You might think of a data clean room simply as a secure environment where two or more parties who couldn’t previously share data can now do so with privacy, security, and governance rules in place.
You might also think of data clean rooms as a tool designed to be used by data scientists, engineers, and analysts. All of that is true, but today, with the modern-day data clean room, there’s a whole lot more to it. And it’s not nearly so complicated or restrictive.
Before we get into all the capabilities and benefits of the modern clean room, let’s look at how far we’ve come.
The History of Data Clean Rooms
Data clean rooms have undergone quite a transformation since their inception. Google was the first to market, launching their data clean room, Ads Data Hub (ADH) in 2017. It was a cloud-based solution for YouTube advertisers to gain measurement and insight.
Although ADH has evolved quite a bit since then and many others have followed suit creating their own clean room environments, including Amazon’s Marketing Cloud and Facebook’s Advanced Analytics, these are primarily environments built for data scientists and engineers.
This is not to say that these clean room environments are not extremely valuable. But people who use the software are required to work with raw data and need to be able to run complex queries in order to extract insights. That analysis can sometimes take weeks before it gets in the hands of the marketer or business strategist.
When you’re a day-to-day marketer or business person and need to optimize media campaigns and/or gain intelligence right away, that just won’t do. What’s the benefit of access to more and better quality data if you can’t quickly gain business insights and it can’t be utilized by non-data scientists across your organization?
The modern-day data clean room solves this lag in intelligence and makes it available to more business people.
The Modern Data Clean Room Is All Business
The modern data clean room is designed to focus on solving business challenges and providing quick insights by supporting both the data scientist AND the marketer or business manager.
What does that mean? It means that:
- You don’t need to know how to write SQL queries or work within Jupyter Notebooks to extract intelligence from the new data that’s now available to you
- More business units have direct, self-serve access to insights more quickly
- The data scientist is freed up from repeated, everyday queries to focus on more complex, unique, and challenging business questions
In other words, if you’re working with data clean rooms that have been designed to solve business challenges and reveal new opportunities, you’re in the perfect position to empower more people and really scale your efforts across the organization.
Features of the Modern Data Clean Room
As brands are adapting to the ecosystem changes upon us and begin to embrace a privacy-first posture, they are looking to data clean rooms as a core part of their strategy and also asking, what are the features of a data clean room that I should be looking for?
Here are three attributes that indicate your data clean room was designed with a business-first mindset:
- A flexible user interface.
- An automation-first approach.
- An intelligence layer.
Let’s take a closer look at each one.
1. A flexible user interface.
A flexible user interface allows you to seamlessly choose between business user and data scientist roles. When you’re in “business user mode” you’ll have access to a robust library of natural language business questions and visualizations. These include questions like:
- How do my loyalty segments overlap with retail purchase categories?
- What is the latency curve by first and last touch for my converting audience?
- What is the frequency distribution by campaign and site for a given month?
When you’re in “data scientist” mode, you can run more complex and customized queries.
2. An automation-first approach.
An automation-first approach enables you to work more efficiently by automating advanced query analysis and surfacing actionable insights on a regular basis. Automation saves time, creates consistency, and ensures that you catch more opportunities.
3. An intelligence layer.
The intelligence layer is what transforms the data in clean rooms, into actionable insights & business outcomes via end-to-end marketing applications such as Measurement, Attribution, Segmentation, Insights, Activation, Incrementality, and beyond. From there, marketers and their supporting analysts are able to use a single modern interface to extract insights framed as plain-speak critical business questions, across any clean room environment(s).
When the data clean room is designed to deliver end-to-end solutions that solve business challenges, and not simply to be a tool to access raw data, you truly have an environment that will make a difference in your marketing and strategic business efforts.
Benefits of the Business-First Data Clean Room Design
To sum up, why is a business-first mindset so important in a data clean room?
- It expands the universe of people and skill sets within your organization that can extract value from the data clean room.
- You can still leverage a data clean room and the value and growth it can offer your business, even if you don’t have a data science team.
- If you do have data scientists, the modern data clean room enables them to focus on more complex analyses, while the business users can leverage the library of business question queries to extract insights and optimize their campaigns quickly.
Data clean rooms are providing opportunities for companies to unlock new data, use cases, and partnerships previously unimaginable. When a business is investing heavily in media, waiting weeks to extract value from the data, is simply not effective.
The modern-day data clean room has capabilities that ensure you’re able to extract the most value possible, as quickly as possible.
Want to learn more about how a modern data clean room can benefit your business? See how Habu can help.
