Data Mesh Radio

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Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here

Jarkko's LinkedIn: https://www.linkedin.com/in/jarkkomoilanen/

Open Data Product Spec: http://opendataproducts.org/#open-data-product-specification

Jarkko's Data Product Business website: https://www.dataproductbusiness.com/

This episode is part of the Data Innovation Summit Takeover week of Data Mesh Radio.

Data Innovation Summit website: https://datainnovationsummit.com/; use code DATAMESHR20G for 20% off tickets

Free Ticket Raffle for Data Innovation Summit (submissions must be by April 25 at 11:59pm PST): Google Form

Scott interviewed Jarkko Moilanen, a Data Economist and Country CDO ambassador for Finland. Jarkko will be presenting on "Data Monetization and Related Data Value Chain Requires Both Data Products and Services" on May 6th in track M6.

Jarkko is approaching measuring the value of data and then trying to extract that value from data from many different angles. He is thinking about data products, data as a service, data as a product, etc.

Per Jarkko, treating data like a product can apply a LOT of the learnings from the API revolution - this time around we can skip a lot of the sharp edges. APIs are about an interface to value creation - how can we treat data products the same way?

We discussed the difference between return and return on investment. A data initiative may have a very high return but if the investment to get that return is too high, it's a bad initiative. How do we get to figuring out what quality level we need to solve our challenges - there is no reason to go for 5 9s quality if that doesn't move the needle.

Jarkko coined a new concept on the call - the half life of data value. For a large percent of data, Jarkko believes the value of the data starts to fall considerably over a relatively short period of time. How can we extract the value when it is most valuable? If

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Please Rate and Review us on your podcast app of choice!

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here

Daniel's LinkedIn: https://www.linkedin.com/in/danielengberg/

This episode is part of the Data Innovation Summit Takeover week of Data Mesh Radio.

Data Innovation Summit website: https://datainnovationsummit.com/; use code DATAMESHR20G for 20% off tickets

Free Ticket Raffle for Data Innovation Summit (submissions must be by April 25 at 11:59pm PST): Google Form

Scott interviewed Daniel Engberg, Head of AI, Data, and Platforms at Scandinavian Airlines. Daniel will be presenting on "Structuring an Enterprise-Wide Data Organization" on May 6th in track M4.

A key point Daniel made right away was that organizational structure should be tailored to accomplishing your goals - so we have to know what those goals are first. What are the capabilities we need to meet those goals? "Traditional" companies are often locked into their structure - silos by competence; so data engineering in one silo, marketing in another, sales in another, and so on. Daniel is interested in figuring out how we can split up the competencies to create cross-functional, cross competency teams but not cause chaos to the organization as a whole.

Daniel gave an example of creating a cross functional team early in the pandemic as there were some very big threats to the business - being an airline when no flights are happening is a scary place. The cross-functional team was able to move so much more quickly than the way the company tackles challenges when it is business-as-usual, achieving their goals in a few days instead of what typically would have taken months. This cross-functional work also created new information sharing connections across the entire company which continues to create additional value.

What Daniel learned from that experience, he is trying to replicate as best he can to make it the new business-as-usual instead of a one-off. As the head of AI, Data, and Platforms, he is working to infuse members from his team directly into more projects so they can be part of the teams and decisions instead of handling requests after decisions are made. It

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Please Rate and Review us on your podcast app of choice!

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Please Rate and Review us on your podcast app of choice!

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here.

Ellie's LinkedIn: https://www.linkedin.com/in/sellieyoung/

Philippe's LinkedIn: https://www.linkedin.com/in/hoijnet/

Philippe's Twitter: @hoijnet / https://twitter.com/hoijnet

DFRNT website: https://dfrnt.com/

Knowledge Graph Conference website: https://www.knowledgegraph.tech/

Free Ticket Raffle for Knowledge Graph Conference (submissions must be by April 18 at 11:59pm PST): Google Form

In this episode of the Knowledge Graph Conference takeover week, special guest host Ellie Young (Link) interviewed Philippe Höij, Founder at DFRNT.

