What impact will #COVID19 have on #enterprise investments in #ArtificialIntelligence? Our CEO Ryohei Fujimaki recently shared his thoughts with @TechTarget’s @markrlabbe: https://bit.ly/3gDkKKk #AI #DataScience #DigitalTransformation
- Carl Bowen
- July 11, 2019
We look into why data science projects fail at such an alarming rate, and what data science teams can do to increase their success.
- March 31, 2019
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.
- Carl Bowen
- February 7, 2019
Data science is now a major area of technology investment given its business impact. Business impact may be realized via:
- customer experience,
- 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 enterprises to follow when democratizing data science.
Read the full article “How and Why Your Enterprise Should Democratize Data Science” in TDWI, featuring Ryohei Fujimaki, dotData CEO and founder.