You have successfully collected data. Millions of records in multiple formats reside in numerous disconnected datasets - from multiple systems, regions, sources and timelines.  DataStitch makes them usable by cleaning and connecting them.

On-Premise Execution
DataStitch works on on-premises data. Your data does not have to be uploaded on any external server and does not have to leave the organization's systems (or even your desktop). There is no communication between DataStitch and any external system and hence it can run without connectivity in a standalone mode. 

Data Volume
DataStitch​ has been tested and used for tens of millions of records. Still it does not necessarily require a server and performs well on a fast desktop machine. DataStitch​ is currently supported on Windows. Special builds on Linux can be provided. 

No Implementation 
DataStitch finds patterns across dozens of dimensions automatically and requires no setup. It works on CSV files as input and outputs the records in same format, grouped by similar clusters.  The one important  consideration is the name of header fields within the CSV file, which plays an important part in the way that field is treated by the system. 

DataStitch Evaluation 
Yes, a fully functional version of DataStitch​ application is available. 

DeDuplication Quality
The algorithms used in DataStitch are exactly same as ones used in our other products which are hosted on cloud for CRM Data Quality, Enterprise Contact Management or as plugins for standard CRM like Zoho. These have been tested on hundreds of millions of records since 2016. The learnings are part of executable software. 

Here are some examples of results that can be expected. Majority of these are close to real cases of duplicate data found by algorithms during their use. The real data has been changed for confidential reasons, but the discovered pattern is intact.

Full Name - First Example

Example of Name de-duplication taken from a medical institution in India. Combination of salutations, qualifications and swap of first name and surnames were considered​:

• Sheela Joshi
• Dr. Sheela Joshi, PhD
• Mrs. Joshi, Sheela
• Sheela Joshi, M.B.B.S.
• Joshi Sheela

Full Name - Second Example

​Example of Name de-duplication taken from a warranty registration database in Philippines:

• Ivy Mathew Griffin
• Ivy M. Griffin
• Ivy Matt Griffin

Full Name - Third Example

A powerful example of algorithm's  capabilities. Example of Name de-duplication taken from a database of a South Asian country:

• Mohammed Qasim
• Mohammad Kasim
• Mohd. Kasim
• Mhd. Kasim
• Md. Kasim
• Muhammad Kasim

Full Name - Fourth Example

Example of variations of a name considered in a suspected duplicate cluster: 

• Casey Pabilla
• Cassey Pabilla
• Caseyy Pabilla
• Caesey Pabilla
• Caseey Pabilla
• Caseey Pabillaa
• Caseey Pabella
• Caseeey Pabilla
• Caasaay Pabilla
• Caasey Pabilla
• Caseey Pabilla
• Casey Pabillla
• Casey Pabellla
• Casey Pabella
• Casiy Pabilla
• Casii Pabilla
• Casey Pabiilla
• Casey Pabilla
• Caseeyy Pabilla
• Caassey Pabilla

Address - First Example

This is one of the best example of Address de-duplication, highlighted by algorithm ​within a massive CRM database in India.  Not only there are inconsistent abbreviations and spelling errors, the old and new official name of the city has been detected as duplicate: 

• 43/2, Industrial Road, Sector 65, Bangalore
• sector 65, bangalore - 43/2 (industrial) rd
• 43\2, sectorr 65 - industrial rd,, BENGALURU
• 43 2 indl rd sec 65 bangalore 

Address - Second Example

An example of Address de-duplication, from Singapore:

• #01-33, 92 Whampoa Annexe, Causeway Drive
• 01 33, 92-whampoa annx, causeway dr
• 01--33 whampoa anx #92, causeway drv
• #92. Whampoa Anex. Causeway= Drive, 01,33

Address - Third Example

​An example of duplicate address cluster from Australia. Note the abbreviations and variations of state name captured in duplicate cluster: 

