January, 2019

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Analytics and Business Intelligence for a Data-Driven World

David Menninger's Analyst Perspectives

Ventana Research provides unique insight into the analytics and business intelligence (BI) industry. This is important, as its processes and technology play an instrumental role in enabling an organization’s business units and IT to utilize its data in both tactical and strategic ways to perform optimally. To accomplish this, organizations must provide technology that can access the data, generate and apply insights from analytics, communicate the results and support collaboration as needed.

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In the age of AI, fundamental value resides in data

O'Reilly on Data

The O’Reilly Data Show Podcast: Haoyuan Li on accelerating analytic workloads, and innovation in data and AI in China. In this episode of the Data Show , I spoke with Haoyuan Li, CEO and founder of Alluxio , a startup commercializing the open source project with the same name (full disclosure: I’m an advisor to Alluxio). Our discussion focuses on the state of Alluxio (the open source project that has roots in UC Berkeley’s AMPLab ), specifically emerging use cases here and in China.

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8 Tips For Implementing A Successful Business Intelligence Strategy

BA Learnings

In order to keep your business competitive, drive growth, ensure continuous improvement and enhance profits, a Business Intelligence (BI) strategy is essential. But a BI strategy is not just about technology or choosing the right platform. Like any part of your business, BI requires strategy, planning, buy-in, execution and continuous review. In this blog post, we will look at the key steps to implementing a business intelligence strategy.

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Programming Languages Most Used and Recommended by Data Scientists

Business Over Broadway

The practice of data science requires the use of analytics tools, technologies and programming languages to help data professionals extract insights and value from data. A recent survey of nearly 24,000 data professionals by Kaggle revealed that Python, SQL and R are the most popular programming languages. The most popular, by far, was Python (83% used).

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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Blockchain And GDPR: Not Mutually Exclusive But Can Be A Toxic Blend

Martha Bennett

Depending on who you listen to, the combination of GDPR and distributed ledger technology (DLT, AKA blockchain) is either a poisonous cocktail or a magic potion. As you’d expect, the reality is more nuanced: While GPDR poses a challenge to DLT-based architectures, it doesn’t make them obsolete or unviable. Furthermore, DLT can actually form an integral […].

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Machine Learning Data Prep Tips for Time Series Models

DataRobot Blog

by Jen Underwood. In my previous articles Predictive Model Data Prep: An Art and Science and Data Prep Essentials for Automated Machine Learning, I shared foundational data preparation tips to help you successfully. Read More.

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How companies are building sustainable AI and ML initiatives

O'Reilly on Data

A recent survey investigated how companies are approaching their AI and ML practices, and measured the sophistication of their efforts. In 2017, we published “ How Companies Are Putting AI to Work Through Deep Learning ,” a report based on a survey we ran aiming to help leaders better understand how organizations are applying AI through deep learning.

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5 Ways To Manage Denial On Business Projects

BA Learnings

You meet a client to discuss a problem. You present the facts and suggest solutions. They refuse to acknowledge your ideas. Worse still, they even refuse to accept the facts or shift their perspective. What can a Business Analyst do in such circumstances? Here are five ways to minimize confrontation, manage denial and find ways forward on business projects. 1.

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Most Popular Machine Learning Frameworks and Products Used by Data Professionals

Business Over Broadway

A recent survey revealed that 84% of data pros have used at least one ML framework in the last 5 years while 51% of data pros have used at least one ML product in the last 5 years. The most popular ML frameworks include Scikit-Learn, Tensorflow and Keras. The most popular ML products include SAS, Cloudera and Azure. Figure 1. Machine Learning Frameworks used in last 5 years.

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NLP Learning Series: Part 1 - Text Preprocessing Methods for Deep Learning

MLWhiz

Recently, I started up with an NLP competition on Kaggle called Quora Question insincerity challenge. It is an NLP Challenge on text classification and as the problem has become more clear after working through the competition as well as by going through the invaluable kernels put up by the kaggle experts, I thought of sharing the knowledge. Since we have a large amount of material to cover, I am splitting this post into a series of posts.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Open Data Science and Machine Learning for Business with Cloudera Data Science Workbench on HDP

Cloudera

It’s official – Cloudera and Hortonworks have merged , and today I’m excited to announce the availability of Cloudera Data Science Workbench (CDSW) for Hortonworks Data Platform (HDP). Trusted by large data science teams across hundreds of enterprises —. Western Union and IQVIA to name just a couple — CDSW is now also ready to help Hortonworks customers accelerate the delivery of new data products through secure, collaborative data science at scale.

