Choosing a data matching software is more than storing your data in a warehouse or CRM. Data matching solutions should intelligently analyze your...
What Makes Up Next-Gen Data Matching?
A truly intelligent data matching solution interacts with your data and revolutionizes how users perform matching tasks.
Data must now meet the needs of many, not the few. That means making data accessible to more business users without the need for a background in Python, Soundex, or database administration.
But putting data quality in the hands of the many shouldn’t just mean unleashing free reign and hoping it all works itself out. It should come with safeguards - intuitive controls that give business users the same advanced logic that data scientists are used to, but presented in a way that is easy to understand and invites (safe) experimentation or refinement.
When it comes to data quality, enterprises need to:
- Eliminate blockages within the master data management strategy to lower cost and increase agility
- Scale as needed without constantly learning new software or overhauling the existing infrastructure
- Empower users no matter their skillset with flexible, powerful tools and products
So what makes up a truly “Intelligent” matching solution?
The word “intelligence” gets thrown around generously these days. Whether we’re talking about artificial intelligence or the brainpower of some government leaders, sometimes it’s all a bit vague.
Except that with data matching, it should be exceptionally clear. “Intelligence” should be synonymous with more than simply intuitive design and enterprise-ready controls. An intelligent data matching solution should interact with your data and revolutionize how users perform matching tasks via proprietary approaches, powerful performance, and scalable architecture.
Intelligent Matching with Confidence
Think of it this way: any matching tool can generate matches - but are they the right matches? 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 things a little further.
While data matching continues to grow in popularity, enterprises are finding that not all solutions can keep up with the amount of data they ingest on a daily basis. Whether utilizing a proprietary matching algorithm, traditional approaches, or a combination thereof, there’s one thing these legacy solutions don’t want you to know.
To truly get the most out of your matching, the approach needs to be backed by an engine primed for power. That’s because it’s not just about merging millions of records and removing the clear-cut duplicates.
Accurate and truly insightful matching technology can look at countless variables in and between various data sources in a fraction of a second. Records have to be evaluated contextually, compared, and scored across various elements much in the same way humans do. Before choosing a given matching solution, ask if they utilize methods like candidate grouping and contextual scoring to process enterprise-grade databases.
The platforms that manage your data need to be able to grow with the business and the increasing amount of incoming data. A future-ready approach to tackle the growing volume and variety of customer data can get you there without burning through endless person-hours and resources. A unique scoring engine backed by massive computing power can resolve most customer or business records in a matter of minutes. When it comes to matching, look for partners who love pushing their matching engine to the max, like configuration settings that give you back control and allow for easy customization as your customer data needs grow.
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, but lack 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.
- Raw data invited: Truly intelligent solutions let you bring your data as-is, raw, dirty, and without an ounce of wrangling required.
- 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.
- Optimized performance: Innovative matching solutions today harness in-memory and multi-threaded processing to deliver scalable efficiency. Translation: enterprise matching jobs are tackled in minutes.
Looking Ahead: The Next Generation of Data Quality
When data management platforms first started to make headway in the digital landscape, tools largely centered around one thing - simply housing their data. But as expectations and concerns around privacy, regulations, and compliance steadily grow, businesses are looking to get something much more imperative out of their solutions: accuracy.
And the stakes for accurate data are higher than ever before. Legacy, on-prem tools are being migrated to hybrid or cloud-based solutions as the increasing volume and variety requires a higher standard of sophistication. Businesses are becoming less and less tolerant of errors in their data, such as mismatches or false positives - and they should be. Whether in terms of fees, fines, or revenue loss, inaccurate data comes at a higher price. The data quality solution of tomorrow will prove itself in this competitive and complex space by keeping accuracy where it should be - as a core, central tenet to the business