Syniti introduces AI-driven matching solution to support both party and operational data - The EE

Syniti introduces AI-driven matching solution to support both party and operational data

Boston, United States – Syniti, a global provider in enterprise data management, announced the upcoming availability of its fully cloud-native Syniti Match, the rapid and precise AI-powered data matching software on the market that supports both party and operational data. Enterprise data is often riddled with errors and consistencies like typos, missing fields, duplications, and name variances. Those errors are compounded when dealing with multiple systems which is increasingly common as organisations continue to modernise legacy operations. Syniti Match is purpose-built to handle data complexity, no matter the shape, source, or type of enterprise data, including customer, ERP, supply chain and business data.

  • Improve productivity – Syniti Match can reduce the time spent on data matching jobs. It can match millions of records in minutes with batch ingestion and can find matches in sub-seconds with real-time ingestion.
  • Enhance customer relationships – Organisations can use reliable, matched data to build a unified, 360-degree customer profile to enhance targeting campaigns and drive meaningful, relevant experiences.
  • Increase profitability – Free up working capital by identifying duplicate spare parts to help increase profitability; match parts and align them with vendors to help identify millions in potential procurement savings as well as in volume discounts.
  • Proactively maintain data quality – Protect the people, processes, and applications that rely on accurate data to function by getting ahead of duplicates that degrade data quality. Real-time matching helps prevent duplicates from being created by identifying them at point of entry. This function can also aid an enterprise’s MDM strategy by matching master records across the company’s business application landscape, offering real-time mastery to help ensure a single source of truth is being used for key functions such as business operations and analytics.

Syniti has replicated the natural intelligence humans use when forming comparisons, and with Syniti Match, can deliver that capability at scale. AI, proprietary phonetic and fuzzy matching algorithms, and context-sensitive lexicons evaluate matches contextually, allowing the software to understand data based on what it is, rather than where it resides in a table. It maximises matches found while minimising false positives and can easily scale as business data grows in volume and variety. With a flexible SaaS-based model, Syniti Match can be deployed from anywhere, anytime – without installation.

Emily Williams, vice president of product alliances, Syniti, says: ““The ability to match both types of data quickly and accurately fills a very necessary gap in the market as it benefits every part of an organisation from operations to sales and marketing. With Syniti Match, we are reducing the wasted time spent on the tedious processes of eliminating the duplications and inconsistencies that drive down the quality of your data.”

Kevin Campbell, chief executive officer, Syniti, says: “Organisations today simply can’t afford to base any decisions on poor quality data. At Syniti, we keep finding new and innovative ways to enable our customers to achieve the accurate and trustworthy data they need to run and grow their businesses.”

Jon Severn, circulation director, MJH Lifesciences, says: “MJH Lifesciences is the largest privately held medical media company in the U.S. We have a constant flow of new data sources into our system which makes keeping the data free of duplicates very difficult. Previously, we were de-duping files manually, and spending more time than we would like doing so – and delivering less accurate results. With Syniti Match, our current process is much more accurate, resulting in greater audit compliance and we have reduced incidents of unintentional duplicates.”

Register for One duplicate is all it takes: How to prevent the bad data domino effect to learn about establishing a proactive data quality strategy and what you should be looking for in a data matching solution.

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