Data quality for government agencies
Trusted By
Trusted By
Did you know?
How bad data affects public sector data?
Inter-agency matching limits data reliability
Duplicate records
Repeated copies of the same entity increase the complexity in creating a holistic view of a specific population segment.
High false negatives
Inaccurate record linkage processes can fail to detect fuzzy, phonetic mis-keyed and abbreviated variations resulting in fewer matches.
Long cycle times
Duplicate and redundant records cause a surge in data bandwidth, increasing the time taken to process records.
Unreliable planning data
Compromised research integrity due to inaccurate public data can hinder efficient allocation of public policy resources and funds.
Lack of unique identifiers
Tracking individuals using unique identifiers across multiple agency databases whilst protecting PII data is a challenge.
Mismanagement of public funds
Poor visibility into financial and accounting data can cause overpayments to contractors and delayed collections from debtors.
Solution
DataMatch Enterprise – A robust cross-jurisdictional matching solution
Customer Stories
See what our clients are saying...
The idea of linking two groups of records was overwhelming for the research department. The process would be very time-consuming and threaten the timeliness and process of the research activitie'
DataMatch Enterprise™ was much easier to use than the other solutions we looked at. Being able to automate data cleaning and matching has saved us hundreds of person-hours each year.
It scales our time very well, I can’t quantify in dollar terms since it’s new, but I would say that it represents thousands of dollars since it’s time that is not being spent by our employees.
Business Benefits
What’s in it for you?
Better regulatory compliance
Create uniformity in formats, naming conventions, and patterns to ensure accuracy and consistency in data reporting for compliance purposes.
Keep track of anomalies
Run routine audits of databases using data profiling to reveal spelling, punctuation, casing errors and null values.
Uncover missing matches
Detect fuzzy, phonetic, mis-keyed, and abbreviated variations to identify higher cross-jurisdictional matches across multiple states and territories.
Enhance inter-agency matching
Create master IDs to link individuals across disparate databases operated by different agencies without compromising PII data.
Expedite request management
Automate record linkage and cleansing processes through real-time API flows or batch schedules to process requests faster.
Effective policy planning
Maintain reporting and data integrity to effect public policy decisions such as funds appropriation and resource allocation.
Want to know more?
Check out DME resources
Merging Data from Multiple Sources – Challenges and Solutions
Oops! We could not locate your form.
Address standardization guide: What, why, and how?
Inaccurate and incomplete address data can cause your mail deliveries to be returned. In fact, the US postal service handled 6.5 billion pieces of UAA
What is data integrity and how can you maintain it?
While surveying 2,190 global senior executives, only 35% claimed that they trust their organization’s data and analytics. As data usage surges across various business functions,
Address standardization guide: What, why, and how?
Inaccurate and incomplete address data can cause your mail deliveries to be returned. In fact, the US postal service handled 6.5 billion pieces of UAA
What is data integrity and how can you maintain it?
While surveying 2,190 global senior executives, only 35% claimed that they trust their organization’s data and analytics. As data usage surges across various business functions,
Guide to data survivorship: How to build the golden record?
92% of organizations claim that their data sources are full of duplicate records. To make things worse, valuable information is present in every duplicate that