At the wrap-up, Philippe mentioned that data architects should be able to communicate in ways other than PowerPoint. We need new and better ways to express ourselves, and the way things are connected. We will always need metadata around our data, we need text to express our ambiguity; but we don't have great ways to express things that are slightly ambiguous - not fully formed but also mostly known. A good tool allows to more easily query your model of the world to iterate and increment on it. That is where knowledge graphs can be the most helpful. Ellie responded, "It's not difficult, it's just complicated."

Philippe shared his journey towards knowledge graphs, especially thinking about the AIDITTO project he and a team built out of the "Hack the Crisis Sweden" event in 2020 around COVID-19. He needed a way to prototype, visualize and collaborate on data and the connections between data at scale. A regular data model does not convey enough information about what the data is and how it relates.

Ellie then shared some insight into the difficulties around collaborating on data across organizations and people in her climate change work at Common Action....

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Scott gets grumpy and calls out some misbehaving vendors. And adds some fun new music to the episodes.

Please Rate and Review us on your podcast app of choice!

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Please Rate and Review us on your podcast app of choice!

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here.

Ellie's LinkedIn: https://www.linkedin.com/in/sellieyoung/

Olivier's LinkedIn: https://www.linkedin.com/in/olivierwulveryck/

Knowledge Graph Conference website: https://www.knowledgegraph.tech/

Free Ticket Raffle for Knowledge Graph Conference (submissions must be by April 18 at 11:59pm PST): Google Form

In this episode of the Knowledge Graph Conference takeover week, special guest host Ellie Young (Link), founder of Common Action, interviewed Olivier Wulveryck, Senior Consultant and Manager at OCTO Technology.

In the first two thirds of the interview, Olivier and Ellie chatted about a lot of concepts specifically around data mesh and then they linked in the concepts around knowledge graph in the last third.

For Olivier, a knowledge graph is the map for the data that is available - each data product or node in a data mesh is the representation of the knowledge within the organization. The knowledge graph is the abstraction of that knowledge across the data mesh - a logical representation on top of the data mesh nodes to help people make sense of the data mesh.

Currently, Olivier is working with a client sharing their data in a data marketplace. They are working on implementing a knowledge graph on that but not on their internal data. If they are seeing value from applying a knowledge graph externally, they may apply to their internal usage.

Olivier shared his view that it's easier to start with a data mesh than a knowledge graph - any first steps with a data mesh will bring you value. It is not the same with knowledge graphs - you have to do more work to get to value with knowledge graphs.

Olivier previously worked on the operational side of software engineering. He realized they had lots of data sitting in databases but the data was just a consequence - it was state data, there was no temporal dimension. He wanted...

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Please Rate and Review us on your podcast app of choice!

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here.

Ellie's LinkedIn: https://www.linkedin.com/in/sellieyoung/

Veronika's LinkedIn: https://www.linkedin.com/in/veronikahaderlein/

Knowledge Graph Conference website: https://www.knowledgegraph.tech/

Free Ticket Raffle for Knowledge Graph Conference (submissions must be by April 18 at 11:59pm PST): Google Form

In this episode of the Knowledge Graph Conference takeover week, special guest host Ellie Young (Link) interviewed Veronika Haderlein-Høgberg, PhD. Veronika was employed at Fraunhofer-Gesellschaft at the time of recording but was representing only her own view and experiences. She was invited for her special mix of both data mesh and knowledge graph know-how.

At Fraunhofer-Gesellschaft, Veronika's employer up until recently, she and team were currently implementing a knowledge graph to help with decision support for the organization. And previously, Veronika worked on a data mesh-like implementation as part of the Norwegian public sector at the Norwegian tax authority before the data mesh concept was really congealed into a singular form by Zhamak.

