In this episode, our host Arpit Choudhury talks with Boris Jabes, CEO of Census, about how you can transform your data warehouse into your growth engine.

While many of us say that using the existing data in your warehouse is the best way forward, the real question is, how do we ensure that the growth team can effectively leverage the data that is already in the warehouse?

If you're looking for insights into making your data warehouse a powerhouse for growth, this episode is a must-watch. In just under 15 minutes, you'll receive answers to the following questions:

• What does it take for a growth team to effectively leverage the data that's already in your warehouse?
• What role does the data team play in empowering the growth team to utilize the available data?
• How can data analysts and engineers engage in more impactful work and understand how their efforts drive business outcomes?

Happy watching! 🥁

You can learn more about Census here: https://www.getcensus.com/

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Reverse ETL has been gaining traction over the last few years. In this episode, our host Arpit Choudhury talks to Boris Jabes, CEO at Census about Reverse ETL and how it can improve customer experiences, especially given the increasingly complex user journeys spanning multiple touchpoints across various channels.

With data permeating every aspect of businesses, the conversation moves to how people in GTM (go-to-market) roles can leverage available customer data to improve campaigns via privacy-friendly personalization, and how modern tooling is enabling GTM folks to move even faster.

Arpit also shares his take on the term "non-technical" and Boris describes the factors leading to the rapid adoption of Reverse ETL as well as the pros and cons of centralizing all the data in the warehouse.

Happy listening!

_______________

Come say hi 👋 on our socials!

🧳LinkedIn: https://www.linkedin.com/company/data-beats/

📱Instagram: https://www.instagram.com/datafreakinbeats/

🐦Twitter: https://twitter.com/databeatsnow

What is data minimization?

As per the GDPR, data minimization implies that “data controllers should collect only the personal data they really need, and should keep it only for as long as they need it”

Organizations that collect data about their users and customers are essentially data controllers. Organizations control the data they collect and store and are responsible for the consequences of that data being misused.

But that’s not all.

To stay compliant with privacy regulations such as the GDPR, organizations need to ensure the following:

• They only collect and store customer data that they have received consent for
• They do not continue storing any data that they’re supposed to delete from all they systems

The practice of Data Minimization ensures that organizations only collect and store data that they have an identified need for – they know why they’re collecting the data and how they’re going to use that data to improve the customer experience. Knowing the purpose of the collected data enables organizations to easily keep customers and regulators informed about what data is being collected, how it’s being collected, and where it is being used.

It also makes it easy for customers to opt out from certain data collection practices because they know exactly what they will be losing out on – they need not continue sharing data in fear of losing access to a service or being subject to a degraded customer experience.

It’s becoming the norm for organizations to collect ALL the data from ALL the sources and dump it ALL in the data warehouse. And this practice of collecting and dumping all the data is fueling the rise of “data swamps”.

There’s a massive disconnect between data teams that implement data collection initiatives and non-data teams that need the data in the tools they use every day. And that is the biggest cause for a data swamp – too much raw, unusable data that not only increases storage cost but also increases the risk potential for the organization.

Therefore, organizations that are serious about adopting privacy-friendly personalization practices must embrace the practice of Data Minimization — sooner rather than later.