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Data science transformations: Learn from these clients at Think 2019

IBM Big Data Hub

If you’re a data scientist or leading a team, Think 2019 is where you’ll want to be in February to hear success stories from clients using IBM’s data science portfolio of solutions.

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7 data trends on our radar

O'Reilly on Data

From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data. Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead. Increasing focus on building data culture, organization, and training. In a recent O’Reilly survey , we found that the skills gap remains one of the key challenges holding back the adoption of machine learning.

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Architect Machine Learning with IoT

Paul DeBeasi

Developers with no data science experience are now able to integrate Machine Learning (ML) with IoT. As the number of IoT endpoints proliferate, the need for organizations to understand how to architect machine learning with IoT will grow rapidly. However, for this to occur, IoT architects and data scientists must overcome the challenge of having two very different disciplines collaborate closely to design an ML-powered IoT system.

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How to Build an Experimentation Culture for Data-Driven Product Development

Speaker: Margaret-Ann Seger, Head of Product, Statsig

Experimentation is often seen as an aspirational practice, especially at smaller, fast-moving companies who are strapped for time and resources. So, how can you get your team making decisions in a more data-driven way while continuing to remain lean and maintaining ship velocity? In this webinar, Margaret-Ann Seger, Head of Product at Statsig, will teach you how to build an experimentation culture from the ground-up, graduating from just getting started with data-driven development to operating

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Real-time Analytics: The Tool for Efficient Consumer Acquisition and Retention.

BizAcuity

Today, there is a vast ocean of data that is being amassed every minute, every day. The biggest challenge is what we make of that data and how fast. Real-time analytics is a practice that analyzes this data as and when it comes into the system. Analysts are continuously sifting through and studying this data in order to identify a pattern or identify important insights that help can help businesses make informed decisions.

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A Layman guide to moving from Keras to Pytorch

MLWhiz

Recently I started up with a competition on kaggle on text classification, and as a part of the competition, I had to somehow move to Pytorch to get deterministic results. Now I have always worked with Keras in the past and it has given me pretty good results, but somehow I got to know that the CuDNNGRU/CuDNNLSTM layers in keras are not deterministic, even after setting the seeds.

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Why I’m Breaking Up with Facebook

Tim Mitchell

I have been in a serious relationship for more than 12 years. My partner in this relationship has brought me joy through the years, but lately, I feel like I’m giving to this relationship far more than I’m getting out of it. The relationship no longer brings me the joy that it once did, and has suffered from several breaches. The post Why I’m Breaking Up with Facebook appeared first on Tim Mitchell.

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Is your data ready for AI? Part 1

IBM Big Data Hub

Enterprise leaders understand the importance of integrating AI into their business models. However, there's a big difference between experimenting with AI and true enterprise-grade integration of AI.

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Entity Resolution Checklist: What to Consider When Evaluating Options

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

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How machine learning impacts information security

O'Reilly on Data

The O’Reilly Data Show Podcast: Andrew Burt on the need to modernize data protection tools and strategies. In this episode of the Data Show , I spoke with Andrew Burt , chief privacy officer and legal engineer at Immuta , a company building data management tools tuned for data science. Burt and cybersecurity pioneer Daniel Geer recently released a must-read white paper (“Flat Light”) that provides a great framework for how to think about information security in the age of big data and AI.

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VR Data Visualization: More Natural Interactions with Data

The Data Visualisation Catalogue

While researching on the Buzz Surrounding VR Data Visualization , I found the most common claim being made was that VR allows for more “Natural” interactions with the data. Initially, I thought to myself, how could virtual reality actually make things seem more natural? The fact that it’s called VIRTUAL reality already implies that it’s something unreal and not connected to the natural, physical world.

