DQM is a data quality software dedicated to helping business users get the most out of their data through data matching, profiling, deduplication, and enrichment tools. Whether it’s matching millions of records through our fuzzy matching algorithms, or transforming complex product data through semantic technology, DQM’s data quality tools provide a level of service unmatched in the industry

Enterprise Data Cleansing

DQM includes multiple proprietary and standard algorithms for detecting phonetic, fuzzy, miskeyed, and abbreviated variations, and is a scalable platform for deduplication and record linking, suppression, enhancement, extraction, and standardization of business and consumer data.

Advanced fuzzy matching algorithms for up to 100 million records at one go, and the processing speed to do it in near realtime.

Our tool finds and links customer data, consolidate data across multiple sources, and removed duplicate and unwanted records, and can survive the best data forward - quickly and easily improving your warehousing, marketing and mailing performance. With DQM, you can automate daily maintenance functions with our inbuilt functions that can be integrated to your exist processes.

PRODUCT MATCHING

Best in class Semantic Technology transforming unstructured data into human recognizable data that can be simply worked or reassembled and standardized.

FROM PRODUCT DATA CLEANUP TO GOVERNANCE:

Unstructured data unfortunately brings a lack of clarity and can become unmanageable very quickly. This can leave customers without the important information they need to conduct business in a smart manner. DQM’s Product Matching gets your unstructured data right, managing your data quality, data integration and data governance tasks all in simple processes.

If we look at a simple task of matching product codes below, as a human we can easily see that three of these products are the same, but for most matching process this would be a little harder. Often text matching will get this process wrong giving false matches or miss that match altogether.

LG 42CS560 42-Inch 1080p 60Hz LCD HDTV

LG 42 Inch 1080p LCD HDTV

LG 42” 1080p LCD HDTV

LG 50 Inch 1080p LCD HDTV

Panasonic LED TV TC-50CS560 (50")

The Semantic Technology in DQM looks at the values and classifies the data into tokens then matches and scores the data. This process also makes brand and feature normalization a simple task. The scoring allows for data to pass through if enough of the data matches, and the Governance process allows for data to be approved if it has been targeted to a master list. With the scoring attached this become a simple process to accept or refine the matching.

DQM automates the creation of reliable data for organizations with semantic technology that translates unstructured data into standardized information that makes sense. We combine this with being able to combine data from multiple sources and the lighting fast processing speed to deliver you powerful data asset.

ADDRESS VERIFICATION

Check the validity and deliverability of a physical mailing address with address validation and geocoding tools.

Provides;

1. •Enriched data through the addition of up-to-date geocodes and demographic data
   

2. •On premise solution allows user privacy, with no need to send private information through the cloud
   

3. •Built on Australian data, and data rules
   

4. •Address parsing
   

5. •Data standardization 
   

6. •Matching and scoring
   

7. •Holds over 15 million G-NAF Lat, Long address points ( with 4 levels of resolution of GPS data )
   

Standardization converts an address to a standard format by correcting the address and adding missing information. The advanced record linkage functionality to create standardize inputs for data warehouses, and marketing lists. By appending a Address ID to each of you data records  you can ensure you mail data at one per household simply and quickly.

Most matching address engines are not built for Australia, and cannot understand some the fun things we do for and to addresses here. Simple things like reseting the street numbers on a change of postcode, or calling a street name “HIGH STREET ROAD” often upsets most tools. We Built DQM in Australia for Australian data, then we applied the rest of the worlds data sets to it. It is even able to process double bite data (Chinese data) if your into that sort of thing.

To visualize data after it has  been standardized, cleaned and matched is a snap, and by appended the geocodes at different levels can cluster data up simply, and based on what makes sense to consume or display the data.

DQM can append any data assets or modeled data you may have to any match address data, items like sales regions or service areas, so all of your processed data is useful for any analysis you want to do.