In this contributed article, Pete Goddard, CEO and co-founder of Deephaven Data Labs, paints a new picture of data: one of a synchronized group effort rather than a relay race of individual, siloed teams. He explains how the “baton pass” method of working with data – inherently full of risks and slow-downs – has evolved but not quite met the needs of modern data teams. Pete then outlines how de-compartmentalizing data use cases can lead to a new understanding that data is changing and exists on a continuum of time. When all data problems are viewed through the lens of how data needs to meet software, and diverse teams are brought together to work in tandem on these interesting problems, we can create one frictionless data world free from the current limits of intermediation.
Book Review: Modern Data Science with R, 2nd Edition
There’s good reason why the word “modern” is in the title of this new title from CRC Press: “Modern Data Science with R, 2nd,” by 3 professors Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton – the goal of the text is to provide a solid guide for state-of-the-art data science with the […]
2022 Can Be Insurance’s Big Data Moment
In this special guest feature, Robert Clark, CEO of Cloverleaf Analytics, discusses trends and other factors driving what he believes to be a major year for big data analytics and BI in insurance in 2022. The article will outline a few major reasons why insurance can become a standard-bearer regarding innovative uses of big data and analytics.
Heard on the Street – 1/12/2022
Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.
Cogniteam’s Nimbus Supports Cloud-Enabled Robot Deployment Powered by NVIDIA Jetson Edge AI Platform
Cogniteam today announced it is supporting the NVIDIA Jetson edge AI platform into its cloud-based robotics development and deployment platform, allowing cloud-enabled deployments to use NVIDIA technology for the first time. More customers are moving over to NVIDIA’s GPU-accelerated technologies that can be developed and deployed through the cloud.
Video Highlights: MLDataR – a Data Package for Supervised Machine Learning in R
The video presentation below is courtesy of Gary Hutson, regarding a new R package he launched recently on CRAN to provide example ML data sets for supervised machine learning problems. The data sets have examples in healthcare, but he plans to widen to include other types of data.
Alteryx Announces Acquisition of Trifacta
Alteryx, Inc. (NYSE: AYX), the Analytics Automation company, announced it has entered into a definitive agreement to acquire Trifacta, an award-winning cloud company that leverages scalable data management and machine learning to make data analytics faster and more intuitive.
The insideBIGDATA IMPACT 50 List for Q1 2022
The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, […]
Video Highlights: Expert.ai – Executive Interview
In this interview, expert.ai’s CEO Walt Mayo gives an update on the company’s recent FY20 results. He discusses the technology that is being developed as part of the company’s Path to Lead five-year strategy and outlines how the company expects to commercialise it. He discusses the wider natural language understanding/processing (NLU/NLP) market, highlighting recent M&A activity. Finally, he outlines the key milestones the company is targeting over the next 12 months.
68% of CTOs have Implemented Machine Learning at their Organization
Research from STX Next, Europe’s largest software development company specializing in the Python programming language, has found that 68% of chief technical officers (CTOs) have implemented machine learning at their company. This makes it overwhelmingly the most popular subset of AI, with others such as natural language processing (NLP), pattern recognition and deep learning also showing considerable growth.
