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Editorial Review of “Building Industrial Digital Twins”

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[link]. I was asked by the publisher to provide an editorial review of the book “Building Industrial Digital Twins: Design, develop, and deploy digital twin solutions for real-world industries using Azure Digital Twins“, by Shyam Varan Nath and Pieter van Schalkwyk.

My top learning and pondering moments at Splunk.conf22

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I recently attended the Splunk.conf22 conference. While the event was live in-person in Las Vegas, I attended virtually from my home office. Consequently I missed the incredible in-person experience of the brilliant speakers on the main stage, the technodazzle of 100’s of exhibitors’ offerings in the exhibit arena, and the smooth hip hop sounds from the special guest entertainer — guess who ?

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Data Insights for Everyone — The Semantic Layer to the Rescue

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What is a semantic layer? That’s a good question, but let’s first explain semantics. The way that I explained it to my data science students years ago was like this. In the early days of web search engines, those engines were primarily keyword search engines.

Top 10 Data Innovation Trends During 2020

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The year 2020 was remarkably different in many ways from previous years. In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data. This steady march of data-driven innovation has been a consistent characteristic of each year for at least the past decade. These data-fueled innovations come in the form of new algorithms, new technologies, new applications, new concepts, and even some “old things made new again”.

Monetizing Analytics Features: Why Data Visualization Will Never Be Enough

Five years ago, data visualizations were a powerful way to differentiate a software application. Today, free visualizations seem to be everywhere. Two trends are forcing application providers to rethink how they offer analytics in their products.

Analytics Insights and Careers at the Speed of Data

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How to make smarter data-driven decisions at scale : [link]. The determination of winners and losers in the data analytics space is a much more dynamic proposition than it ever has been.

Are You Content with Your Organization’s Content Strategy?

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In this post, we will examine ways that your organization can separate useful content into separate categories that amplify your own staff’s performance. Before we start, I have a few questions for you. What attributes of your organization’s strategies can you attribute to successful outcomes? How long do you deliberate before taking specific deliberate actions? Do you converse with your employees about decisions that might be the converse of what they would expect?

Data Science Blogs-R-Us

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I have written articles in many places. I will be collecting links to those sources here. The list is not complete and will be constantly evolving. There are some older blogs that I will be including in the list below as I remember them and find them. Also included are some interviews in which I provided detailed answers to a variety of questions. In 2019, I was listed as the #1 Top Data Science Blogger to Follow on Twitter.

The Power of Graph Databases, Linked Data, and Graph Algorithms

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In 2019, I was asked to write the Foreword for the book “ Graph Algorithms: Practical Examples in Apache Spark and Neo4j “ , by Mark Needham and Amy E. Hodler.

RPA and IPA – Their Similarities are Different, but Their Rapid Growth Trajectories are the Same

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When I was growing up, friends at school would occasionally ask me if my older brother and I were twins. We were not, though we looked twin-like! As I grew tired of answering that question, one day I decided to give a more thoughtful answer to the question (beyond a simple “No”).

EX is the New CX

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(This article is a continuation of my earlier article “ When the Voice of the Customer Actually Talks.”). I recently attended (virtually) CX Summit 2021 , presented by Five9 , which focused on “CX Reimagined.” At first this title for the event seemed a bit grandiose to me – Reimagined! After attending the event, I now think the title was perfect, and it could have gone even further.

The 2023 Supply Chain Crystal Ball: Challenges and Solutions

Speaker: Olivia Montgomery, Associate Principal Supply Chain Analyst

Curious to know how your peers are navigating ongoing disruption? In this webinar, you’ll gain actionable insights from Olivia Montgomery as she walks us through Capterra’s extensive research on how businesses - notably small and midsize businesses - are addressing supply chain challenges in 2023.

Glossaries of Data Science Terminology

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Here is a compilation of glossaries of terminology used in data science, big data analytics, machine learning, AI, and related fields: Glossary of common Machine Learning, Statistics and Data Science terms. Data Science Glossary on DataScienceCentral. Data Science Glossary. Machine Learning Glossary at Google. Glossary of Artificial Intelligence Terms (From A to Z). Big Glossary of Artificial Intelligence on Wikipedia. 28 Artificial Intelligence Terms You Need to Know.

Shocking Amount of Data

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50 years after the publication of Alvin Toffler ’s landmark book “ Future Shock “, a new book “ After Shock ” is here.

Key Strategies and Senior Executives’ Perspectives on AI Adoption in 2020

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Artificial intelligence (AI) has become one of the most significant emerging technologies of the past few years. Some market estimates anticipate that AI will contribute 16 trillion dollars to the global GDP (gross domestic product) by 2030.

Meta-Learning For Better Machine Learning

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In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. An example of a cold start problem is k -Means Clustering, where the number of clusters k in the data set is not known in advance, and the locations of those clusters in feature space ( i.e., the cluster means) are not known either.

How We Teach The Leaders of Tomorrow To Be Curious, Ask Questions and Not Be Afraid To Fail Fast To Learn Fast

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I recently enjoyed recording a podcast with Joe DosSantos (Chief Data Officer at Qlik ). This was one in a series of #DataBrilliant podcasts by Qlik , which you can also access here and here. I summarize below some of the topics that Joe and I discussed in the podcast.

