Best Data Matching Software for SQL Server

Clean & Match, WinPure's award winning data cleansing and data matching software suite is designed to improve the accuracy of consumer or business data. This software suite can be used to clean, correct, and deduplicate mailing lists, spreadsheets, CRMs, and databases. WinPure™, Clean & Match will save your business money and time. * Increase accuracy of any list, spreadsheet, database, CRM, etc. * Windows software is locally installed so you don't have to worry about security. All processing takes place on your own systems. * Use built-in phonetic and fuzzy match algorithms to save hours cleaning duplicate records from your databases or lists. * Low-cost licences with World Class Support & Training. * Free Demo with Live Online Training Available

Senzing® entity resolution API software provides the most advanced, affordable, and easy-to-use data matching and relationship detection capabilities available. With Senzing software, you can automatically resolve records about people, organizations and their relationships in real time as new data is received. The highly accurate and complete views Senzing software delivers allow you to reduce costs and enable new revenue opportunities. Senzing provides a set of libraries that that can be deployed on premises or in the cloud, in a variety of ways, depending on your architecture and environment requirements. Data remains in your ecosystem and never flows to Senzing, Inc. Minimal data preparation is required when and no tuning, training or entity resolution experts are needed. A free proof of concept can be completed in about six hours on AWS or bare metal. You can try the Senzing API on up to 100K records for free.

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.

WinPure™, MDM, is a master data management system that aligns with your company to provide a single view of all your data. It also includes functions and features to help manage your data. These features are available as a free download from the clean & matching enterprise edition. They have been repurposed for easy web-based data prep and MDM operations. There are many ways to clean, standardize, and transform data in dozens different formats. Industry-leading data matching and error-tolerant technologies. It is simple and easy to configure survivorship technology. The general benefits include a lower cost and a faster time to market. It is easy to use, requires minimal training, and can be implemented quickly. Faster MDM and systems deployments will result in better business results. Simple and easy data preparation tools, batch loads that are faster and more accurate. Flexible and efficient interconnectivity via API with other databases and systems. Accelerate the time it takes to create synergies in M&A.

Matchit®, the core of our matching software, is specifically designed to produce results that are human-like. It does this at scale and without any preprocessing. Matchit uses Artificial Intelligence, which is a proprietary phonetic algorithm and lexicons as well as a contextual scoring engine to overcome the inconsistencies and challenges often found in business and contact data. Matching logic is a combination function and off-the shelf fuzzy algorithms that produces an alphanumeric value. Conventional matching solutions require the user to define it. This alphanumeric value (or'match key') is used to compare two records and ultimately find matches. Matchit is not like traditional matching solutions. It doesn't rely solely on one comparison between match keys to find matches. Matchit evaluates records contextually by running a variety comparisons and scoring them individually to determine the similarity between all elements in your data.