As companies embrace AI in their business, they are confronting the technologies of Deep Learning systems at scale. That is like entering a dark massive cave — wonderment and excitement, along with the fear that you have no clue what you are getting into!

Mostly Clueless in IT

To a colleague, I recommended the blog by Paco Nathan [1] that summarizes recent Strata surveys. The colleague is a respected expert in Business Intelligence and Data Warehousing.

His reaction was, “How much things have changed and how much we do not know!”
…referring to IT professionals confronting the emerging Deep Learning systems.

In my words…

At the IEEE VIS 2018, researchers from diverse universities presented a paper describing a new toolkit for rapid deployment of immersive data visualizations, called DxR (Data visualization in miXed Reality). [1]

As an industry analyst, I listen to numerous vendor briefings. As is increasingly common in these briefings, vendors use the acronym AI frequently and casually to describe various capabilities of their products or services.

In a recent briefing, this practice was particularly obvious with a major vendor. Their phrases were: AI + BI; with AI …power to explore their data; with AI …becomes smarter; AI makes data accessible; AI … fundamentally changes how analytics are used; driven by AI; infused with AI. And, all of this within the first 13 slides!

Note: This is a big red flag for me…

TL;DR — see Summary at end for quick overview.

This article is the second in a series about the Managerial Perspectives on Deep Learning, which are targeted toward managers who are responsible for or involve with analytical systems enabled by Deep Learning (DL) using artificial neural network technology. They faced confusing concepts and unique challenges when dealing with these systems. This article focuses on the New Values that suggest different approaches for evaluating the value realized by an organization from DL-enabled analytics. At the end of each section, a Take-Away offers practical suggestions for managers.

Toward Intelligent Effectiveness

The value that large IT…

This article is the first in a series about the Managerial Perspectives on Deep Learning, which are targeted toward managers who are involve with or responsible for analytical systems enabled by Deep Learning (DL) using artificial neural network technology. They faced confusing concepts and unique challenges when dealing with these systems. This article focuses on the New Paradigms that guide the thinking of these managers about the basic nature of DL. [1]

I have spent my professional career crawling around large information technology (IT) systems as a university professor, software entrepreneur, and industry analyst. Over the last five years, that…

Reflections on Coursera Specialization on Deep Learning by Andrew Ng

TL; DR — I highly recommend this MOOC specialization. However, be aware! It is more than yet-another certificate. Deeper issues lurk beneath the surface, causing you to think deeper about your responsibilities using this new technology.

Note to reader (2018–06–07): Article has two parts. First part describes the MOOC course for potential students of DL. Second part explores the “So What?” implications, especially the responsibilities for practitioners of DL.

I recently finished the Coursera specialization on Deep Learning (DL) with its 5 courses, 14 class weeks, 180 videos, 29 labs…

This article is directed to analytic-mature technology-savvy managers who deal with corporate IT infrastructure and strategy. The article explains quantum computing in terms relevant to IT managers and suggests future business opportunities to exploit this new technology.

Yet another wave will be impacting information technology (IT) globally. It is called quantum computing (QC). The good news is that IT managers do not need to do anything for another 3–5 years. However, they should now start thinking differently about QC, especially if they are part of an analytics-driven corporation. The goal of this article is to answer these questions:

• What should…