Building a reliable customer data infrastructure requires a lot more than the tech resources to do it. Large organizations may have the engineering resources necessary to pull it off, but keeping everyone who has a stake in your customer data happy can evolve into an incredibly burdensome task. On top of that, keeping up-to-date with API changes and ensuring that your customer data ecosystem is healthy can easily suck valuable time away from your team. 

What Is Customer Data Infrastructure?

Customer data infrastructure, also known as CDI, is a common type of data intensive application. For an organization, it's basically a data infrastructure that is responsible for capturing, processing and identifying the important bits of data that can lead to comprehensive analysis and a better understanding of how customers are interacting with various levels of your organization.

Compared to most companies that simply collect customer data and never really get to the point where they can effectively use it, a Customer Data Infrastructure platform concerns itself with effective customer data management, so you can ensure your tools receive the data they need to perform in an organized, standardized manner.

What Are the Modules of Customer Data Infrastructure?

Generally, there are three modules or components in any customer data infrastructure initiative:

• Source
• Data
• Destination

Source

The first is known as a source, which is where all the data comes from. A source can be a desktop browser, mobile app, or even a server inside a data center that feeds information into your business. Even still, a source could be a cloud-based application that generates information or really any server that emits data relevant to your customers. Here are some examples of sources for your CDI.

Data

The second piece of customer data infrastructure is the data itself. As data is collected from sources, it's ingested into the CDI in real-time. From there, the data is transformed, filtered and mapped to your destinations according to your CDI data governance preferences. 

Data Mappings

A data mapping is a function that translates your data input values into output format required by destination APIs. This can take the form of renaming keys, setting default values, and applying transformations like joining strings or converting data types. Mappings are important for ensuring data compatibility and consistency across systems to reduce errors and improve integration workflows. 

Data Enrichment

Data enrichment augments and supplements event data with additional context or generated values. These values can be timestamps, UUIDs, random identifiers, or data fetched from other sources. By enriching event data you can make it more actionable by adding metadata or contextual information that is required for downstream analysis or processing.

Integration Filters

Filters provide a mechanism for allowing or dropping events based on pre-configured criteria. For example, when a certain event name or conditions within the data trigger a filter, they will help determine what data is distributed. This is important for compliance as you can set filters to only send data when user consent has been provided. It can also be useful for reducing unnecessary data processing for downstream systems by only sending relevant information. 

Destination

The third module of customer data infrastructure is the destination, or where the data goes. Sure, you might be collecting gigabytes or terabytes of data, but if you're not responsibly storing it and delivering that data in a way that can be understood by those with the power to make decisions, you could be squandering much of that effort. Additionally, destination platforms tend to change how they accept data, and a customer data infrastructure should help you stay on top of the most recent changes when they occur.

Building Data Pipelines

Getting your data from the point of collection to various destination endpoints (CDP, data warehouse, analytics tools, etc) is a strength of CDIs. Building a robust data pipeline is a key component of CDIs – delivering your data to its final destination. 

• Customizing Data Streams: CDIs let you build data pipelines that route to specific data streams to custom destinations so each platform has the right data in the right format.
• Scalability and Flexibility: Data pipelines built with CDIs allow you to adjust your destinations, add new data sources, and transform the data to fit all your needs.
• Data Transformation: CDIs offer mapping, filtering, and transformations to cleanup data prior to distribution so your data is deliver in a destination-ready format.
  

How to Better Collect Customer Data

Because data can be collected from disparate sources, and destination platforms may utilize different protocols and methodologies for collecting data, it's important to capture the data at the right point and get it to the proper destination. Without that crucial piece, all the data in the world isn't of much value since it can be difficult to qualify the data and ensure that you're actually comparing apples to apples, not some other strange permutation. 

By controlling how the data is collected under a distributed system with all the compatibility and redundancy needed to ensure reliability, customer data infrastructure is far more than a software layer -- it can revolutionize how you do business. A customer data infrastructure is therefore a great resource for any company as it helps you better understand your business, as well as the people that are interacting with your business. 

Real Customer Data Infrastructure

If you're ready to excel at every level, you need a partner that can help you understand the data you're collecting so that you can get your personalized customer experiences right. Here at MetaRouter, we'll help you identify the right third-party tools and software to enhance your offering. Get started today.

Data Resiliency

A customer data infrastructure should ensure that your data is not only standardized and clean for end destinations, but also that it is not unintentionally dropped while being collected, transformed, and delivered. This can range from ensuring that data collection is not blocked by browser anti-tracking tools, to retrying customer data that is not accepted by APIs, and even the ability to resend data to destinations from a storage tool like a data lake.