Helping organizations adopt data products. CTO & Co-founder | DataOS: https://xitter.cf/t.co/H5jdGWnvRp
Indore, India
Joined July 2023
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Animesh Kumar retweeted
The Data Product Testing Strategy: Handbook
Goals & Components of the Data Product Testing Strategy, Testing Integration in the Data Product Lifecycle, Testing Facilitation Strategies & Technologies, and more!
moderndata101.substack.com/p…
Cracking the Data Product Code at #CDOIQ 2024
Excited to be back at @CDOIQ_Program next week!
For years, we've been building a solution to bring a product mindset to data. Now, with the data community embracing "#DataProducts", our efforts are resonating!
Sneak peek...
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Our focus on data product adoption has been about making the process smoother and less overwhelming.
We've created user journeys, backward business-value journeys, and adoption playbooks – all designed with the data user in mind.
Heading to CDOIQ? Here's the agenda: cdoiq2024.org/agenda/
Catch my session on Model-First Data Products at 4:45 PM EDT on July 17th! We'll be diving deep into some cool stuff.
Who else is attending? Let's connect!
I'm looking forward to meeting you all!
#DataProducts #CDOIQ
ALT Session 15-F: Cracking the Data Product Code: The Model-First Approach to Building Impactful Data Products
Animesh Kumar retweeted
🌟 Data Expert of the week: @LedouxCharlotte! 🌟
Meet Charlotte Ledoux — The Data and AI Governance expert with over 15 years of hands-on experience, shaping the journey of large enterprises.
Checkout her profile: moderndata101.com/network/ch…
Strong data products rely on strong Data Governance!
It's no surprise that many Data Product features (DAUNTIVS) align with data governance principles.
Huge thanks to Modern Data 101 for sharing this fantastic resource by Charlotte Ledoux!
Here's a quick glance...
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ALT Unified Platform Representation: Standardised interfacing with all existing tools, declarative data management, e2e governance, orchestration, and metadata management.
Charlotte's approach to governance is built for decentralization.
It is a game-changer, reflecting game theory's concept that it outperforms centralization in competitive environments.
This is especially true in data ecosystems where domains compete for resources & value.
ALT Specific Roles Within a Domain
Key questions to consider:
1) How does governance fit into & enable a decentralized stack?
2) Does it resemble a product approach? If so, how?
3) How can a product approach benefit existing governance goals?
This article hits all the right angles:
moderndata101.substack.com/p…
Ever wonder how we got to the semantic layer?
In today's data world, it's crucial, but did you know it's actually an evolution of a foundation we've always had?
Find all about it here:
moderndata101.substack.com/p…
Here's a short summary...
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Early data efforts used "semantic approaches" – essentially ways to make data meaningful.
We've come a long way, but the core concept remains!
Back then data cubes & marts emerged as workarounds, offering a more organized way to access information.
These paved the way for BI tools we know today.
The semantic layer now, in essence, acts as a central translator, simplifying data access for various tools & users in a reusable & adaptable manner.
Find the works here:
moderndata101.substack.com/p…
Got questions? Let's chat!
ALT The Reliable Semantic Stack
Well it does act as the "lever" to the data domain's north star metric "labs" 🥂
Love the way you're propagating & building the idea!💯
@_abhisivasailam
Classic sunk cost fallacy! Data analysts feel it too.
The solution:
Automated data validation & early exploratory analysis
This allows analysts to identify potential issues early (like digging in the wrong place) and course-correct before significant time is wasted.
Re: sunk cost fallacy:
First order effect: I dug a hole in the wrong place, but I don't want to start over.
Second order effect: Here's why we actually dug it in the right place.
think.ryi.me/p/the-sunk-cost…
This aligns with the concept of "feature engineering"
We're not always creating entirely new data points, the magic lies in how you curate, analyze, and tell the story within the data.
It's all about creating that "aha!" moment and that's a skill worth its weight in terabytes.
Big Problems, Simple Solutions?
The bigger the problem, ironically simpler the approach to the solution should be. Think about it: data systems are simpler than ever, and even ancient philosophy tackled huge questions.
The key to BIG data problems lies in fundamentals.
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Struggling with a complex data approach?
Go back to basics!
Validate if your solution has a fundamental scaffold built on simple principles.
If not, it's time to rethink.