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Data as a Product
Data as a Product

August 23, 2022

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Data as a Product

Have you heard about the phrase “Data as a Product” and wondered what exactly it is? It's one of the four basic principles of Data Mesh and is more about a change in the mindset. Here, Domain Owners take the responsibility for their data as it's their product. Let's understand in detail.
Key Aspects
What are the key metrics for a product? Here are 6 major metrics to track any product success:

availability


In a nutshell, we want to treat our data exactly the way we treat our products. We should be worried about:

●    What value is it adding?
●    Is it highly available?
●    Is it easily accessible and discoverable?
●    Can data consumers understand it?
●    Can data consumers use it with ease?
●    Can any data issues be resolved quickly?

And all the answers point towards taking ownership of our data. Once we feel accountable for our data, we will want to set it up for success. And for that, we have to see all the six metrics / KPIs steadily increasing.


Dark Data
Today, most organizations have lots of Data assets - for which they have no clue of who is generating the data, what’s the context of the data, who is consuming the data etc. Such black-box datasets lead to  “Dark Data”, which is extremely hard to use for analytics to get the right results.

We all know data can be a fuel for any organization’s strategy to grow faster. It helps us to see through the past and look into the future. However, Dark Data makes both past reports and future predictions difficult and inaccurate!

 

Let there be Light


Data Mesh, while defining clear ownership - to remove Dark Data


Who Owns What?
Data as a Product is great, but it requires people to display ownership - and often, the mindset of taking ownership is the tricky piece of the puzzle.

In general, the incentivization for any product is pretty simple. Build revenue models around the product, improve retention and adoption of the production; and the success of product owners & product engineers gets directly tied to the product’s success.

Now for Data owners, there should be a similar incentive that’s tied to the success of their Data Product. There should be good driving reasons why they would want everyone to adopt and consume their data.

 

you get my data


Data Stewards owning the data be like

For example, this is what the potential incentivization could look like:
1.    The specific Domain’s success can get tied to the success of the respective Data Product(s)
2.    Data Owners’ budget can be proportional to the (weighted/unweighted) average Happiness Score of all the Data Consumers
3.    Senior Leadership can track Data as a Product against the above-defined objective metrics/KPIs. 


The Challenges
Two things become extremely important when we start following the model of Data as a Product:

1.    Metadata: When there are multiple Data Products, it should also be very easy to discover and understand. As part of adhering to best practices of Data Product documentation and cataloging, Metadata comes into the picture!
2.    Data Governance: The right set of governance principles should be implemented, accompanied by the right roles defined in the organization. This includes following different compliances, ensuring strict access controls, distinguishing sensitive and non-sensitive data, etc. Data Owners / Data Stewards should understand their roles and responsibilities, and how they can enable Data as a Product. 

Conclusion
Data Product is more like a mix of change in mindset, defining right metrics and processes and deriving best value from your data. Ensure right incentivization for all stakeholders to set it up for success !
Authors’ Bio
Athitya Kumar
is a Sr. SW Engineer & Open-Source Community Lead at Intuit India. He has worked on the various ingestion & self-serve capabilities of the Unified Ingestion Platform, and also kick-started the self-healing capabilities at UIP. While not wearing the “work hat”, Athitya loves reading books, writing blogs, and binging TV series!

Shivansh Maheshwari is a Software Engineer-2 at Intuit and is currently working on the Self-Serve component of the Unified Ingestion Platform. He has worked on various self-serve capabilities and has always been passionate about learning new technologies. Besides work, Shivansh loves watching sports.

Isha Rani is Group Engineering Manager at Intuit with a mission to Self Serve the data ingestion to the lake in both real-time and batch. As a leader, she is playing a key role to adopt Data Mesh at Intuit. She is working with DataBricks to achieve streaming materialization to support real-time analytics and reporting use cases. She feels at home when it comes to interacting with Leadership, Business Stakeholders, Architects and Engineers.
 


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