It’s time we talked about it. Enough’s enough. I mean, really, why are we settling for less? When it comes to our data, there’s no reason we should be struggling with the same old issues: slow time-to-answers, a complex UX, endless wrangling, all for inaccurate results that require manual review. 

It’s true – legacy data matching solutions often fail to deliver accurate results without sacrificing the rest: in other words, delivering on velocity without forgoing value. 

Conventional approaches don’t provide the kind of intelligent results that are truly accurate. What’s more, they’re often bloated platforms loaded with superfluous features. Instead of being helpful, they’re not user-friendly, and they’re hard to train, maintain, and scale. 

And while vast improvements have been made in the way of customer data management, maintaining the quality of that data has been missing an accurate, usable solution that brings data management to any business user’s fingers and decrease time-to-insights.

…Up until now, that is. 

Legacy solutions are failing us…

When it comes to dealing with customer and business data, conventional solutions aren’t equipped to fully account for this kind of complexity. Instead, they’ve left a lot of room for growth in terms of efficiency, scale, and match rate. 

Before 360Science, data quality solutions”

  • Weren’t user-friendly: Cumbersome interfaces that require programming skills and a background in matching algorithms aren’t user friendly, and they certainly don’t empower anyone to tackle the big questions. Business users are more likely to turn to Excel spreadsheets for answers than deal with a confusing software.
  • Didn’t grow with the business: The rate of technological advancements is fantastic, unless you’ve just adopted a piece of software that’s already out-of-date. Enterprises manage more diverse amounts of data than ever before, and solutions need to be able to adapt to this quickly changing marketplace. 
  • Not equipped for Big Data: Legacy solutions may be just fine for small datasets, but they show their age when trying to deal with tens to hundreds of millions of records. Enterprises are already busy enough juggling the volume, veracity, and variety of their data without having to worry if their system can keep up.
  • Delivered inaccurate, unreliable results: It wasn’t long ago that a deterministic approach was used solely to match data. This approach isolates only the ‘certain’ data and severely increases false matches.
  • And access to data was still siloed: Legacy approaches kept data management largely in the hands of a developer, programmer, or IT employee. Even a simple merge between disparate datasets meant researching what schema each department was currently using, and writing lengthy conversions, cleaning, and transformation rules. No one else in the organization knew how to do this kind of painstaking wrangling (and frankly, no one else would want to).

…and it comes at a high price.

With customer data, it’s highly likely that the same customer has records in multiple databases with incomplete, imprecise, or contradictory data. As a result, we often have a limited understanding of our customers, preventing us from building the kind of customer experiences that deepen relationships and drive revenue. 

Throw in the sheer volume of customer data generated on a daily basis, new software and new formats of data, and maintaining the quality of customer data seems downright impossible.

At first glance, it may seem like all data matching solutions produce the same results, but this couldn’t be further from the truth. Behind every platform is an approach that can ultimately make or break your data quality. In the end, you’ll want a matching solution that intelligently analyzes your data with human-like perception and can do so at scale.

So what makes up a truly “Intelligent” matching solution?

The word “intelligence” gets thrown around generously these days. Some of what brands are calling “artificial intelligence” are in fact narrow intelligence at best. Scour their site and you’ll be hard-pressed to find exactly what “AI” actually means.

Except that with data matching it should be exceptionally clear. “Intelligence” means more than intuitive design and enterprise-ready controls; via proprietary approaches, powerful performance, and a scalable architecture, an intelligent data matching solution interacts with your data and revolutionizes how users perform matching tasks. 

Algorithms Designed for Matching

Traditional matching algorithms are not designed to handle the uncertainty inherent to customer data, nor are they good at combining that uncertain data to provide answers. An intelligent matching solution doesn’t just rely on out-of-the-box deterministic or fuzzy matching algorithms to handle the intricacies of customer data – they develop a proprietary means that push the envelope and take accuracy much further. 

Performance Power that Scales

Think of it this way: if you’re selecting a data matching solution, shouldn’t the engine that powers it be a little more “muscle car”, and a little less “unicycle”? We’re talking a unique scoring engine backed by massive computing power to resolve customer records, with configurations refined and customized to ensure the best match quality for your customer data. This approach surpasses deterministic and fuzzy matching and provides unparalleled match rates at scale, even when the data systems don’t have exact linking keys. 

Future-Ready Scale

The platforms that manage your data needs to be able to grow with the business and the increasing amount of information it’s bringing in. A future-ready approach is ready to take on with growing volume and variety of customer data, and can get you there without burning through endless man-hours or resources. 

User-Empowering UI

In this day in age, we should be seeking to make data just as accessible to business users as it is to engineers. Be wary of platforms that try to lure you in with flashy, pretty interfaces, and looking for the kind of deep configuration that your data scientists are accustomed to. When out-of-the-box defaults aren’t enough, you’ll need solutions that put the control back in the hands of the user with deep configuration settings. Drag-and-drop canvases speed up the process and let anyone save and perform complex jobs without breaking a sweat. 

Faster Time to Answers

Truth be told, it’s not one but a handful of traits that truly make up a blazing-fast solution. Innovations in performance and UX have brought new meaning to the term “speed”,  delivering answers in minutes as opposed to hours (or days!) with competing solutions or traditional algorithms for customer data unification.

  1. Raw data invited: Intelligent solutions let you bring your data as-is, raw, dirty, and without an ounce of wrangling required. 
  2. Code-friendly or code-free: These days, high-performance UI’s allow anyone to safely match and dedupe data without extensive background in Python or Soundex. Look for solutions that offer both code-free and code-friendly options to maximize control. 
  3. Optimized performance: Innovative matching solutions today are harnessing in-memory and multi-threaded processing to deliver scalable efficiency. Translation: enterprise matching jobs are tackled in minutes.

So what’s in store for the next generation of data quality solutions? Personally, I’m interested to see what this new generation of business users will do now that they finally have all this data within reach. This is the first time in history anyone can access an intelligent matching solution and perform complex jobs with ease. It’ll be interesting to see where we go from here, and what is possible with that kind of access.

It’s an exciting time for data quality solutions. We’re finally moving past the legacy solutions of the past and challenging platforms with new advancements in technology. These advancements have opened up the gateway for innovation for those who are looking for it – and know how to use it.