• 7th Floor, 43/2 Miller Plaza, Industrial Highway, Sydney, New South Wales
• 7th fl miller plz, (industrial) hw – 43 2, sydney, nsw
• Seventh Floor. Indl Hway. 43-2 Plaza. Miller. Sydney. N.S.W.
• Flr 7th, #43—2, miller pz, sydney indl hwye, ns.w

Mobile Numbers

The algorithm ​finds mobile numbers in multiple formats and groups the duplicate together. Here's an example of such a group:

• +63-906-222-1520
• 0063 9 06 22 21 520
• +(906).222.1520
• 0-906-22-21-520
• 9 0 6 2 2 2 1 5 2 0

Company Name - First Example

An example of Company Name de-duplication from Philippines:

• HPE Philippines Incorporated
• HPE Philippines Inc.
• HPE Phils, Inc.
• H.P.E. Incorporated
• HPE Inc

Company Name - Second Example

Another similar example of Company Name de-duplication, from India:

• HPE India Private Limited
• HPE India Private Ltd.
• HPE Pvt. Ltd. – India
• H.P.E. Pvt. Ltd.
• HPE Limited

Name + Mobile Number

​Example of 5 duplicate Name and Mobile Number combinations as found by system:

Name: Mohammad Kasim 
Mobile: +91-98336-90611

Name: Mohd. Kasim
Mobile: 0091 98 33 69 06 11

Name: Mhd. Kasim
Mobile: (9833) 690-611

Name: Md. Kasim
Mobile: 0-98336-90611

Name: Muhammad Kasim
Mobile: 9 8 3 3 6 9 0 6 1 1

Name + Date of Birth

Example of 4 duplicate Person's Name and Company Name combinations found: 

Person's Name: Narendra Bajpayee
Date of Birth: 15/11/1984

Person's Name: Narindir Bajpayee
Date of Birth: 11-15-1984

Person's Name: Narender Bejpeyee
Date of Birth: 15.11.84

Person's Name: Nariinder Baajpayii
Date of Birth: 1984, novembr 15

Name + Company

​Example of 4 duplicate combinations of Person's and Company Names as found by DataStitch:

Person's Name: Sanjiv Kumar
Company's Name: HPE India Private Limited

Person's Name: Sanjeve Kumarr
Company's Name: HPE India Private Ltd.

Person's Name: Sanjeev Qumar
Company's Name: HPE Pvt. Ltd

Person's Name: Sanjive Koomar
Company's Name: HPE Limited

Website URL

DataStitch ​groups different Website URLs which refer to the same page in a single cluster. Here's an example of such a group:

• contactous.com
• http://www.contactous.com/index.htm
• www4.contactous.com/?query=malaysia
• https://contactous.com:8080/

Manage Duplicate Records
DataStitch automatically indexes names, addresses, dates and text fields, according to their definition. Consequently, merging of duplicate records based on a field or a selection of any number of fields is a common choice. During the merge operation, you can select the key which you would like to be indentified as a unique one (eg, Full Name + DateOfBirth, Full Name + Mother's Maiden Name + IC-Number-Text). You can define the definition of master record and DataStitch will process tens of thousands of records in minutes. While doing this merge operation, a new dataset is created and the original dataset is not modified. You also have the option of downloading the original dataset with unique cluster keys without merging within DataStitch. 

Dataset Operations
Powerful set manipulation operations can be performed between two datasets, based on the common fields between them. The common indexed values are used to join two datasets, find common records between them, subtract one from other or more complex operations. As DataStitch uses its indexed values, field values of "Twelve Newton Crossing" or "03-Aug-1968" get matched to "12 newton xing" or "3 augst '68" during these operations making them very useful to data custodians.  

DataStitch Licensing 
Tens of millions of records can be imported to an evaluation version of DataStitch. The application's licensing works on the keys which enables the processed data to be exported out. 

availability: august '21

Ask for an Evaluation Copy of DataStitch