September 15, 2022 | 4 min read

Data Solutions: Repurposed vs. Purpose-built

Companies want to get value from their customer data, but to do that, they need the right tool for the job — one designed to make sense of chaotic data.

Illustration of various screw types: Phillips and flat

The best solution for any job is one designed for it, not something repurposed with a new functionality tacked on. That's why companies who want to use their customer data to provide stronger experiences and grow their business need to work with the right tool for the job — one designed to make sense of chaotic data and help you get value from it. Broadly speaking, being able to use your data boils down to three categories: making sense of it, learning from it, and putting it into action. All the steps are pivotal, but the first step is the hardest and the one that people stumble over the most. And if you don't have a solid data foundation that you can understand, you can’t execute the next steps well.  This calls for a solution specifically built to handle a mess of data that comes from different sources, in different formats, and is always changing. Other tools may gesture at this but aren’t created to address the root of the problem or properly solve for it. Think of it like this: you can drive a nail into the wall with the base of a screwdriver, but using a hammer will work better. You can also pry out a screw with pliers, but a screwdriver will get the job done quicker — you get the picture. 

Image of a screwdriver and a broken nailThis doesn't quite work, does it?

Navigating the data solution brambles

The market is full of solutions that claim to be able to help you get value from your data, often calling themselves Customer Data Platforms, but they don’t address the whole spectrum of challenges. In reality, they are only able to get at steps two and three, learning from the data or putting it into action. Handwaving past the critical first step of making sense out of the data means what they're able to do with your data isn’t built from a strong foundation, which means it isn’t accurate, and you’re in that old familiar conundrum of garbage in, garbage out. 

It's not their fault they can't make sense of messy data. These data solutions were created to address entirely different tasks, such as being:

1. Tag managers: Designed to manage all the tags and pixels brands added to their websites for various external vendors, they began to tag themselves and include their own pixels and tags to websites to create and collect behavioral data at an individual level. They eventually evolved to bring in data from other systems. 

2. Multichannel campaign managers: These tools began as a way to organize marketing campaigns in a single place and build out complex, multichannel journeys, and grew to be able to track the performance of campaigns and incorporate machine learning for personalization. 

3. Analytics platforms: These vendors came about when brands realized that the data they were collecting wasn't necessarily useful or ripe for analysis. Their aim was to give brands a way to combine their data for better analytics, like understanding customer behavior and predicting intent. 

These solutions were created to solve particular problems, not make sense of chaotic data. Rather than focusing on whether or not something is a CDP, brands looking to get value from their data should focus on what problems their tools were designed to solve.

Built for the job

Being able to get a handle on tangled data means being able to unify it in a way that embraces the inherent messiness of customer data.

Functionally, that means being able to take data from any source and in any format and using AI and machine-learning algorithms that are sophisticated enough to account for inevitable changes and mistakes in people’s data. 

A tool specifically crafted to work with a customer data mess will be able to handle massive datasets from disparate sources quickly and accurately, have advanced identity resolution and profile-building capabilities, and keep the profiles fresh and up-to-date when new data comes in.

Solving the critical first step of preparing the data to find signal in the noise then opens the door for everything that comes next. Whether your purpose-built tool for data decoding has further capabilities for insights and orchestration or has the flexibility to plug into other tools and systems you may have already invested in, it needs to set you up to perform all three steps — understanding, learning, and action.

When you start with tools intentionally designed to help you get value from your data, you’re on your way to understanding your customers’ wants and needs, personalizing their experience, and growing your business. 

Learn more about how a purpose-built data solution can help turn messy data into value.