Matt Creason joined the Let's Talk Data podcast with SAP where he shared his perspective on architecture and design, where and how to get started.
What Is a Truly Intelligent Data Matching Solution Made Of?
The stakes for reliable data are bigger than ever. Legacy on-premises tools are being converted to hybrid or cloud-based solutions as the volume and variety of data demands a higher level of complexity.
Data must now suit the demands of many people rather than just a few. This entails making data more available to business users who do not have a background in Python, Soundex, or database administration.
However, putting data quality in the hands of the many does not imply allowing free rein and hoping for the best. It should include protections, such as simple controls that provide business users with the same complex reasoning that data scientists are accustomed to, but in a style that is easy to grasp and encourages (safe) experimentation or refining.
When it comes to data quality, businesses must:
- Remove bottlenecks in the master data management plan to save costs and boost agility
- Scale as required without having to continually learn new software or rebuild old infrastructure
- Use flexible, strong tools and products to empower people regardless of their skill level
So, what really constitutes a truly "Intelligent" matching solution?
The term "intelligence" is tossed about a lot these days. It's often difficult to distinguish if we're talking about artificial intelligence or the intellect of some government officials.
Except when it comes to data matching, it should be very apparent. "Intelligence" should mean more than just intuitive design and enterprise-ready controls. Through patented techniques, high performance, and scalable architecture, an intelligent data matching system should connect with your data and reinvent how people conduct matching activities.
Intelligent and Confident Matching
Consider this: every matching tool can generate matches, but are they the appropriate ones? Traditional matching algorithms are not built to deal with the uncertainty inherent in consumer data, nor are they effective at integrating that uncertainty to offer answers. An intelligent matching solution does not only rely on out-of-the-box deterministic or fuzzy matching algorithms to handle the complexities of consumer data; instead, they design proprietary methods that push the envelope and go a bit further.
While data matching is becoming increasingly popular, businesses are discovering that not all solutions can keep up with the volume of data they ingest on a daily basis. There's one thing these legacy solutions don't want you to know, whether they use a proprietary matching algorithm, traditional procedures, or a combination of the two.
To get the most out of your matching, the strategy must be supported by a powerful engine. That's because it's not just a matter of combining millions of entries and deleting obvious duplicates.
In a fraction of a second, accurate and really intelligent matching technology may examine innumerable variables in and between various data sources. Records must be reviewed contextually, compared, and scored across many elements, much like humans. Before selecting a matching solution, inquire whether it uses approaches such as candidate grouping and contextual scoring to handle enterprise-grade databases.
Scaling Towards the Future
Data management platforms must be able to scale with the business and the increasing volume of incoming data. A forward-thinking approach to dealing with the increasing volume and variety of client data can get you there without consuming limitless person-hours and resources. Most consumer or corporate data can be resolved in minutes thanks to a unique scoring engine backed by vast processing capacity. Look for partners who like pushing their matching engine to its limits, such as configuration settings that give you back control and enable for easy modification as your client data demands develop.
In today's world, we should strive to make data as accessible to business users as it is to engineers. Be aware of platforms that try to entice you with nice, flashy interfaces but lack the kind of deep setup that your data scientists are used to. When out-of-the-box defaults aren't enough, you'll need solutions that give the user back power through deep configuration settings. Drag-and-drop canvases expedite the process and allow anyone to save and complete difficult tasks without breaking a sweat.
Faster response time
To be honest, a lightning-fast solution is comprised of a number of characteristics. Performance and user experience innovations have given new meaning to the term "speed," offering responses in minutes rather than hours (or days!) with competing solutions or standard algorithms for customer data unification.
- Raw data is welcome: Truly intelligent systems allow you to provide your data as-is, raw, unclean, and without any wrangling.
- Code-friendly or code-free: Today's high-performance UIs enable anyone to reliably match and dedupe data without requiring substantial knowledge of Python or Soundex. For maximum control, look for systems that provide both code-free and code-friendly alternatives.
- Enhanced performance: Today's innovative matching systems use in-memory and multi-threaded processing to achieve scalable efficiency. Enterprise matching jobs are completed in minutes.
In the Future: Data Quality's Next Generation
When data management platforms originally gained traction in the digital realm, their products were primarily focused on one thing: merely hosting their data. However, as expectations and worries about privacy, laws, and compliance continue to rise, businesses are seeking something much more critical from their solutions: accuracy.
And the stakes for reliable data are bigger than ever. Legacy on-premises tools are being converted to hybrid or cloud-based solutions as the volume and variety of data demands a higher level of complexity. Businesses are growing less tolerant of data problems, such as mismatches or false positives, and they should be. Inaccurate data costs more, whether in terms of fees, fines, or income loss. The data quality solution of the future will demonstrate its worth in this competitive and complex market by maintaining accuracy where it belongs - as a core, central pillar of the business.
Interested in learning more about Syniti’s Data Matching technology? We invite you to register for our webinar: One Duplicate Is All It Takes: How to Prevent the Bad Data Domino Effect