If your organization works with any martech or adtech vendors, you know that your data must match vendor schemas. You are also likely familiar with the sheer inconsistency of schemas across different vendors and how tedious it is to transform and map your organization’s data to every vendor you use. 

While the number of vendors in the martech/adtech space constantly fluctuates and is hard to quantify, there are likely upwards of 15,000 in operation today. Depending on your organization’s size, you will use between 20 and 120 of these vendors at any given time. But with each one demanding its own unique schema and all the work that goes into data transformation, who has the time or capacity to keep up with it all?

Traditional Data Transformation Just Isn’t Cutting It

There are many ways to transform your data—some easier than others. Suppose you’ve been in the martech space for a while. In that case, you’ve probably heard of or used some of the following tools: ETL pipelines, transformation scripts, no/low code data integration platforms, transformation templates from vendors, and data warehouse/lake transformations. These vary in technical difficulty, customization, scalability, performance requirements, and maintenance. 

Your engineering team will likely have the final say in how your organization transforms data to meet vendor requirements. However, your marketing team will initiate the process and ensure data lands correctly in the downstream vendor. 

However, if your organization isn’t using a data integration platform or a data lake/warehouse, or you’ve had any type of turnover in your engineering team (more than likely), your transformations may involve more than one of the above parties. When it comes time to change or update the transformations, who is the responsible team or person? It’s hard to tell when there isn’t a cut-and-dry path and information is disorganized—or even lost. 

A Better Way to Transform Data

Keeping data transformations in one central location seems like a slam-dunk no-brainer. Luckily, quite a few vendors specialize in data transformation and consolidation—the trick is selecting a partner that enables the correct level of control over your data and how it's transformed.

Questions to Ask When Looking for a Vendor

Due diligence is crucial when looking for a vendor to work with your organization’s data. Here are a few basic questions to keep in mind while searching:

1. How robust are their data cleaning capabilities?
2. Is schema enforcement built into the platform?
3. Do they support custom data transformations?
4. Are they able to handle identity resolution?
5. Is the platform schema agnostic?
6. How easy will it be to implement?
7. Do they offer a way to test schemas and data transformations?
8. Is the platform user-friendly and understandable?

While a quick search of the vendor’s site may not answer all of these questions, they’re important considerations to weigh during the sales process. If a vendor can’t satisfactorily answer these questions, that’s a sign they’re not up to par to handle your critical data.

The vendor you choose must support your data strategy. They should help your organization grow and modernize your data approach. If there is pushback, your organization is susceptible to falling behind the data curve, losing valuable insight into your customers, and, ultimately, losing money.

The MetaRouter Approach

For most enterprise companies, MetaRouter will check all the boxes. We support a data-first approach that is entirely based on client needs. Our platform is schema-agnostic, so even if your data is messy, we can work with it, help you clean it up, and ensure that it lands in your downstream vendors as they expect to see it. 

How does MetaRouter Transform Customer Data?

MetaRouter uses an analytics.js schema across all integrations as an out-of-the-box solution. If your organization has a custom schema (like many do), several options exist for transforming your data to meet vendor requirements within the platform. MetaRouter supports a robust list of data transformation possibilities, from utilizing an entire transformation library at the cluster level to managing transformations on an individual pipeline basis. 

The quickest and easiest way to transform data is through our transformation library. This occurs at the cluster level and works to modify all data coming in to meet the analytics.js schema. One of the most significant benefits of this approach is that the mapping needed to build integrations in the platform becomes essentially null and void, and they can be built using the out-of-the-box playbook with minor additions of connection parameters. Transforming at the cluster level ensures that all of your data is analogous across the board. This is also the level where filters can be applied to prevent sensitive data from reaching downstream vendors. 

Another option is to transform data at the playbook level. While this option will require more effort, it allows you to specify exactly what each vendor receives. This option is advantageous if there are certain events you don’t want specific vendors to obtain, as you can also filter at this level. The result is similar to utilizing the transformation library at the cluster level with more control over the specific integration level data flow. 

Real-Life Transformations

Fresh and Clean

A newly added (2023) Fortune 500 retailer started working with MetaRouter, mainly to keep up with the quickly changing compliance and data landscape and to remove third-party tags from their sites. During implementation, we determined that their schema needed a significant overhaul; however, the work needed to accomplish the changes would negatively impact their time to value. To work around this setback, during the overhaul phase within the retailer, MetaRouter built a cluster-level transformation layer to support the schema. We modified and updated this layer throughout the process, and it was finalized once the schema work was complete. The result was a transformation layer that modified all incoming events to match a general a.js schema format, allowing the retailer to use the platform without additional internal dev work. (It made the platform more pointy-clicky, if you will.) This retailer is now set to expand their server-side approach internationally and provide their adtech and martech teams with invaluable data.

Compliant Veggies

A Fortune 50 grocer began working with MetaRouter in 2022 to overturn their approach to consumer privacy. After several issues with sensitive data incorrectly routing to places it didn’t need to be, this grocer became fed up. In comes MetaRouter. We worked with their internal teams to route their event stream to several downstream vendors; even today, we are continuously adding more. The most important part of this process was ensuring the downstream vendor had zero visibility into the entire event stream. This required advanced filtering for keywords and product groups and transforming data at the playbook level to obfuscate any sensitive information remaining in the data. The result? Downstream vendor data that is 100% compliant with their internal privacy policies and consumers who can safely shop knowing their data is safe. 

Whether your organization is starting its data modernization journey or updating old systems, data transformations will eventually be part of that process. Do your due diligence early and match with a partner who will support and guide your organization through the process and treat your data with the respect it requires. 

If you have questions about MetaRouter or want to learn more, schedule a discovery call with our team today!

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