The modern martech landscape is home to over 14,000 different products.

The modern martech landscape is home to over 14,000 different products.

And every piece of marketing software generates and leverages data. “This has become, over time, one of the greatest challenges and opportunities we have,” Scott Brinker explains. Much of the marketing tech stack has stayed isolated with its own database. Everyone has a different view of the data (and the customer), so no one has all the information needed to make the best business decisions.

The emergence and maturity of the cloud data warehouse have led to a universal data layer that can be leveraged by martech tools and the entire company tech stack. Now, organizations don’t have to tough it out with fragmented, siloed data. They can stay competitive, understand their audience, meet consumer expectations, and remain compliant by unifying their data. Meet data transformation: The process of breaking down data silos to create a single source of truth, unlock the true value of data, and make more strategic, data-driven decisions. Data transformation isn’t easy — it requires operational shifts, introduces new tools and processes, and often takes months. But outcomes of higher revenue, business growth, and better customer experiences are worth the effort. 

If you’re a marketing or data leader hungry for a better, more unified approach to data, this eBook is for you. With best practices and hands-on next steps to help you get started, this guide will help you chart a unique path to data transformation. You’ll learn what it looks like to:

The modern martech landscape is home to over 14,000 different products.

The classic marketing cycle is slow and deliberate. It involves manual data pulls, manual audience building, manual journey building (if any), manual campaign management, and manual results analysis. Months later, you pull together disjointed data from multiple systems to look at your results, search for insights, and try to make adjustments.  But too often, the window of opportunity has already passed.

Slow iteration means slow growth. How many times have you waited for a data request, ad trafficking, or a performance report?
All the time needed to execute these steps increases the time it takes to iterate on marketing. Improvements happen monthly, quarterly, or between campaigns.

Marketing platforms and CDPs were supposed to solve this, but they didn’t. They claim to unite customer data, but in practice, they add another data silo. New problems replaced old problems: mapping the data, security challenges, synchronization issues, and so on. Many CDP projects never delivered the results they promised, yet they remain active  — consuming budget without delivering results.