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Reinventing the Casinos with Customer Behaviour Analysis

BizAcuity

Technology is the pursuit for more, isn’t it? Today, across all spectrums of business, technology is being used to empower smarter and efficient solutions. At BizAcuity, we’ve identified one such domain of business that we felt could use a technological makeover. And that is what today’s blog is about – Customer Behaviour Analysis.

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Five Benefits of an Automation Framework for Data Governance

erwin

Organizations are responsible for governing more data than ever before, making a strong automation framework a necessity. But what exactly is an automation framework and why does it matter? In most companies, an incredible amount of data flows from multiple sources in a variety of formats and is constantly being moved and federated across a changing system landscape.

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Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity

Speaker: Nicholas Zeisler, CX Strategist & Fractional CXO

The first step in a successful Customer Experience endeavor (or for that matter, any business proposition) is to find out what’s wrong. If you can’t identify it, you can’t fix it! 💡 That’s where the Voice of the Customer (VoC) comes in. Today, far too many brands do VoC simply because that’s what they think they’re supposed to do; that’s what all their competitors do.

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How Jet Budgets Fits Every Budgeting Methodology 

Jet Global

There are many ways to prepare a budget. While the final result is the same, the road to get there is usually quite different. Between different accounting strategies to budgeting tools, every company around the world has a choice in how they shape their budgeting process. What Budgeting Methodology Do You Use? After speaking with hundreds of Microsoft Dynamics customers, we uncovered the five most commonly used budgeting methodologies and techniques: Top-down budgeting.

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Top five questions for Chief Data Officers in 2019

IBM Big Data Hub

It’s that time of the year to step back, evaluate what worked, what did not, and what to do differently to make things better personally and professionally. The same applies to businesses.

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Using machine learning and analytics to attract and retain employees

O'Reilly on Data

The O’Reilly Data Show Podcast: Maryam Jahanshahi on building tools to help improve efficiency and fairness in how companies recruit. In this episode of the Data Show , I spoke with Maryam Jahanshahi , research scientist at TapRecruit, a startup that uses machine learning and analytics to help companies recruit more effectively. In an upcoming survey, we found that a “skills gap” or “lack of skilled people” was one of the main bottlenecks holding back adoption of AI technologies.

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New Official Mug and Store Update

The Data Visualisation Catalogue

I would like to announce the release of the Official Data Visualisation Catalogue Mug ! That’s right, the chart icons from the homepage are now featured onto a mug. So now you view through a list of chart types, while drinking your favourite hot beverage. This sturdy mug is made from quality white and glossy ceramic. It’s perfect for any data or tech enthusiasts out there who like to drink their coffee or tea while working with data or for simply relaxing.

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Monetizing Analytics Features

Think your customers will pay more for data visualizations in your application? Five years ago, they may have. But today, dashboards and visualizations have become table stakes. Turning analytics into a source of revenue means integrating advanced features in unique, hard-to-steal ways. Download this white paper to discover which features will differentiate your application and maximize the ROI of your analytics.

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Reflections on the Data Science Platform Market

Domino Data Lab

Reflections. Before we get too far into 2019, I wanted to take a brief moment to reflect on some of the changes we’ve seen in the market. In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. This post describes our observations about these three segments and offers advice for folks evaluating products in this space.

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erwin Automation Framework: Achieving Faster Time-to-Value in Data Preparation, Deployment and Governance

erwin

Data governance is more important to the enterprise than ever before. It ensures everyone in the organization can discover and analyze high-quality data to quickly deliver business value. It assists in successfully meeting increasingly strict compliance requirements, such as those in the General Data Protection Regulation (GDPR). And it provides a clear gauge on business performance.

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Who Was Smarter, Karl Benz or Sigmund Freud?

Teradata

David Socha compares Karl Benz and Sigmund Freud, two people that fundamentally and indisputably influenced how we live today.

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Data Science: Influencers review 2018 and share their 2019 predictions

IBM Big Data Hub

Data science was one of the hot topics of 2018, and it’s likely to dominate again in 2019. We've asked five key data science influencers to take a look back at 2018 and look ahead at what's to come in 2019.

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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.