5 Powerful Prescriptive Analytics Examples in Supply Chain

Prescriptive analytics is a type of advanced analytics that optimizes decision-making by providing a recommended action. Supply chain, with its complex planning questions, is typically an area where optimization technology is required. Read about 5 use cases.

Top 10 Conversations That You Do Not Want to Have on Data Innovation Day

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[Note: this post is a slightly modified version of an earlier post. Although written with light (perhaps weak) humor, this post was created to bring attention to the Benefits of Open Data for innovation, value creation, and digital transformation on Open Data Day.]. Data Innovation is a powerful strategic goal for data-intensive organizations, especially to be celebrated through Data Innovation Day events , whenever they may occur.

Recent top-selling books in AI and Machine Learning

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Machine Learning Making Big Moves in Marketing

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Machine Learning is (or should be) a core component of any marketing program now, especially in digital marketing campaigns. The following insightful quote by Dan Olley (EVP of Product Development and CTO at Elsevier) sums up the urgency and criticality of the situation: “If CIOs invested in machine learning three years ago, they would have wasted their money. But if they wait another three years, they will never catch up.” ” This statement also applies to CMOs.

Bias-Busting with Diversity in Data

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Diversity in data is one of the three defining characteristics of big data — high data variety — along with high data volume and high velocity.

Intent Signal Data 101

Intent signal data helps B2B marketers engage with buyers sooner in the sales cycle. But there are many confusing terms used to describe intent data. Read this infographic to better understand three common areas of confusion.

Analytics By Design, For The Analytics Win

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We hear a lot of hype that says organizations should be “ Data – first ”, or “AI- first , or “ Data – driven ”, or “ Technology – driven ”. A better prescription for business success is for our organization to be analytics – driven and thus analytics-first , while being data -informed and technology -empowered. Analytics are the products, the outcomes, and the ROI of our Big Data , Data Science, AI, and Machine Learning investments!

Data Scientist’s Dilemma – The Cold Start Problem

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The ancient philosopher Confucius has been credited with saying “study your past to know your future.” This wisdom applies not only to life but to machine learning also. Specifically, the availability and application of labeled data (things past) for the labeling of previously unseen data (things future) is fundamental to supervised machine learning. Without labels (diagnoses, classes, known outcomes) in past data, then how do we make progress in labeling (explaining) future data?

Glossary of Digital Terminology for Career Relevance

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Career Relevance. Definitions of terminology frequently seen and used in discussions of emerging digital technologies. NOTE: This page is a WIP = Work In Progress.). AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Examples: (1-3) All those applications shown in the definition of Machine Learning. (4) 4) Credit Card Fraud Alerts. (5)

Sensor Analytics on Big Data at Micro Scale

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We often think of analytics on large scales, particularly in the context of large data sets (“Big Data”). However, there is a growing analytics sector that is focused on the smallest scale. That is the scale of digital sensors — driving us into the new era of sensor analytics. Small scale ( i.e., micro scale) is nothing new in the digital realm. After all, the digital world came into existence as a direct consequence of microelectronics and microcircuits.

Modern Data Architecture for Embedded Analytics

Data has gone from a mere result of applications & processes to being crowned king. Picking the right avenue to data architecture depends on your organization’s needs. Development teams should build in stages, starting with a task that can be solved.

Blockchain applications in the Federal Government sector

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My current position is Principal Data Scientist and Executive Advisor at global technology consulting firm Booz Allen Hamilton. In this role, I am frequently asked many questions about the work we are doing, specifically with the U.S. federal government. I always defer those types of questions to open source materials, including articles, press releases, and content published on my company’s website. I was recently asked about federal government applications of blockchain technology.

Data Makes Possible Many Things: Insights Discovery, Innovation, and Better Decisions

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In the early days of the big data era (at the peak of the big data hype), we would often hear about the 3 V’s of big data (Volume, Variety, and Velocity). Then, people started adding more V’s, including Veracity and Value , plus many more!

Variety is the Secret Sauce for Big Discoveries in Big Data

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When I was out for a walk recently, I heard a loud low-flying aircraft passing overhead. This was not unusual since we live in the flight path of planes landing at a major international airport about 10 miles from our home. In this case, I thought to myself that the sound seemed more directly overhead and lower than normal as well as being suggestive of a larger than average jet aircraft. I realized that in my one simple thought, I had made three different inferences from a single stream of data.

Data Science Training Opportunities

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A few years ago, I generated a list of places to receive data science training. That list has become a bit stale. So, I have updated the list, adding some new opportunities, keeping many of the previous ones, and removing the obsolete ones.

A Deep Dive Into Supply Chain Strategy: Why Yours Isn't Working

Speaker: Michelle Meyer, Founder and CEO of MatterProviders

Michelle Meyer is here to walk you through the future of supply chain strategy, and why your current approach is probably not working. In this exclusive webinar, she will explore ways to develop and perfect your new supply chain design in this post-pandemic era of economic uncertainty.