News Category: Media Coverage

AI is considered the most disruptive technology, according to Gartner’s 2019 CIO Survey (it includes over 3,000 CIOs from 89 countries). So yes, this is a big reason why there has been a major increase in adoption and implementation. Yet there is a bottleneck that could easily slow the progress – that is, finding the right talent. The fact is that there are few data scientists and AI experts available. Read the full article "How To Reskill Your Workforce For AI (Artificial Intelligence)" in Forbes featuring Ryohei Fujimaki, dotData CEO and founder.

As automation increases and business needs evolve, so will the ranks of data scientists. But while the future data scientist role may look a little different -- with a heavier focus on business operations and oversight -- it will be no less important to enterprises."As the adoption of automated machine learning platforms ... spreads, the role of the data scientist will become less about building models and more about implementing them in a meaningful way," said Forrester analyst Brandon Purcell. Read the full article "A future data scientist needs business, deep learning skills" in the TechTarget featuring Ryohei Fujimaki, dotData CEO and founder.

As the role of data scientist grows in magnitude, it's more important than ever to stay current on today's most in-demand data science skills. But it's not just the programming and mathematical skills that data scientists need to hone; domain and business knowledge is equally valuable. The top tech skills employers look for most in data scientists are machine learning and expertise in Python, R, SQL and Hadoop, according to research results published in April 2019 by Indeed, one of the top jobs sites. Read the full article "Most in-demand data science skills include ML, Python" in the TechTarget featuring Ryohei Fujimaki, dotData CEO and founder.

Data science is a major area of investment for banks.   It has proven impact on cybersecurity and fraud protection, risk mitigation, customer relationship management and more. When fully operationalized for production, data science enables banks to make data-driven decisions with unprecedented levels of speed, transparency and accountability.  Its accelerating digital transformation initiatives while delivering better financial products and services that meet customers’ needs.  Time-to-market  delivery for data science impact is crucial to success.  It is especially crucial for traditional retail banks with physical locations and high overhead that must find innovative ways to compete with their online competitors.Read the full article "Better Data, Better Banking: How New Technologies are Helping Banks Solve the Big Data Challenge" in the Bank News featuring Ryohei Fujimaki, dotData CEO and founder.

Data science has emerged as a new powerhouse for organizations.  It has turned mountains of data into actionable business insights that can impact every part of the business. However, not all data science platforms and methodologies are created equal. The ability to use data science to make business decisions with transparency and accountability comes down to many factors. These may include whether the platform uses a Black Box or White Box model. Once the industry standard, Black Box models offer high degrees of accuracy. Neither the transparency nor features required for accountability and actionable insights are provided.  White Box models offer accuracy while also explaining how they behave, how they produce predictions and what influencing variables are available. Read the full article "Mobile Apps That Use Big Data Better Do the Data Science Too" in App Developer Magazine featuring Ryohei Fujimaki, dotData CEO and founder.

Companies are moving forward with digital transformation with unprecedented speed. A recent survey by Gartner Research found that 49 percent of CIOs are reporting their enterprises have updated business models to better scale their digital endeavors. As companies embrace these transformations, they are infusing data science and machine learning in various business functions.  A typical enterprise data science project is highly complex and requires deployment of an interdisciplinary team.  That team is often comprised of  folks such as data engineers, developers, data scientists, subject matter experts and may even be individuals with other special skills or knowledge. Read the full article "Five Reasons Why Your Data Science Project is Likely to Fail" in eWEEK featuring Ryohei Fujimaki, dotData CEO and founder.

Regarding data science, there are a couple of important areas where the financial services industry is particularly interested.  It’s all about understanding. The first area to observe in the financial sector has to do with risk. Data science can help financial institutions adhere to compliance standards, and/or credit control detection, for example. The second area surrounds the customer. Data science can provide insight to better understand the customer, their individual desires and pain points. All organizations, such as banking or B2B, want to better understand the customer while exploring new avenues for business growth. Read the article "Overcoming Legacy and More Stringent Regulation in the Financial Industry with Data Science" on Information Age featuring Ryohei Fujimaki, dotData CEO and founder.

dotData is the first and only company focused on delivering end-to-end data science automation for the enterprise.  It recently announced version 1.4 of the enterprise platform. This latest version adds significant enhancements to the platform providing users with deeper insights, increased flexibility, and greater performance. The AI-powered Data Science Automation platform completely automates the entire data science process from data collection through production-ready models, including feature engineering. Read the full article "dotData to Showcase New Version of its Data Science Automation Platform at Gartner Data and Analytics Summit" in AIthority featuring Ryohei Fujimaki, dotData CEO and founder.

dotData is the first and only company focused on delivering end-to-end data science automation for the enterprise.  It recently announced version 1.4 of the enterprise platform. This latest version adds significant enhancements to the platform providing users with deeper insights, increased flexibility, and greater performance. The AI-powered Data Science Automation Platform completely automates the entire data science process from data collection through production-ready models, including feature engineering. Read the full article "dotData to Showcase New Version of its Data Science Automation Platform" in the MarTech Series featuring Ryohei Fujimaki, dotData CEO and founder.

The latest version of the Sublime text editor is now available. Sublime Text 3.2 features Git integration, incremental diffing, new theme functionality, and block caret support.  According to the team, the release builds off of a lot of work done in Sublime Merge, its latest Git client. Additional features include improved performance of file watching, a number of syntax highlighting enhancements, and support for Unicode 11.0. Read the full article "SD Times News Digest: Sublime Text 3.2, dotData 1.4, and GraphQL Foundation’s Collaboration with the Joint Development Foundation" in SD Times featuring Ryohei Fujimaki, dotData CEO and founder.

dotData released its enterprise data science platform, version 1.4.  They have added multiple enhancements providing users with deeper insights, increased flexibility, ease-of-use, and greater performance to meet their specific business goals. The AI-powered platform completely automates the entire data science process, from data collection through production-ready models, including feature engineering. Read the full article on "dotData Boosts AI Updates for its Data Science Automation Platform" in Database Trends & Application featuring Ryohei Fujimaki, dotData CEO and founder.

Data is the oil that greases the cogs of the modern machine.  But, there’s a problem.  Organizations are struggling to gain business insights from this new power. Data: If it’s the next oil, is it renewable or toxic? The Economist magazine famously described data as the new oil.  It certainly has the potential to grease the wheels of the digital economy.  With that potential are both opportunities and threats.  Some go further in saying that data is the new asbestos. Read the full article on "Automating Data Science and Machine Learning for Business Insights" on Information Age, featuring  Ryohei Fujimaki, dotData CEO and founder.