analyze, govern and self-serve structured and unstructured data — on premises, on cloud, and hybrid — at any scale.

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Minimally Viable Data Visualization

Perceptual Edge

I received an email a few days ago from the founder and CEO of a new analytics software company that led to an interesting revelation. In his email, this fellow thanked me for sharing my insights regarding data visualization and shared that he has acquired several of my books, which are “nearing the top” of his queue. He went on to provide a link to his website where I could see his attempts to incorporate visual analytics into his product.

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Some stuff that’s always on my mind

DMBS2

I have a LOT of partially-written blog posts, but am struggling to get any of them finished (obviously). Much of the problem is that they have so many dependencies on each other. Clearly, then, I should consider refactoring my writing plans. So let’s start with this. Here, in no particular order, is a list of some things that I’ve said in the past, and which I still think are or should be of interest today.

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IDG Contributor Network: How to overcome the bottlenecks between data lakes and analytics for customer engagement

CIO Business Intelligence

Competing in the business world today is all about knowing your customers. Consumers have come to expect personalized service and a satisfying experience, and anything less from the brands they interact with might cause them to take their business elsewhere. Many organizations in a variety of industries struggle to access the customer data they need to provide personalized and contextual experiences across all touchpoints.

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State of AI in Sales & Marketing 2025

AI adoption is reshaping sales and marketing. But is it delivering real results? We surveyed 1,000+ GTM professionals to find out. The data is clear: AI users report 47% higher productivity and an average of 12 hours saved per week. But leaders say mainstream AI tools still fall short on accuracy and business impact. Download the full report today to see how AI is being used — and where go-to-market professionals think there are gaps and opportunities.

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Machine learning everywhere: IBM banks on trust and open standards

IBM Big Data Hub

Hear from IBM's Dinesh Nirmal on his thoughts on AI and the potential for widespread adoption as IBM puts its weight behind enterprise data science.

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Alternative approaches to implementing your data lake

ScienceSoft

ScienceSoft answers burning questions about big data lake design and implementation. We look at different approaches to its architecture and contemplate if there exists a preferred technology among the available stack.

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Fascinating 2018 Gartner Critical Capabilities Results for Analytics and BI

DataRobot Blog

by Jen Underwood. Earlier this month without nearly the grandiose fanfare or market madness of Gartner Magic Quadrant releases, the Gartner Critical Capabilities for Analytics and Business Intelligence Platforms Report 2018 was quietly. Read More.

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72-hour rule: Can you identify and report a data breach within 3 days?

IBM Big Data Hub

Rather than worry about your future performance, work to be the best today. The rest will follow. Seize the opportunity to respond to customers in the moment, rather than react too late. That way, everyone benefits.

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Nutanix “.hearts” New Orleans

Nutanix

NEXT… What an experience in one of the best cities in the US! I was fortunate enough to lead the.heart program at.NEXT NOLA where we encouraged attendees to “share your.heart.

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Building the Modern Platform with Cloudera Enterprise 6.x

Cloudera

Why Enterprises Need to Unify ML, Analytics, and Cloud. Times are changing, and the traditional models of analytics and data management don’t serve the needs of the modern enterprise, so the way to address these topics is changing too. While organizations are moving more workloads to the cloud, many mission-critical workloads remain on-prem. End users, data stewards, governance groups, and security groups alike can easily get overwhelmed with multiple access points, inconsistent user interfaces,

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How to Achieve High-Accuracy Results When Using LLMs

Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage

When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m

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Technology implications of political trends

DMBS2

The tech industry has a broad range of political concerns. While I may complain that things have been a bit predictable in other respects, politics is having real and new(ish) technical consequences. In some cases, existing technology is clearly adequate to meet regulators’ and customers’ demands. Other needs look more like open research challenges. 1.

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Silver Sponsor ElegantJ BI Demonstrates Smarten Analytic at Gartner Data & Analytics Summit, June 5-6, Mumbai, India

Smarten

ElegantJ BI, an innovative vendor in Business Intelligence, Augmented Analytics and Augmented Data Preparation, is pleased to announce its participation in the Gartner 2018 INDIA Data & Analytics Summit from 5 – 6th June 2018 in Mumbai, India. ElegantJ BI is proud to be a Silver Sponsor at this important event. ElegantJ BI CEO, Kartik Patel says, “We look forward to demonstrating our Smarten analytics software solution, and to seeing new and familiar faces, as we welcome analytics experts an

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Cloudera Altus is Now Available on Azure

Cloudera

It was exactly one year ago at Strata London that we introduced the world to Cloudera Altus Data Engineering. The premise was simple: make it quicker and easier for customers to drive data to their machine learning and analytics services by leveraging cloud resources, while at the same time, eliminating the pain associated with managing datacenter or cloud infrastructure.