The concept of a single version of the truth has been proselytized by data warehouse experts for quite some time. The idea was to retain a single record within the data warehouse that represented the current version of a business entity. But as data warehouses evolved and more data sources offered their own versions of customer data, the ability to produce a single version of the truth became a challenge.

Master data management (MDM) has morphed the concept of a single version of the truth into something altogether different. Operational data integration via MDM hub technologies has introduced the possibility of cleansing and integrating data “where it lays.” This offers several advantages, including the ability to bring together many sources of information and produce a single authoritative record or generate the best record (or best version of the truth) based on an application’s needs.

With MDM, the customer information returned by the hub may depend on the department making the request. In public sector terms, the Department of Corrections may want to access the last known alias used by John Q. Public, while another department will want to recognize John Q. as the head of the household while he was incarcerated to determine changes to qualified household benefits. The data search, matching, rules assignment, cleansing and provisioning functionality is complex and highly performant.

Many companies require this type of functionality but can’t afford new technologies to automate it. Recently, I worked with a government client on a project that needed to bring together nearly a dozen different sources in order to maintain a history of events relating to an individual citizen. This history of events ranged from wage earnings, unemployment benefits, additional sources of unearned income, involvement with the Department of Corrections and several other touchpoints with various governmental agencies. The goal of the project was to provide the agency’s field workers with a single portal where they could see a complete view of information necessary to determine eligibility for public assistance. In some instances, the worker was still required to access an external system, but a portal was able to notify the worker whether or not that access was required.

This was an analytical application, but it required master data. Keeping the bigger picture of MDM in mind, the government client wanted to leverage the data in its mature data warehouse. Moreover, the client’s budgetary situation meant that a real-time, operational MDM hub was out of the question. The challenge was integrating numerous data sources in a manner that provided the flexibility necessary to be an enterprise solution for master citizen data that supported analytics. This project was just what the client needed to supplement the existing address master data.

To facilitate adoption by additional projects or agencies, the ability to integrate new data into the incumbent model had to be accelerated and streamlined. Because a smaller-scale, proof of concept extract, transform and load (ETL) process for address master data was working well, the team determined that it could extend the functionality to leverage additional requirements. If an agency or project wanted to integrate its data to leverage the data model, they would need to complete a data mapping exercise in which they defined how and where the data was staged in the data warehouse. The actual time it took to integrate a new data source into the client master data was reduced from days to a mere couple of hours.

A matching process was added based on a predetermined set of criteria. Today, the results are retained in the data model to provide deeper analytics to agencies or projects that have a vested need. However, the data model supports an additional mechanism to simplify the ability to identify the same person across the data sources. This process completes the transitive matches across multiple sources and assigns a single sequential ID to all parties that meet the same basic set of attributes.

As a result, field workers across agencies can obtain all the information necessary to bring together the events related to a given constituent or household. The solution may not have the panache that comes with owning the Mercedes or BMWs of the customer data integration industry, but it provides the reliability and efficiencies of a Volkswagen. Problem solved!

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