Helping government institutions with identity resolution, cross-jurisdictional matching, and data cleansing.
Duplicate and false identity records are common and a significant challenge in identity management systems and government databases. It is often inevitable and tedious to deal with identity duplication, especially if data is streaming in from multiple sources and jurisdictions. Traditional methods rely on unique identifiers such as SSNs, DOBs, to detect matches, however, such attributes also vary across systems in terms of structure, unintentional human errors and in some cases intentional deception.
Duplications, mismatched information, incomplete or invalid information can have devastating consequences. Modern data quality management and data cleansing solutions do more than just match names. Data Ladder, for example, has a comprehensive dashboard that you can use to profile data, identify duplicates or close matches and merge data from different sources to obtain a single view of truth.
In an age of electronic data, it is imperative for federal, state and local governments to invest in the right data solution tools and ensure that they have data they can trust.
Entity resolution refers to the process of finding duplicates and close matches of the same individual (or entity) in a single or across multiple databases. Data Ladder’s user-friendly dashboard helps you identify records across tables, fields and data sources to identify duplicates of a single entity.
Entity resolution may also lead to identity resolution; however, identity resolution is not dependent only on finding duplications. Missing values, incomplete attributes, incorrect or erroneous data are all areas that make identity resolution a significantly complex challenge. Several techniques are used to detect flawed, concealed or false information. Data Ladder employs these techniques in its robust DataMatch Enterprise platform that lets you use special characteristics of identity records to identify flawed data.
Government data is usually sensitive and has serious consequences if they are flawed. With data streaming in from multiple purposes it becomes difficult to cross-check individual details and ensure that the data is error-free. DME allows you to profile your data, ensuring that there are no typos, incomplete or invalid fields in the data. You can then choose to clean your data in batches or in real-time.
Secure, on-premise software makes unlimited address verification easy. DataMatch Enterprise Server Edition comes equipped with address validation and geocoding technology, which helps verify and standardize your address lists. Standardization converts an address to a standard format by correcting the address and adding missing information (such as a zip code or a suﬃx).
It is then compared against a list of valid addresses to determine validity. You also have the ability to enhance each matching address and add ZIP+4 level latitude and longitude values for the best in mapping precision.
Agencies from different departments and states have shared data for decades to fulfill purposes of research, public safety, civil intelligence and much more. For example, the Alabama State Department of Education leverages data matching to determine schools that qualify for the USDA National School Lunch Program. In order to fulfill this goal, the state performed a cross-walking analysis on various data sets to determine a 95% match for the grant program.
Inconsistency in data is one of the key challenges for data matching, especially when state barriers prevent the use of unique identifiers such as SSNs to identify matches. This necessitates the use of other factors such as names, last names, DOBs to identify matches and ensure that datasets speak the same language.
Using cleansing, deduplicating, matching & de-identification processes, DME helps you achieve a 96% match of your data.
In an era of big data, your data will continue to grow and data sources will continue to be disparate. DME not only helps you manage your data but also be instrumental in providing you with analytics and insights of your constituents.
Government data comes from a myriad of sources, which opens it to significant standardization issues.
Data standardization is the process of transforming data into a consistent and usable format. Add human error to the data being entered and you have issues like inconsistent punctuation, capitalization, special characters, invalid entries, obscure or multiple variations of acronyms, etc.
For government organizations, data standardization is a crucial step to identity resolution as it ensures that all other irrelevant or obsolete standards are rectified.
We heavily invest in research and development improving the speed, accuracy, and usability of our products. In 15 independent studies, DataMatch Enterprise provided a 96% accuracy for data matching of millions of records. We are rated the fastest and the most accurate, above systems like HP and IBM.
> It’s easy to learn. You don’t need additional experts to use DataMatch Enterprise.
> We offer exceptional customer support at all times
> Get the most out of your data in a short time span. It takes minutes to sort your data in our DME platform.