The arrival of #AI #agents creates urgency around the need to guide and govern them.
After 15 years of building reliable AI solutions for banks and other enterprises, Jacobus Geluk sees a standards-based #dataProduct marketplace as key to #AIagent success.
His proposed new #useCaseTree specification articulates the business needs that data products address, complementing his earlier work on the #DPROD data-product description standard.
The Treasury Board's policy suite is conceptually a giant graph structure, but is frustratingly resistant to automated analysis.
Some annoyances:
The policy suite straddles tbs-sct.gc.ca and canada.ca and policies often draw their authorities from material on laws-lois.justice.gc.ca
There is frustratingly little common structure you can rely on. If you think you found a structure, you just need to see a few more policies
Links between policies or to laws rarely link to relevant sections
Only a few policies have an XML data representation, most are available only as HTML, making web scraping the most reliable approach
Markers indicating sections, clauses etc. are not consistent across HTML documents making web scraping extremely annoying
Multiple requirements often occur in a single ("and")
Enabling programmatic analysis of policy would be broadly valuable both inside and outside government.
This should be an #opendata #dataproduct but it seems like these documents are largely treated like marketing material: if it looks OK in the browser it's done.
What do you think of the following description of Flowcore (the data management platform)?
The platform provides you with event streaming and event sourcing in a single, easy-to-use service. Data flow and replayable storage. Designed for developers at data-driven startups and enterprises that aim to stay at the forefront of innovation and growth.
#data #eventsourcing #eventstreaming #datafabric #dataproduct
The hype cycle has lessons for #product
https://www.superversive.co/blog/hype-cycle-lessons
#DataGovernance #DataProduct #SystemsThinking #HypeCycle #LessonsLearned #ProductManagement
One critical question business stakeholders should be asking when formulating strategy (independent of LLMs) is about the source, utility, and quality of data. Data governance should be Step 0 and basic information hygiene for any organization relying on data for business-critical workflows (which
I think this is really “data product management” 🤷🏻♂️
I've been too long without posting. There's been a lot going on in data engineering, the modern data stack, the business(es) of data, … not to mention FTX and ChatGPT (but, of course, you already know about all of that.)
https://medium.com/@rhm2k/resonance-calendar-for-50-2022-3341b2b94339
"A Data Product is set of prepared data or information (and hence specifically not raw data) that is ready to be consumed by a wide set of consumers."
Willem Koenders provides insights into one of the fundamental concepts of contemporary data engineering.
https://medium.com/@koendit/whats-the-big-deal-about-data-products-26ac347b7d7a
Larry Swanson
Mike Williamson
💧🌏 Greg Cocks
Olavur Ellefsen
Coach Pāṇini ®
Sam Panini
Theo
RIch Miller
