George Anadiotis

Contributing Writer

George's got tech, data, and media, and he's not afraid to use them. Coming from an IT background, he's had the chance to learn to play many instruments on the way to becoming a one-man band and an orchestrator: being a Gigaom analyst, serving Fortune 500, startups and NGOs as a consultant, building and managing projects, products and teams of all sizes and shapes, and getting involved in award-winning research among others. George runs Linked Data Orchestration: http://linkeddataorchestration.com

George Anadiotis has nothing to disclose. He does not hold investments in the technology companies he covers.

Latest from George Anadiotis

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Pitfalls to Avoid when Interpreting Machine Learning Models

Pitfalls to Avoid when Interpreting Machine Learning Models

Modern requirements for machine learning models include both high predictive performance and model interpretability. A team of experts in explainable AI highlights pitfalls to avoid when addressing model interpretation, and discusses open issues for further research. Images created by Cristoph Molnar: https://twitter.com/ChristophMolnar/status/1281272026192326656

August 20, 2020 by in AI & Robotics

Public-private data partnerships for road safety

Public-private data partnerships for road safety

SharedStreets is a project of the Open Transport Partnership, a non-profit organization that builds tools for public-private collaboration around transport data blending technology and policy. SharedStreets is building software, digital infrastructure, and governance models to support new ways of managing and sharing data.

April 2, 2019 by in Big Data

Graph query languages

Graph query languages

Unlike the world of relational databases, where SQL is the de facto query language, in graph there is a number of query languages. Images taken from https://developer.ibm.com/dwblog/2017/overview-graph-database-query-languages/

January 22, 2018 by in Data Management

Hybrid transactional analytical processing

Hybrid transactional analytical processing

Traditionally, operational databases and platforms for data analysis have been two different worlds. This has come to be seen as natural, as after all the requirements for use cases that need immediate results and transactional integrity are very different from those that need complex analysis and long-running processing.

December 18, 2017 by in Data Management

Insight Platforms as a Service

Insight Platforms as a Service

Remember how we noted data is going the way of the cloud? While there are no signs of this slowing down, there's another interesting trend unraveling, the so-called Insight Platforms as a Service (IPaaS). The thinking behind this is simple: if your data is in the cloud anyway, why not use a platform that's also in the cloud to run analytics on them, and automate as much of the process as possible?

December 18, 2017 by in Data Management

Streaming becomes mainstream

Streaming becomes mainstream

The endless streams of data generated by applications lends its name to this paradigm, but also brings some hard to deal with requirements to the table: How do you deal with querying semantics and implementation when your data is not finite, what kind of processing can you do on such data, and how do you combine it with data from other sources or feed it to your machine learning pipelines, and do this at production scale?

December 18, 2017 by in Data Management

The machine learning feedback loop

The machine learning feedback loop

The pace of change is catalyzed and accelerated at large by data itself, in a self-fulfilling prophecy of sorts: data-driven product -> more data -> better insights -> more profit -> more investment -> better product -> more data. So while some are still struggling to deal with basic issues related to data collection and storage, governance, security, organizational culture, and skillset, others are more concerned with the higher end of the big data hierarchy of needs.

December 18, 2017 by in Data Management

Moving up the analytics stack

Moving up the analytics stack

As descriptive and diagnostic analytics are getting commoditized, we are moving up the stack towards predictive and prescriptive analytics. Predictive analytics is about being able to forecast what's coming next based on what's happened so far, while prescriptive analytics is about taking the right course of action to make a desirable outcome happen.

December 18, 2017 by in Data Management