Marketing data integration is the process of combining marketing data from different sources to create a unified and consistent view. If you’re running marketing campaigns on multiple platforms—Facebook, Instagram, TikTok, email—you need marketing data integration. Why? Because being able to assimilate data from different channels and across multiple marketing touchpoints gives you visibility into the overall impact of a campaign, event, or another marketing effort. Proper integration of marketing data is a critical element to establishing the right data driven marketing foundation—your customer 360. CMOs and MarTech leaders need to have their finger on the pulse of the data integration and modeling that’s needed to get the most of their marketing data, because these functions are integral to proving—and improving—the ROI on marketing spend.
Data integration and modeling is a vital component of the marketing function, yet may be among the least understood. One reason for this is that dependencies usually exist outside of the marketing team, such as marketing ops serving as a liaison, and marketing campaign teams are the “consumer” in the integration/modeling/data warehouse activities.
Data integration and modeling was one of the top capabilities identified when Snowflake analyzed usage patterns of over 6,000 active customers to understand just how marketing teams were using the Data Cloud to modernize their marketing efforts. Snowflake has a unique vantage point because many organizations use these technologies in conjunction with Snowflake’s Data Cloud alongside their martech stack. We also looked at which partners were most likely to be helping customers with these use cases and put it all in the Modern Marketing Data Stack report. This is the second in a series of articles focusing on the six top use cases for the Data Cloud.
What Problems does Data Integration Solve?
Customer data integration provides your company with a 360-degree customer view, which in turn, provides a complete picture of customers‘ behaviors, interests, and likelihood of purchasing a product. Sounds simple enough, but good outcomes start with a good data strategy, one that can help you avoid common pitfalls.
A solid data integration strategy can ensure your team gets the best ROI for their budget and avoids costly mistakes that can render some of your data unusable. Common mistakes include failing to get stakeholder alignment on the goals for data integration and modeling, lack of a unified way to label data that is gathered (something as simple as one team putting a last name first and another doing the reverse, or asking people to check their job responsibilities but using two different lists), and not effectively overcoming data silos.
Siloed data is a huge obstacle for many marketing teams. When data is scattered, it becomes difficult to understand how your marketing efforts perform and where you should focus your budget and resources. It also makes it extremely challenging to operate with the right agility and the needed effectiveness to deliver impactful and personalized campaigns. Additionally, manually collecting data from different platforms takes hours, if not days, leaving little time for campaign optimization and eliminating the agility needed by modern marketers. The worst part is that by the time you turn that data into actionable insights, your marketing team may have wasted budget heading in the wrong direction. So the most important thing data integration can do for you is banish data silos and create a single view of your organization’s data. This allows businesses to see trends and patterns that they could miss if their data was siloed.
In the past, some teams would integrate marketing data into a spreadsheet tool, like Google Sheets or Excel, to do ad hoc analysis and daily campaign reporting. But that’s basically taking your data out of one silo and putting it into another, one that is manually created and maintained (or not maintained)! Not a good option. Today, the Data Cloud handles data, both structured and unstructured, from a variety of marketing touchpoints in a way that manual methods never could. From a cloud-based data warehouse, like the one present in Snowflake, you can easily feed data into a BI tool to make it easier for different stakeholders to understand how marketing is helping the business to succeed.
Data integration also improves operational efficiency—integrating data from myriad sources is a process frequently marred by data redundancy and replication. External data sets are often acquired without knowledge of how they were created and are therefore subject to data quality problems. Many providers offer data clean rooms—a secure, governed way to collaborate and access sensitive data, without exposing it. And while data platforms have emerged to better handle data, there are lessons to be learned as well.
There is a significant difference between an implementation of a conventional data cloud architecture on cloud-hosted platforms and one that has been designed to exploit the types of capabilities and services supported by the cloud provider. A conventional data platform implemented in the cloud is still bound by the same limitations—the complexity of the platform, the need for extraction, transformation, and loading, and an inability to ingest both structured and semi-structured data. Furthermore, the interoperability challenges become even more acute with the need to not just span different systems but possibly the need to cross the on-premises/cloud boundary.
Our research found that the majority of Data Cloud customers already use leading technologies that fall into either integration and modeling or business intelligence, and 92% of customers that appear in the Global 2000 use tools from Snowflake or one or more of its partners. Of that group, 75.7% currently use one of the companies identified as a leader within the integration and modeling category of Snowflake’s report.
How Data Modeling Fits In
Once the data is captured and integrated into Snowflake, it must be harmonized, or modeled. The ultimate aim of data modeling is to establish clear data standards for your entire organization. By using data models, developers, data architects, and business analysts can use logical data and determine how they want to use it before building databases and data clouds. Once data is collected, integrated, and maybe cleaned, it’s time to render new understandings for business stakeholders through modeling. ML-based modeling techniques better help marketing teams view and even interact with the data in terms they use daily and in alignment with the metrics and KPIs they must report. But the success of this important step starts way up the value chain.
Data modeling comes after a good data integration strategy. At the outset of a marketing campaign, anticipating the need to create visual ways to understand the data means marketers have to ask important questions: What is it we need to see modeled? For whom are we modeling this data? How will these models help us make better business decisions?
A marketing data model organizes elements of data that your campaigns collect to determine how those elements relate to each other, allowing you to spot relationships, glean insights, and determine how to improve results by making changes to your marketing strategy. It also means considering which third-party tools and platforms will help the modeling effort, such as data visualization tools and those for data transformation.
Data models also help with data governance and legal compliance, as well as ensuring data integrity. They allow you to set standards from the start of the project so teams don’t end up with conflicting data sets. For a more technical deep-dive into how data modeling can benefit your marketing team, read this Snowflake blog.
Data integration and modeling isn’t fun stuff for most marketers, but it is necessary to build the right marketing foundation. Successful organizations typically start with the business use-case and then work backward to identify the data they need to capture and ultimately model to deliver on those core business goals. This will not only streamline your campaigns but truly unlock the power of your data to maximize benefit. For more information on how and why more teams are using a modern marketing data stack as the infrastructure for their martech stack, read this.