Hub & Spoken.

What's in this podcast?

In this episode, Jason talks to Murali Bhogavalli,Data Product Manager at Tinder. They talk about the concept and the idea of treating data as a product, the roles of Data Product Manager and how the industry is shaping around it.

Listen to this episode on Spotify, iTunes, and Stitcher. You can also catch up on the previous episodes of the Hub & Spoken podcast when you subscribe.

What are your thoughts on this topic? We’d love to hear from you; join the #HubandSpoken discussion and let us know on Twitter and LinkedIn.

If you are interested in this, you might like to view our on-demand webinar on Data Product Management: https://cynozure.in-beta.link/insights/data-product-management-webinar/

One Big Message

Treating data as a product means bringing product management practices into data solutions.

[00:41] Murali shares his journey to getting into Data Product Management

[03:06] What is data as a product, and how does Tinder treat it that way?

[05:52] The evolution of Agile Methodologies in the context of data

[11:01] What is the function and purpose of Data Product Management?

[14:17] What does a Data Product Manager do?

[17:14] How to interact with Data Product Managers to ensure the building of the right products

[22:27] Murali shares the biggest challenge of helping the organization to move forward

[27:17] How to set up teams for effectivity

[35:01] How the industry is shaping around Data Product Management

The function of Data Product Managers

If you treat data as a product, you also need people who can put on the data product manager hat and help (not necessarily everybody in the organization) to start looking at data as a product.

From a Data Product Manager perspective, one of the biggest things that Data PM should be looking out for is how data maps to the business goals out there.

When the company comes up with a business goal of increasing revenue, the Data PM would come in and ask the following questions:

• What data does the revenue team use?
• What are the features that the revenue team owns?
• What is the data that they collect from these other new features and how is it getting stirred?
• Who’s actually using it?
• Who has access to this?
• Do they do any transformations before they actually use it for insights?

If they are consumers:

• What are their latency requirements?
• What are the SLS?
• Do they need it on an hourly distance?
• Do they need it on a real-time basis or not?

All of these questions will help prioritise what needs to be delivered to whom and build a data product road map, and not to mention a bunch of regulatory and compliance requirements around the data that is being collected.

Playing offense versus playing defense with data

What companies tend to do in their growth stage is they want to use data for playing offense. We play offense with data and use data for growth, more engagement, more retention and motivation. Those are your focus areas in terms of growth of the company using data as a fuel.

There’s another aspect of playing defense with data, which is building resilience systems for the future. Ensuring that tools needed for data teams are provided to be more efficient.

So, playing the balance of playing offense versus playing defense is the challenging part. In playing offense, everybody gets it because there’s a clear business value that is being driven with that while in playing defense, one might not be able to quantify the value in the short term because it’s a long term goal.

Often, people struggle with this aspect of playing defense until something comes along and hits the lawsuit. That’s why the balance between both, and strategies for both, on a data road map are important.

Industry shaping around data as a product

Companies start capturing everything and anything that is out there and putting it in the data lake; it has become this unwieldy monolith that they’re not able to move fast and deliver value on, to their consumers.

Taming the data lake, or data warehouse, and breaking it out into sizable chunks is a microservice architecture. The industry is shaping around this. They’re saying no matter where your data is, whether it’s a lake, a dead lake or a data warehouse will give you your capabilities to build subsets of that data.

To summarise

To know good data is to treat it as a product. You have to learn how to evaluate the current state of your data as well as its potential for improvement. With the help of Data Product Managers who know how to map to the business goals using data, achieving business growth is attainable. Treat data as a product, in a similar way to the regular product that you build.