Veronika and Ellie wrapped the conversation with a few key insights: to share data, groups need to agree on common standards to represent it, and they also need to be able to share information with each other about that data into the future. To develop these initial data standards, and to build the relationships to coordinate around that data long term, different departments in the enterprise have to converse with each other. Building conversations across departments requires also building trust, and for this curiosity is a crucial ingredient, both on the individual level, but also at the domain and organizational levels. If people don't feel comfortable asking questions, they can’t understand each other’s perspectives well enough to contribute to that shared context.

What does...

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Please Rate and Review us on your podcast app of choice!

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

Knowledge Graph Conference website: https://www.knowledgegraph.tech/

Free Ticket Raffle for Knowledge Graph Conference (submissions must be by April 18 at 11:59pm PST): Google Form

Thank you to our contributors! You can find additional introductory resources below.

Contributors and their contact info:

Karen Passmore, CEO and Founder of Predictive UX

Karen LinkedIn: https://www.linkedin.com/in/karenpassmore/

DMR Episode 30

Xhensila Poda, Machine Learning Engineer at CARDO AI

Xhensila's LinkedIn: https://www.linkedin.com/in/xhensilapoda/

Jens Scheidtmann, Lead Architect at Bayer

Jens' LinkedIn: https://www.linkedin.com/in/jens-scheidtmann/

Juan Sequeda, Principal Scientist at Data.world

Juan's LinkedIn: https://www.linkedin.com/in/juansequeda/

DMR Episode 14

Steve Stesney, Senior Product Lead and Data Practice Lead at Predictive UX

Steve's LinkedIn: https://www.linkedin.com/in/stephenstesney/

DMR Episode 30

Tim Tischler, Principal Engineer at Wayfair

Tim's LinkedIn: https://www.linkedin.com/in/timtischler/

DMR Episode 43

Further Introductory Resources (provided by Ellie Young and Juan Sequeda):

What is a Knowledge Graph video by Martin Keen: https://www.youtube.com/watch?v=y7sXDpffzQQ

Knowledge graphs: Introduction, history, and perspectives (paper):

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Please Rate and Review us on your podcast app of choice!

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/

Please Rate and Review us on your podcast app of choice!

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.

Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here

GoDataDriven Data Mesh Webinar: https://godatadriven.com/topic/webinar-data-mesh-9-feb-2022-thanks/

GoDataDriven Self-Service Whitepaper (info-gated): https://godatadriven.com/topic/data-democratization-whitepaper/

Steven's LinkedIn: https://www.linkedin.com/in/stevennooijen/

Guillermo's LinkedIn: https://www.linkedin.com/in/guillermo-s%C3%A1nchez-dionis/

In this episode, Scott interviewed two people from the European data consultancy GoDataDriven - Steven Nooijen, Head of Strategy, and Guillermo Sánchez, Analytics Engineering Tech Lead.

Guillermo started off by talking about how for the last ~3 years, he was seeing the data engineering team as the bottleneck before data mesh came onto the scene. For Steven, they were seeing lots of companies that were building out data platforms, especially data lakes, and then not really getting the promised benefits so data mesh made sense.

All agreed data mesh is not right for every company and then mentioned some good signs that an organization should consider data mesh. Guillermo pointed to a lot of the usual suspects: size of company, size of data team, how many data consuming teams do you have, how many data sources do you have, etc. He then gave a specific example: if you have a data analyst in a consuming domain that has to wait more than 1 week for data, there is a bottleneck somewhere. Is it centralization? Not sure but time to investigate and that might be where you start to consider data mesh.

Steven gave the example of an even earlier indicator that bottlenecks are occurring: teams start to hire their own data people rather than leverage the central team. Guillermo also pointed to the rise of consuming teams getting direct data access from producing teams instead of going through the data team.

Guillermo made a very crucial point: data mesh is really about interfaces. People talk about data...