Data Matching

What to Look for in Data Matching Solutions

Choosing a data matching software is more than storing your data in a warehouse or CRM. Data matching solutions should intelligently analyze your data.

When it comes to dealing with data, many conventional solutions fall flat as technology and growth quickly outpace them. Even despite the slow time-to-answers, the endless wrangling, and the complex UI’s, the reality is most data quality software isn’t equipped to fully account for the kind of complexity that goes with data matching and unification. Instead, they’ve left a lot of room for growth in terms of efficiency, scale, and match rate.
Until now, data matching solutions have often failed to deliver accurate insights without sacrificing the rest: in other words, delivering on velocity without forgoing value.
Even as stakeholders ramp up investment in data quality and matching software, many “Artificial Intelligence” options don't provide the kind of experiences or insights that are truly intelligent. Instead, we receive bloated platforms loaded with superfluous features. Instead of being helpful, they’re not user-friendly, and they’re hard to train, maintain, and scale.

Before Syniti, Traditional Data Quality Software: 

  • Was Not 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The rate of technological advancements is fantastic unless you’ve just adopted a piece of software that’s already out-of-date.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.  
  • Was Not Equipped for Big Data: Legacy solutions may be just fine for small datasets, but they show their age when dealing 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, Untrustworthy 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. 
  • Left Access to Data Siloed: Legacy approaches kept data management mainly 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). 
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 things like the sheer volume of customer data businesses generate daily, onboarding new software, managing endless types of data, and maintaining customer data quality seems downright impossible.
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What to Look for When Investing in a Data Matching Solution 

Choosing a data matching software or solution is more than simply storing a large quantity of data in some warehouse or CRM. Software embedded with data quality and matching solutions should intelligently analyze your data with human-like perception and do so at scale. 
Via proprietary approaches, powerful performance, and scalable architecture, an intelligent data matching solution interacts with your data and revolutionizes how users perform matching tasks. Let’s break it down: 
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 about 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 need 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 increasing volume and variety of customer data and can get you there without burning through endless hours or resources.  
User-Empowering UI 
In this day and age, we should seek 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 look 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 an extensive background in Python or Soundex. Look for solutions that offer both code-free and code-friendly options to maximize control.  
  3. Optimized Performance: Today, innovative matching solutions are harnessing in-memory and multi-threaded processing to deliver scalable efficiency. Translation: enterprise matching jobs are tackled in minutes.

Read our White Paper: The Definitive Guide to Data Matching

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.

To learn more about Syniti's enterprise-grade, AI-driven data matching solution, click here.

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