Company:Data Ladder

From HandWiki
Data Ladder
TypePrivate
IndustryData Quality
Founded2006
HeadquartersHartford, Connecticut, United States
ProductsDataMatch Enterprise, ProductMatch
Websitewww.dataladder.com

Data Ladder is privately owned, American-based software organization headquartered in Hartford, Connecticut. It provides data quality software for enterprise-level private, government and public sector organizations.[1]

Data Ladder was recognized as part of the Gartner Magic Quadrant research study on data quality vendors in 2013.[2]

The company’s software tools have been used in several research projects, including work with the Connecticut State Department of Education for a P20 WIN project.[3] The company was also part of a study on entity management systems, conducted by the University of Wisconsin-Madison.[4]

History

Data Ladder was founded in 2006. The idea was to help businesses get the most out of their data while decreasing reliance on IT. The company developed a software application that allows users to profile, cleanse, and match their data across disparate systems within a point-and-click interface.

While the toolkit provides full-spectrum data quality, Data Ladder’s focus has remained data matching and record linkage. Their flagship software, DataMatch Enterprise, has been rated faster and more accurate than industry-leading solutions in various independent studies [5] - a result of the company’s tetrahedral approach to data matching that combines establishing fuzzy matching algorithms with proprietary matching algorithms to detect matches just as a human would.

Services and Products

Data Ladder offers a suite of data matching and data enrichment services categorized into two main products; the DataMatch Enterprise and the ProductMatch. DataMatch Enterprise (DME), a highly visual desktop application with entity matching as the core of the solution. The application is specifically designed to resolve customer and contact data quality issues.[6]  It is also available in the Server Edition Module that allows organizations to perform record linkage on the database in the server.[7]

The ProductMatch offers automated data standardization, attribute extraction and classification for products across different databases. The application uses semantic recognition technology and machine learning to process product data from disparate sources, match and create hierarchical categorization on top of unstructured data.

Product Features

Data Ladder is recognized as one of the 15 major commercial entity matching systems[8] with support for multiple entity matching tables. One of the key characteristics of DataMatch Enterprise is the seamless integration with virtually any data source, including Big Data (Hadoop, MongoDB, Google Big Query, etc.), Social Media (Facebook, Twitter, etc.), File Formats (Excel, JSON, XML, etc.), Enterprise Applications (Dynamics GP, Office 365, QuickBooks, ZenDesk, etc.), and relational databases (Oracle, SQL Server, TeraData, IBM DB2, MS Access, etc.).

The suite performs tasks as data profiling, data matching, data cleanup, data deduplication and data standardization. In 2016, the company announced native integration with SalesForce CRM as part of their new feature addition to DataMatch Enterprise, along with other popular CRMs (Zoho CRM, Sugar CRM, MS Dynamics CRM, HubSpot, etc.).

The user can match databases by loading data into the application. The application will use a combination of matching algorithms to identify missing values, presence of non-printable characters, spelling mistakes and redundant field values. The user can clean and standardize the data. Basic transformation such as changing from uppercase to lowercase, to removing characters specified by the user and cleaning of email as well as of address using pre-defined data sources. Once the cleaning is performed, the user can begin the matching process.

Identity Resolution for Government Organizations

Identity resolution remains a significant challenge for government organizations, especially in areas of research and grant funding. DataMatch Enterprise has been used by government and public sector institutions to cross-match data from disparate data sources. Independent studies prove that the software can find matches with 96% accuracy (tested between 40,000 to 8 Million sample records), with the first matches produced within 15 minutes.[9]

In the absence of unique identifiers such as SSNs, Passport numbers or other similar sensitive information, identity resolution is a methodology that can provide an accurate view of the entity. It uses other data fields such as names, phone numbers, email addresses and physical addresses to generate a single source of truth.

Identity resolution takes into its stride data profiling, data analysis and data standardization – the three critical steps in cleaning data before it actually begins comparing them. The method is performed via the use of algorithms that parse, standardize, normalize and cleanses data.

Publications

  1. Provider of Simple and Affordable Data Cleansing and Matching Software [10]
  2. Listed in 10 Best Data Cleaning Tools to Get the Most Out of Your Data [11]
  3. A Record Linkage Study Conducted by BMC Medical Informatics and Decision Making [12]
  4. Top 10 Data Quality Tools [13]
  5. Top 10 Data Cleansing Solutions for the Enterprise [14]

References

  1. ↑ "Leader in Enterprise Data Cleansing & Matching Software by Data Ladder" (in en-US). https://dataladder.com/. 
  2. ↑ Friedman, Ted. Magic Quadrant for Data Quality Tools, Gartner, pgs. 11-13.
  3. ↑ "Report for Data Request: SDE-BOR Test Evaluation", BOR Office of Policy, Research & Strategic Planning, July 2014, pgs. 1-8.
  4. ↑ "Magellan: Toward Building Entity Matching Management Systems", University of Wisconsin-Madison, 2015, pgs. 4-7.
  5. ↑ "duplicates - Tools for matching name/address data". https://stackoverflow.com/questions/46007/tools-for-matching-name-address-data/127893. 
  6. ↑ "DataMatch Enterprise - Product Details". https://www.enterpriseguide.com/products/8003/datamatch-enterprise. 
  7. ↑ "DataMatch Software - 2020 Reviews, Pricing & Demo". https://www.softwareadvice.com/bi/datamatch-profile/. 
  8. ↑ "Executing Entity Matching End to End - A Case Study". http://pages.cs.wisc.edu/~anhai/papers1/umetrics-edbt19.pdf. 
  9. ↑ "DATA LADDER Data Quality Tools Software Reviews". https://www.360quadrants.com/software/data-quality-tools-software/data-ladder. 
  10. ↑ "Data Ladder LLC, a Provider of Simple and Affordable Data Cleansing and Matching Software, Announces the Release of DataMatch 2012". https://www.prweb.com/releases/2012/4/prweb9407941.htm. 
  11. ↑ Deoras, Srishti (2018-01-25). "10 Best Data Cleaning Tools To Get The Most Out Of Your Data" (in en-US). https://analyticsindiamag.com/10-best-data-cleaning-tools-get-data/. 
  12. ↑ Churches, Tim; Christen, Peter (December 2004). "Some methods for blindfolded record linkage" (in en). doi:10.1186/1472-6947-4-9/comments. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-4-9. 
  13. ↑ "10 Top Data Quality Tools". https://www.datamation.com/big-data/10-top-data-quality-tools.html. 
  14. ↑ Farhan, Rema. "Top 10 Data Cleansing Solutions for the Enterprise" (in en-GB). https://www.em360tech.com/data_management/tech-features-featuredtech-news/top-10-data-cleansing-solutions/. 

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