fbpx

How Will Automation Tools Change Data Science?

By dotData

Data science is now a major area of technology investment, given its impact on: customer experience, revenue, operations, supply chain, risk management, and multiple other business functions. Data science enables a data-centric decision-making process for organizations.  It is accelerating digital transformation and AI initiatives.  According to Gartner, Inc. only 4 percent of CIOs have implemented AI, and only 46 percent have plans to do so.While investments continue to grow, many enterprises find it increasingly challenging to implement and accelerate data science practices.  This article provides an overview of recent trends in machine learning and data science automation tools.  It also  addresses how those tools will change data science. Read the full article "How Will Automation Tools Change Data Science" on KDnuggets, featuring Dr. Ryohei Fujimaki, CEO and founder of dotData.

2019 Predictions for Machine Learning

By dotData

Manufacturers are adept in gathering data from their operations, but don’t always know how to make the best use of the data collected.  Data science automation can provide the needed guidance. Dr. Ryohei Fujimaki, CEO and founder, of dotData, joins us to predict how machine learning will change data science over the next year.  Read the full article "2019 Predictions for Machine Learning" on Manufacturing.net for Ryohei's five (5) predictions for machine learning.

A Vision of Rapid, High-Quality Data Analysis for All Businesses

By dotData

Leveraging Data to be Competitive It is becoming increasingly important for enterprises to leverage data to be competitive. Yet, there are three challenges related to embracing data utilization that all businesses share: it takes time, advanced skills, and expertise. Together, these challenges make it difficult for enterprises to fully leverage their data for business growth.  Data analytics is not simply prediction by machine learning. Rather, it is a process involving many different steps, including: data preparation, feature engineering, machine learning, visualization, and model operationalization. Until now, completing this process for just a single project would have taken months. Moreover, a wide variety of highly-skilled personnel are needed for each step – such as domain experts, data scientists, data engineers, and BI engineers.  Additionally, processes and outcomes have tended to be highly dependent on the experience and intuition of each individual. Feature Engineering Made Easy For feature engineering in particular, it…