Months have passed, nights of empty take-out boxes litter your desk, and a fresh pot brews contentedly in the background. The project is complete. You’ve managed to build your company’s first-ever in-house matching solution. But there’s no time to sit back and admire your creation. Having only begun six months ago, the project is already late, the budget is stretched thin, and senior-level executives are already piling on feature requests.
This is a common pitfall of businesses that get overconfident in their abilities (or underestimate the challenges of data matching). Unfortunately, writing fuzzy matching or address validation engines isn’t nearly as black-and-white as the CSV you’re importing. It’s an enormous undertaking that takes years of specialization, especially regarding contact data.
When it comes to data matching software and tools, to build or not to build is the question many organizations struggle with as data accuracy becomes paramount to their data quality.
Do you invest aggressively in long-term initiatives or take the more conservative and (seemingly) less expensive approach by attempting to build your own data matching solution in-house?
This “buy versus build” question ultimately comes up within many companies reassessing their needs. Finding a solution that balances the business’s immediate needs with long-term growth requires research, meetings, investment, and onboarding.
Is there a way that all that be rolled into a simple solution that serves your business today?
The Importance of Data Matching
Let’s back up for a moment. Why even spend all that time on a data matching solution? Why all those takeout boxes?
Because it’s inevitable, enterprise data is riddled with errors and inconsistencies. Duplicate records compromise the accuracy and integrity of your data and can lead to inefficient marketing campaigns, missed sales opportunities, and poor customer experience.
Take a simple name variance, such as Tony Layton, Anthony J. Layton, and A.J. Layton. These are all people listed with the same phone number in your CRM. Due to variances in how Tony’s name, Anthony, is spelled, he’s been entered into your CRM as three separate records.
What seems like a simple error can quickly turn into losing trust with current customers and prospects. This can lead to real reputational damage. At some point, every business is going to have duplicate data. Unfortunately, identifying the problem isn't enough to improve it. A short- and long-term cost is associated with finding and fixing this poor-quality data.
Why Buy a Data Matching Solution?
Cost of Reducing Failure
Teams often choose to build because it will give them unlimited flexibility to integrate with their application codebase and existing systems and processes. But the compounded cost of resources and maintenance makes the “build” option the more costly of the two.
With that in mind, just one duplicate, one error, can have a staggering impact across your entire organization, affecting each system, application, and business process it touches as it moves through your enterprise. A data matching solution will apply a proactive strategy and remediation to catching and correcting these pesky duplicates. This method ensures that your organization only pays the baseline cost of preventing these errors instead of the 100x price that could incur with a failure.
Resources: People & Time
When you build your own data matching solution, you need to think about the tremendous amount of time and energy it will take to create something from scratch, when many software companies specializing in data matching have been doing this for decades.
Building your own custom data matching solution will take a lot of resources, both in people and in time. To build a solution that rivals today’s data matching software, you will need dedicated software engineers. Highly skilled software engineers remain in high demand (and rightfully so) and may not be available or within your budget to hire.
Whether you buy or build, once the software is written, you still need to deal with ongoing maintenance and monitoring. The software program needs to be updated regularly to ensure that it is free of bugs and continues to perform well on the latest operating systems.
When clients build their own services, these solutions tend to be rudimentary, have less processing power, and are not as accurate. They're usually not built to scale and require manual wrangling to get the data into a standardized state before it can even be matched.
If you built the program, you need to have even more people dedicated to actually maintaining it on top of the engineers you hired to write the software.
Most modern data matching solutions have been deployed for hundreds of clients in various situations and industries. They’re constantly being worked on to improve their work based on real-world situations. That’s hard to replicate in a one-off, bespoke custom build.
A great data matching program can also match millions of records in minutes with batch ingestion and find matches in sub-seconds with real-time ingestion. These features reduce the wasted time spent on the tedious processes of eliminating the duplications and inconsistencies that drive down the quality of your data.
Buying > Building
Data matching is critical for any organization that wants to use quality data to generate business outcomes. Custom-built solutions fail to deliver the accurate results businesses need consistently and reliably.
If you were to build your data matching solution, your organization would remain in a reactive data strategy. You would likely constantly be left correcting errors and cleaning up duplicates after the damage has been done. When you buy your data quality solution, your organization will become a lot more proactive. You will be able to catch duplicates at the point of entry and before they have the chance to impact your organization's more extensive data landscape.
Now that you’ve decided to buy a data matching solution, how do you choose the right one? Check out the top questions to ask of your data matching solution.