Data ownership for marketers: How to get deeper insights from your marketing data

“If I have one dollar to spend on advertising, where should I spend it, and how much will I get in return?”

There’s so much pressure on marketing leadership to prove the ROI of marketing and figure out the best channels to spend the marketing budget.

Here’s a hint—taking full ownership of your marketing data will make it much easier. You’ll be able to:

Break down data silos and measure marketing’s impact
Future-proof your marketing from uncertainty

In this post, we’ll discuss data ownership and how you can get started with it.

We teamed up with Snowflake’s Field CTO of Advertising and Marketing, Jim Warner, to discuss all things data ownership. Watch the recording.

What’s data ownership?

Simply put, data ownership refers to the extent to which organizations control the data they generate—marketing, sales, product, customer data, etc.

If you work in marketing, you probably use many different platforms and tools to reach your current and potential customers, and your data is scattered across all those channels. But when data exists in individual silos, it’s impossible to understand if your marketing efforts paid off.

Additionally, leaving your data in the hands of third-party platforms, like Facebook or Google, makes you vulnerable to sudden changes—let’s not pretend that the introduction of Google Analytics 4 didn’t cause marketers any panic.

Owning marketing data means you have a system to collect your data over time—typically in a data warehouse—so you can build historical overviews and forecast customer trends. You can use your data to draw cross-channel comparisons and identify the strong and weak points of your marketing.

Why is data ownership important?

If you rely on other platforms to store your data, you’re missing out on the value of your data. Here are three main reasons you should take data ownership seriously:

• Measure marketing’s ROI
• Get rid of data silos
• Future-proof your marketing

We’ll discuss each of these more closely.

Understand marketing’s impact

Let’s say you have an ecommerce store and you have:

• Paid data in Facebook Ads, Snapchat Ads, and TikTok Ads
• Traffic and conversion data in Google Analytics
• Order and customer data in Shopify

Without consolidating data from these sources, it’s hard to understand your marketing performance holistically and identify areas of improvement.

“You can’t tell where you’re going unless you know where you are. For example, how much are you spending? Where are you spending your budget. Putting everything into a common language so that you can compare apples to apples.”

Jim Warner, Field CTO of Advertising & Marketing, Snowflake

Bringing all of your data under one roof is the first step to finding meaningful insights and understanding the ROI of your marketing.

Get rid of data silos

Only storing your data within marketing platforms creates a siloed data environment. If you need to refer back to all the different ad platforms to monitor your performance all the time, your reporting is inefficient, inaccurate, and tedious.

Having one central location for your marketing data streamlines your reporting process and makes your reports more reliable. For example, by automating your data transfers from all ad platforms to a data warehouse, you’ll have a standardized set of data coming in daily without time wasted on collecting it.

Future-proof your marketing measurement

The marketing measurement landscape is in constant flux as the industry sees a shift towards a privacy-first future. Big players, like Apple, have already rolled out new privacy policies, which affect how marketers can track users across devices and report on their campaign performance.

While companies can’t control external factors, they can reduce the risks by taking ownership of their data. Storing data in a centralized and secured destination lets you be proactive about sudden changes.

Let’s look at the major changes in marketing in recent years, including:

• Third-party cookie deprecation
• New data retention policies
• The transition from UA to GA4

Third-party cookie deprecation

The days when you could track a customer or audience member across the internet are almost over. The sunset of third-party cookies will make the use of first-party data acutely relevant. Consolidating all your data will help you build a first-party data strategy and adapt your marketing strategy to a privacy-first world.

Unpredictable data retention policies

Most marketers rely on platforms like Google and Meta for their historical performance data. But any data left within a platform is vulnerable to data retention policy changes. The provider can alter how much data can be stored, how long it will be stored, and whether you can extract it.

Let’s say you want to pull the last three years of web analytics data to benchmark your performance before Covid. Previously, that data would’ve been easily available, but with the current limitations, you’ll quickly run into a dead end.

The transition to GA4

Significant changes can happen with a limited warning period. An example is Google’s decision to change its data ecosystem and sunset UA. Businesses using UA for their analytics need to act quickly to avoid losing their historical data.

The best option is to transfer your UA data to a BigQuery or other data warehouse, so you can continue benchmarking your performance after the transition to Google Analytics 4.

How to take full control of your data

Taking full ownership of your data is one of those things that’ll never truly be done. Instead, you want to think of it as an iterative process. You should keep updating your stack as your data and analytics needs evolve.

Ultimately consolidating your data consists of three steps:

• Identify the use cases and business questions
• Build the architecture
• Keep marketing data governance in mind

Identify the use cases

It’s best to identify the business questions and use cases you want to prioritize first. For example, is mapping the customer journey a big roadblock for your business? Or maybe your priority for the next three to six months is to create one single source of truth, so your team can build reports with reliable data.

Additionally, choosing the most impactful business questions allows you to start small and build a solid foundation for your data.

Once you pick the use cases, it’s easier to identify the data you want to collect. For example, you want to know how well webinar attendees convert into customers. In that case, you may want to combine data from your marketing automation platform, where you have your registrant data, with your CRM data.

Krittin Kalra, Founder of Writecream, shares, “The biggest challenge is identifying the most important data to your business. It’s up to you to decide what data to focus on and ignore.”

