The foundation for a high-performance marketing technology stack is having a well- thought-out data strategy and acquiring the right set of anchor platforms (those products that sit at the core of the stack, that drive campaigns, programs, and customer engagement and that house all customer data). Your data strategy should ensure that data is acquired, complete, cleansed, and normalized to your company’s specifications, and is leveraged properly across the organization and stack.
Just like virtually everything else related to marketing technology, there is no right or wrong data strategy, only the right or wrong strategy for your organization.
Data strategy is likely to be driven by a number of factors—philosophical beliefs about data organization and management; systems and capabilities already in place; internal technology expertise; data reach (are you working in a silo or across the organization); and budget limitations.
The four things to consider in defining your data strategy are:
Over the last few years there has been a lot of discussion about creating a centralized single source of truth for customer data, which has driven the development of Customer Data Platforms (CDPs). The idea behind a CDP is that customer data is fed into a single database by marketing, sales, services, and business systems, where it is cleansed, appended with 2nd and 3rd party data, and then fed back to each system as a complete data record. Advocates for this centralized approach believe it is the simplest way to manage data compliance requirements and to ensure that everyone is working with the same set of data. Over the last year, we’ve seen the CDP category gain momentum.
Another approach is to pick a single system of record (usually a sales automation or marketing automation solution), keep that pristine, and then leverage a data management/orchestration platform to synchronize data among your other systems.
In this scenario, the data orchestration platform cleans, normalizes, appends and de-duplicates records from multiple systems but does not serve as a data warehouse itself. All data resides in the separate platforms and passes through the orchestration platform to ensure data integrity and completeness. This approach can be simpler, cost effective, and more agile than introducing an additional database into your stack.
And, of course, there’s always the hybrid approach, which leverages a centralized platform within an organization (e.g., marketing) and then uses a data orchestration platform to synchronize data between organizations.
In an M&A environment, coming to an agreement on data architecture is the first major step towards integrating your marketing technology stacks.
A note about data: with the introduction of new privacy laws and, in particular, General Data Protection Regulation 2016/679 (GDPR) which makes companies responsible for the compliance of their data supply chain, it’s essential to understand how each technology vendor and data supplier handles customer data and complies with regulatory requirements. This is something that should be tracked within the marketing tech stack. As you begin to rationalize your stack, you should flag any product vendors or data suppliers from whom you need data compliance statements.
Once your data architecture is in place, the next step is to look at data flow. Start with a generic approach – in a perfect world how would data flow between product layers and platforms? Documenting the desired flow will help you in evaluating the key platforms in your stack.