SSIS Integration with DataMatch Enterprise

Data Ladder’s seamless integration with Microsoft SQL Server Integrations Services (SSIS) empowers users to collect data from any source, clean, compare, and enrich it during migration and integration operations, to gain immediate insight for actionable intelligence.

SSIS integration, SSIS Integration

Integrating Data Ladder’s best-in-class data cleansing and matching solution with Microsoft’s integration and ETL (extract-transform-load) platform allows users to build seamless integration flows with built-in data quality.

With DataMatch Enterprise, you can intuitively test and build scalable data profiling, data cleansing, and data matching configurations and save them in a project. That project can be called within an SSIS data flow to fuse the industry’s fastest and most accurate data matching solution with Microsoft’s stellar ETL capabilities to help you integrate clean data across the enterprise.

Gain immediate insight with actionable intelligence using data you can trust.

See how independent studies rate Data Ladder’s data quality solutions when compared against industry leaders:

Rated Fastest and Most Accurate Data Cleansing and Matching Software

Features of the solution
Data Ladder
IBM Quality Stage
SAS Dataflux
In-House Solutions
Match Accuracy (Between 40K to 8M record samples)
Software Speed
Very Fast
Purchasing / Licensing Costing
80 to 95% Below Competition
Time to First Result
15 Minutes
2 Months+
2 Months+
3 Months+
Above tests were completed on 15 different product comparisons with university, government, and private companies (80K to 8M records). This includes the effect of false positives.
Need multi-threaded. in memory, no-SQL processing to optimize for speed and accuracy. Speed is important, the more match iterations you can run, the more accurate your results will be.
Includes base license costs. 2014 prices or later, in-house, includes salary + benefits. Note in-house implementations had a 10% chance of losing in-house personnel, so over 5 years half of the in-house implementations had lost the core member who ran and understood the matching program.
A metric for ease of use. This is the time to first result, not necessarily full cleansing.

Integrating DataMatch Enterprise with Microsoft SSIS

Let’s say you have an SSIS data flow that receives customer information from a variety of sources and then integrates this data into your Microsoft Azure database, which contains your customer master data.

Trouble is, without cleaning and comparing all those different sets of customer data you’re processing, you will inadvertently end up polluting your master data.

By plugging a DataMatch Enterprise project into your dataflow, you can quickly and easily conduct a cogent profile analysis of data quality, apply a variety of pre-built data cleansing transformations, leverage 300,000+ standardization rules for name, address, and phone matching, and finally compare records to deduplicate, enrich, and create golden records.

Once that’s done, your data continues its journey to your final destination: your customer master database.


Change the casing of capital to lower case, etc. Example: John to john.


Expand or contract abbreviations, for example, CA to California.

Standardization Libraries:

Over 300,000 standardization rules for name, phone and address data.


Add or remove punctuation.

Search and Replace:

Replace portions of a string


Use regular expressions to extract, validate, etc.

Data Matching Operations Available:

Survivorship/Golden Record

Automatically determine the most complete record and enrich missing fields from other entries.

Proximity Matching

Our Address Verification addon uses lat/long coordinates up to the 6th decimal to pinpoint location within inches and identify duplicates.


Identify and consolidate records that are members of the same household.

Search and Replace:

Find common data elements between multiple lists and/or use suppression to find just the data unique to each individual list.

With Data Ladder, You Get:

Fully visual, intuitive interface

Complete set of data cleansing tools

Semantic matching for unstructured data

Support for disparate data sources for record linkage

Affordable package; costs 95% less than comparable solutions

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