Managerial Challenges with Deep Learning Systems

…by Richard Hackathorn, Bolder Technology [Email LinkedIn About]

I have spent my professional career crawling around large IT systems as a university professor and as a software entrepreneur. For the last twenty years, I have been an independent industry analyst in Business Intelligence, documenting innovative applications pushing current technology. Over the past five years, I shifted focus to Business Analytics and, currently Deep Learning systems (using neural network technology) within typical corporations. Hence, I am dba BizSmartAnalytics.

In Spring 2018, I completed the 5-course Coursera specialization in Deep Learning, taught by Andrew Ng of Stanford. My a-ha moment was the discussions of analytics showing predictions that exceed human-level performance. I concluded that: We humans have created tools smart enough to out-smart ourselves! I wrote my reflections in this article, the latter half of which addresses this point.

  • Deep Issues Lurking Within Deep Learning (link, pdf)

Taking the social and ethical issues to heart, I started to think how IT managers should approach analytical systems enabled by Deep Learning. The issues just exploded in my mind. Hence… Most IT professionals are woefully unprepared to manage properly or even to understand Deep Learning as applied to enterprise systems. Further, the future challenges will involve managing super-intelligence systems at scale! …a challenge for which no one is prepared.


These insights prompted an article series about

  • How Managers Should Prepare for Deep Learning (link, pdf)

The objective is making Deep Learning relevant and useful to the managers who fund analytical systems, who evaluate their performance, and who are held accountable for their impacts.

The series is published in Towards Data Science. The first two articles are available here…

In addition, related articles to this series are…

  • Vendors — Define Your Usage of #AI (link, pdf)
  • Confronting Deep Learning Systems: How Much Things Have Changed (link)


BizSmartAnalytics is presenting the following talks:

  • DVEM 2018, Are You Prepared for Deep Learning, Genesee CO, 2018-10-03 (pdf)
  • Teradata Analytics Universe, How is Deep Learning Valuable for Your Company, Las Vegas NV, 2018-10-17 (pdf, unable to be presented)


BizSmartAnalytics is offering the following services:

  • Mentoring session for peer groups of managers involved with DL-enabled systems, along with providing one-on-one advice. This service is performed via the Patreon BizSmartAnalytics. Richard organizes and mentors peer groups, which meet online for an hour, twice monthly, plus one hour of weekly preparation. Group size is limited to 5 to 7 persons to facilitate interaction. Group composition is spread across business functions and even industries to maximize a diversity of perspectives. Richard shares the latest concepts, research, and trends occurring in rapidly-evolving DL-enabled analytics. By stressing mentoring-the-mentors, group members are equipped to mentor colleagues within their organizations. More details are given in  Guidelines for Peer Groups.
  • Custom 1-3 day hands-on workshops for training IT managers about DL-enabled systems. Fees vary depending on duration, class size and travel. Customization to specific use cases relevant to your company is a suggested option. Client supplies the training facility. Ask for a free consultation and proposal.


BizSmartAnalytics is conducting interviews with managers who are involved with enterprise analytical systems using neural network technology. The interviews focus the emerging managerial issues and challenges facing managers at all levels. Interviewees do not need in-depth technical knowledge. The interviews are informal and unstructured. Interviews can optionally be conduct as anonymous. These questions are to stimulate discussion:

  • Describe your management involvement with analytical systems and deep learning.
  • What is the motivation for using neural network technology? Tactical or strategic or mixture?
  • What are the issues that you are encountering or expect to encounter? Any surprises?
  • Are these issues unique to deep learning? Are the issues technical or managerial?
  • Do you have suggestions for best practices or lessons learned for similar professionals?

Past Studies and Articles

Links to 2015-2017 studies and articles are here.