Build the data architecture

If you’re thinking about taking control of and consolidating your data, you should use a data warehouse. While spreadsheets are great for ad-hoc analysis and reporting, you’ll run into storage limitations—especially if you’re processing a large amount of historical data. A data warehouse gives you more capabilities to store, manage, and transform your data.

Every company is on a data journey. Some are further along than others. Let’s look at the common marketing analytics stacks.

Note that we’ll use BigQuery and Google Cloud Platform to illustrate how your stack could look.

Tier 1: The basic marketing analytics stack

The basic marketing analytics stack is a good starting point when you graduate from your spreadsheet tool. You can use a data integration tool like Supermetrics to automate advertising data transfers into BigQuery. Additionally, you can add ad-hoc data sources, for example, your Google Sheets or Excel reports, as inputs to your data warehouse.

Finally, you can use a BI tool, like Looker Studio (formerly Data Studio), PowerBI, or Tableau, to build reports and visualize your data.

Tier 2: Separating reporting from raw data

As your business grows and accumulates more data, you’ll realize that pushing data directly from data sources to a data warehouse and straight to a BI tool isn’t optimal. If you’re doing reporting on top of your raw data, you’re missing out on many opportunities.

Separate raw data from your reporting table allows you to enrich and transform your data before piping it into a BI tool. You’ll get more meaningful and valuable insights. Typically in a tier 2 stack, you can use cron jobs to process data from the raw tables into reporting tables.

The downside of tier 2 is that you need to rely on stellar documentation and human resources to manage and maintain it.

And that’s where the tier 3 marketing analytics stack comes in handy.

Tier 3: Future-proofing the tier 2 marketing analytics stack

Essentially, there aren’t many differences between the tier 2 and tier 3 setup. The main difference lies in these areas:

• Testing: for example, tools like dbt or Dataform help you build a testing framework to ensure that your data is reliable.
• Orchestration: you can use a tool like Airflow or Google Cloud Composer to handle all the data processing in your data warehouse.
• Version control: this helps you identify the causes of issues and problems with your data.
• Documentation: encourage your team to document the process.

Implement marketing data governance

When you start combining data from different sources, for example, marketing data with product and revenue data, you want to get serious about data governance and marketing data management.

Marketing data governance refers to the internal policies that help you manage marketing data and make sure your data delivers business value. Good governance ensures data is protected, secure, and compliant.

Data governance aims to ensure that the right people in the organization have access to the right and consistent data that they can use to make decisions.

Best data governance practices include:

• Assign ownership: if everyone owns it, no one owns it.
• Build a data treasure map: document your datasets, sources, destinations, user levels, etc.
• Educate everyone: teach your employees how to use data.

How companies benefit from marketing data ownership

At the beginning of this post, we mentioned taking full control of your data is the first step to measuring the impact of marketing.

Let’s look at how companies from different industries create a single source of truth and improve their marketing measurement.

A Cloud Guru solved their marketing attribution challenge and future-proofed their analytics stack

A Cloud Guru is an e-learning platform that helps more than 2.2 million engineers build modern tech skills. Like many growing SaaS companies, A Cloud Guru found themselves asking questions about attribution like, ‘how do we make sense of our digital footprint? And ‘how do we measure the impact of our ad spend.’ The team needed to aggregate data from different sources to answer these questions, and their spreadsheet solution couldn’t handle a large amount of data.

Additionally, Zach Cooper, the Director of Analytics at A Cloud Guru, wanted to expand their analytics team to prepare the business for more advanced business problems, for example, adding custom attribution on top of their first touch and last touch model.

As a result, they built a marketing data warehouse in Snowflake with data from Google Analytics and other ad platforms. This setup helped them create cleaner and more flexible datasets, allowing them to build better reports.

Zach shares, “We were in a situation where we had to decide if we wanted to plan for the future or get stuck in the past. And in that scenario, we’ll always plan for the future. We’re a SQL-native analytics team, and getting our data into Snowflake created a far more flexible, easy-to-join, native dataset for us.”

Read how A Cloud Guru solved their marketing attribution challenge with Supermetrics and Snowflake.

dentsu Norway powered marketing mix modeling

dentsu Norway is an agency within the dentsu network. The Norway team is one of the leaders in data analytics and marketing reporting. They advocate using marketing mix modeling (MMM) to help their clients understand the true impact of their marketing—identify the sales that come anyway and those that come from marketing activities.

To do MMM, the team brings all their marketing data into BigQuery. Once the data is there, they continue to move it into the dentsu data hub, where they combine the ad data with their client’s sales and brand data. Next, they enrich the data and prepare it for MMM, for example, creating custom columns or adding a new exchange rate or a currency. After that, they’ll visualize the data in a BI tool.

Jarle Alvheim, Head of Data Technology, dentsu Norway, shares, “One thing that changed since we started is that we do more data modeling for our clients now. Supermetrics helps us move marketing data into Google BigQuery and allows us to do marketing mix modeling at a lower cost and less development time.”

He continues, “I think one of the biggest selling points in several client meetings now is that the clients can look at the dashboards, and if they want to change something, we can make the changes on the fly.”

Read how dentsu Norway helped clients build better data models with Supermetrics and Google BigQuery.

Marketers, take control of your data!

No one knows exactly what the future holds for marketing and measurement. But we know for sure that brands who own their data will be in a better position to respond to changes and better understand marketing’s impact.

If you’re interested in taking full ownership of your data and building a data warehouse, contact our team.