News Category: Media Coverage

Machine learning (ML) is fast becoming a major area of interest for organizations of all types.  It is helping to power everything from cybersecurity defense, to recruitment, and chatbots. While discussion about the benefits has piqued the interest of many businesses, deciding how to implement ML can be overwhelming. Siliconrepublic.com spoke to Dr Ryohei Fujimaki, founder and CEO of dotData, about the key differences between black-box and white-box ML models, as well as the growing calls for transparency in data science. Read the full article "What are the Benefits of White Box Models in Machine Learning?" on Silicon Republic featuring Ryohei Fujimaki. Related Articles

Data quality and the amount of effort required to address data quality issues are usually the biggest challenges of the data modeling process, said Ryohei Fujimaki, founder and CEO of dotData.  "The traditional process to deal with data quality involves a lot of hard-coded business logic which makes the data pipeline very difficult to maintain and to scale." Read the full article on "How to Navigate the Challenges of the Data Modeling Process" in TechTarget’s SearchDataManagement featuring Ryohei Fujimaki. Related Articles

There are areas of the data center that may be out of reach from automation for a myriad of reasons. “Much of database automation is about storing data, and maintaining data, but less about extracting significant business insights out of the data stored in the database,” said Ryohei Fujimaki, CEO of dotData. “As we all know, data is only as valuable as the business insights it produces. From that perspective, the current database automation technology needs improvement in this area.”Read the full article on Database Trends and Applications featuring Ryohei Fujimaki, dotData CEO and founder.

Machine learning may seem like a mysterious creation to the average consumer, but the truth is we’re surrounded by it every day. ML algorithms power search results, monitor medical data, and impact our admission to schools, jobs, and even jail. Despite our proximity to machine learning algorithms, explaining how they work can be a difficult task, even for the experts who designed them. In the early days of machine learning, algorithms were relatively straightforward, and not always as accurate as we’d like them to be.  As research into machine learning progressed over the decades, the accuracy increased, and so did the complexity. Since the techniques were largely confined to academic research and some areas of industrial automation, it didn’t impact the average Joe very much. Read the full article in datanami on "Making ML Explainable Again" featuring Ryohei Fujimaki, dotData CEO and founder.

According to a recent press release, “dotData, the first and only company focused on delivering end-to-end data science automation and operationalization for the enterprise, today announced the launch of dotDataPy, a lightweight and scalable Python library that enables advanced users to access dotData’s data science automation functionality, including AI-powered feature engineering and automated machine learning. With just a few lines of code, data scientists can now create, execute and validate end-to-end data science pipelines.” Read the full article "dotData Further Accelerates Data Science Automation with the Launch of dotDataPy" on DATAVERSITY featuring Ryohei Fujimaki, dotData CEO and founder. Related Articles

dotData, a provider of end-to-end data science automation and operationalization, is releasing dotDataPy, a lightweight and scalable Python library that enables advanced users to access dotData's data science automation functionality. With just a few lines of code, data scientists can now create, execute and validate end-to-end data science pipelines. dotDataPy can be easily integrated with Jupyter notebooks and other Python development environments, enabling users to fully leverage the advanced Python ecosystem, including rich visualization (e.g. Matplotlib and Plotly), state-of-the-art machine learning/deep learning tools (e.g. scikit-learn, Spark MLlib, PyTorch, and TensorFlow), and flexible DataFrames (e.g. pandas and PySpark). Read the full article on Database Trends & Applications, which features Ryohei Fujimaki, dotData CEO and founder.

Data science is now a major area of technology investment given its business impact.  Business impact may be realized via: customer experience, revenue, operations, supply chain, risk management, and multiple other business functions. However, recent research indicates that although digital transformation and AI journeys are key initiatives, companies are struggling to get them off the ground.  One of the key challenges is hiring the right team including a scarce commodity -- the data scientist.One of the most noticeable trends to overcome this challenge, and to accelerate enterprise data science is data science democratization.  This process would empower citizen data scientists (such as business analysts and business intelligence engineers) to solve complex analytic problems, making it possible for a broader range of practitioners to execute data science projects.  Although this concept has been widely discussed, many enterprises have been struggling to truly democratize data science. This article discusses best practices for…

To overcome the shortage of data scientists, many organizations are turning to democratizing data science. Here's what you need to know.Read the full article "How and Why Your Enterprise Should Democratize Data Science" on TDWI's Upside.

Augmented analytics promises to bring BI to a much larger audience of business users.  Early implementations could prove useful in answering simple questions, like how much inventory an organization should plan to stock.  However, a higher level of data literacy skills is still required for more complex types of analysis. For example, when Gartner invited vendors to apply their augmented analytics tools against a sample data set at a BI Bake Off in 2016, only one -- Salesforce Einstein -- allowed business users to accurately identify the root driver.  In this example, the tools were presented with a data set relating to college students to determine what factors lead to higher long-term earnings.  Most of the tools simply reinforced the inaccurate bias that earnings were correlated with Ivy League colleges, with the main driver as parents' income. Augmented analytics greatly reduces the need for data literacy in order to extract…

We can expect that AI will lay the foundation for an acceleration in innovation over the next few years.  It is expected to boost some economic sectors while completely restricting some industries.  It might not be quite ready to take our jobs, but what can we expect in 2019? DZone talked to a number of people enmeshed in the challenges to find out, including dotData CEO and founder, Ryohei Fujimaki.  Read the full article "Your 2019 AI Predictions" featuring Ryohei's forecast.

Augmented data discovery is an emerging BI capability.  It can automatically prepare and organize enterprise data for self-service BI.  This is particularly challenging for unstructured data from sources like email, social media channels, IoT feeds, and customer service interactions. Traditional BI tools have supported basic capabilities for joining, manipulating and transforming structured data. Augmented data discovery can build on these basic capabilities.  It uses augmented data preparation and automated pattern discovery for self-service BI, according to research firm Gartner Inc.  Augmented data preparation streamlines processes for data profiling, managing quality, cleaning data, modeling, enriching, and labeling metadata in a manner that supports reuse and governance. Automated pattern detection builds on traditional BI tools to support complex, large data sets with more than 10 columns. Augmented data discovery focuses on providing insight for citizen data scientists. In Gartner's view, these are similar but somewhat different to augmented data science platforms used…

As they continue to bring readers year-end roundups and predictions for 2019, the KDnuggets team reached out to a number of influential industry companies for their thoughts, posing this central question: What were the main developments in AI, Machine Learning, Analytics & Data Science in 2018, and what key trends do you expect in 2019? Read the full article "Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019" including predictions from dotData CEO, Ryohei Fujimaki.