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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 and other emerging technologies.1 He is currently based in San Jose, California, and works as a contract advisor, researcher, and writer on graph and other emerging data technologies since November 2020.1

Career Highlights

  • PwC Experience: For two decades, Alan was one of PwC's principal forecasters on enterprise emerging technology trends, focusing on advanced database management, modeling, and analytics techniques.1

  • Editorial Contributor: He contributed to Data Science Central from September 2021 to January 2025, writing about hybrid or neurosymbolic AI and data layer transformation strategies.1

  • US Navy Background: Alan is a former US Navy cryptologist, aircrewman, and Russian linguist.1

Current Activities

  • Dataworthy Collective: Alan hosts weekly meetings for the Dataworthy Collective.1

  • The Fair Data Forecast: He hosts a monthly podcast on Data Science Central, featuring interviews with experts on making data findable, accessible, interoperable, and reusable.1

  • Writing: Alan regularly contributes to Data Science Central and other TechTarget sites.12

Expertise

Alan's expertise lies in advanced data technologies, particularly knowledge graphs, AI, and enterprise data strategies. He focuses on helping businesses identify and act on emerging technologies relevant to their needs.12

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:

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Location

San Jose, California, United States