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Alan Morrison

Advanced data technologies consultant and writer

Alan Morrison is a freelance advisor, writer, public speaker, and podcaster specializing in knowledge graphs, hybrid AI, and other emerging technologies.1 He is based in San Jose, California, and has been self-employed since December 2020.1

Morrison's career spans over four decades, including:

  1. 20 years at PwC as a principal forecaster on enterprise emerging technology trends1
  2. Analyst and forecaster for the wireless semiconductor market at Strategies Unlimited1
  3. US Navy cryptologist, aircrewman, and Russian linguist1

Currently, Morrison:

  • Hosts the Dataworthy Collective's weekly meetings
  • Produces "The Fair Data Forecast," a monthly podcast on Data Science Central
  • Contributes regularly to Data Science Central and other TechTarget sites1

His educational background includes:

  • Bachelor of Science in Liberal Arts from Excelsior College
  • Master of Arts in Russian and International Policy Studies from Middlebury Institute of International Studies at Monterey
  • MBA from Santa Clara University1

Morrison is recognized as an industry expert, having spoken at various conferences such as the Data Centric Architecture Forum, SEMANTiCS, and Enterprise Data World.1 His LinkedIn username is indeed alanmorrison.1

Highlights

Aug 28 · linkedin

Typical SaaS and other enterprise applications do three things:

A means for data input

A means for information output

Connections to input and output workflows

For that reason, each application becomes one of many black boxes, each of which duplicates or overlaps the others’ functions. Moreover, each new SaaS adds a new and different data model to the mix, implying a continually rising integration workload for those who are trying to use application data downstream in a unified fashion.

Ironically, by indiscriminately subscribing to more and more SaaSes, enterprises are adding another layer of complexity on top of an already challenging situation.

Knowledge graphs, by contrast, allow the visibility and shareability of common functions and data. Knowledge graph architecture encourages the elimination of functional and data duplication.

My latest post for Data Science Central: A 12-step FAIR data fabric program for recovering application addicts https://lnkd.in/grcvxxry

Jul 7 · linkedin

A good enterprise knowledge graph does these things well:

  • Helps an organization find actionable answers to hard questions.

  • Decentralizes the access to those answers so that more people can take informed actions and solve hard problems.

  • Decentralizes the access to shared business rules and contexts so that there's broad agreement and alignment on how to take action.

Such a knowledge graph starts with thoughtful, methodologically sound research and data collection, including how to find the right questions to ask.

Good knowledge depends on sound divergent and convergent research.

If you're not harnessing the power of a good knowledge graph, you're still looking for your keys under the lamppost because that's where your only light is.

Illuminate more places to get better answers quickly and make better decisions.

From my 2022 Quora answer at:

Related Questions

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Alan Morrison
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Location

San Jose, California, United States