The Top 5 AI & Machine Learning Trends for 2021 And Beyond

Five Common Mistakes to Avoid for Digital Transformation Success

March 15, 2021

Ryohei Fujimaki, Ph.D., founder and CEO of dotData explains the five common mistakes to avoid for digital transformation success with eWEEK.

Companies are forging ahead with digital transformation at an unprecedented rate and anticipate ROI increases of between 10% and 20% in the next year. Research shows that 71% of all digital transformation initiatives don’t reach their goals. This article describes five mistakes that leaders can avoid to beat the odds. Recent research shows that empowered Chief Data Officers, backed by solid mandates and executive support, are essential in successful digital transformation.

Business strategies are constantly changing due to market forces, such as regulation, supplier, customer, or competitive pressure. The data infrastructure needs to be in place for companies to be successful with digital transformation. IT needs to evolve and have the correct data processing architecture and legacy IT infrastructure to support AI solutions.

If the business is new to AI and ML, start with less ambitious projects because data science projects take months to complete and require visibility into value. Start by selecting low-hanging fruits to make teams data-savvy and, ultimately, result in large projects’ success. Many enterprises use automated machine learning (AutoML) tools to automate data science processes. These tools make AI more accessible and easy to use. A wide range of platforms can support AI initiatives, but next-generation platforms offer the most comprehensive support.

Digital transformation is less about technology and more about people and culture. It is crucial to have support from all levels and be supported by the executive leadership team.

Read the entire article at eWeek today.


dotData Inc.

dotData Automated Feature Engineering powers our full-cycle data science automation platform to help enterprise organizations accelerate ML and AI projects and deliver more business value by automating the hardest part of the data science and AI process – feature engineering and operationalization. Learn more at, and join us on Twitter and LinkedIn.