David Menninger's Analyst Perspectives

JANUARY 29, 2019

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.

Business Intelligence 230

Business Intelligence Data-driven Analytics Optimization 230

O'Reilly on Data

JANUARY 3, 2019

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.

Technology 209

Technology Analytics 209

BA Learnings

JANUARY 11, 2019

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.

Business Intelligence 100

Business Intelligence Strategy Key Performance Indicator Cost-Benefit 100

Business Over Broadway

JANUARY 13, 2019

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).

Machine Learning 110

Machine Learning Data Science Visualization Technology 110

Speaker: John Mansour

If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.

Management

Martha Bennett

JANUARY 8, 2019

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 […].

Technology 96

Technology Risk Management IT Risk 96

DataRobot Blog

JANUARY 27, 2019

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.

Machine Learning 92

Machine Learning Modeling Predictive Modeling Predictive Analytics 92

O'Reilly on Data

JANUARY 29, 2019

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.

Deep Learning 208

Deep Learning Machine Learning Data Science Metadata 208

BA Learnings

JANUARY 5, 2019

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.

Management 100

Management Risk IT 100

Business Over Broadway

JANUARY 27, 2019

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.

Machine Learning 89

Machine Learning Data Science Technology Analytics 89

MLWhiz

JANUARY 16, 2019

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.

Deep Learning 81

Deep Learning IT 81

Advertisement

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

Analytics

Paul DeBeasi

JANUARY 25, 2019

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.

IoT 75

IoT Machine Learning Deep Learning Data Science 75

IBM Big Data Hub

JANUARY 23, 2019

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.

Data Science 83

Data Science 83

O'Reilly on Data

JANUARY 17, 2019

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.

Machine Learning 204

Machine Learning Big Data Data Science Risk 204

BizAcuity

JANUARY 25, 2019

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.

Analytics 75

Analytics Machine Learning Sales Marketing 75

Advertisement

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Visualization

Tim Mitchell

JANUARY 1, 2019

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.

IT 75

IT 75

MLWhiz

JANUARY 5, 2019

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.

IT 75

IT 75

erwin

JANUARY 24, 2019

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.

Data Governance 71

Data Governance Metadata Data-driven Cost-Benefit 71

IBM Big Data Hub

JANUARY 10, 2019

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.

Enterprise 81

Enterprise Modeling 81

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Experimentation

O'Reilly on Data

JANUARY 8, 2019

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.

IoT 204

IoT Machine Learning Internet of Things Data Science 204

BizAcuity

JANUARY 21, 2019

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.

Recreation/Entertainment 75

Recreation/Entertainment Data Collection Modeling Technology 75

Cloudera

JANUARY 30, 2019

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.

Data Science 71

Data Science Machine Learning Experimentation Cost-Benefit 71

The Data Visualisation Catalogue

JANUARY 30, 2019

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.

Interactive 69

Interactive Visualization Big Data Technology 69

Speaker: Kevin Kai Wong, President of Emergent Energy Solutions

In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.

Manufacturing

Jet Global

JANUARY 22, 2019

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.

Consulting 67

Consulting Management Strategy Enterprise 67

IBM Big Data Hub

JANUARY 18, 2019

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.

81

81

O'Reilly on Data

JANUARY 31, 2019

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.

Machine Learning 182

Machine Learning Analytics Statistics Reporting 182

Domino Data Lab

JANUARY 24, 2019

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.

Data Science 65

Data Science Marketing Data-driven Statistics 65

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.

Testing

erwin

JANUARY 17, 2019

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.

Metadata 63

Metadata Data Governance Data Quality Data-driven 63

Teradata

JANUARY 7, 2019

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

75

75

The Data Visualisation Catalogue

JANUARY 18, 2019

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.

IT 62

IT 62

IBM Big Data Hub

JANUARY 8, 2019

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.

Data Science 80

Data Science 80

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

Management