Master Data Management for Government Customers’ Data

Andrew Rosa By: Andrew Rosa

Jitendrudu Lacaraju By: Jitendrudu Lacaraju

“To protect and manage the fish, forest, and wildlife resources of the state; to facilitate and provide opportunity for all citizens to use, enjoy, and learn about these resources.”
--Missouri Department of Conservation

“We protect and develop healthy, sustainable forest resources for Virginians.”
--Virginia Department of Forestry

“Connecting people, products and places safely and efficiently with customer focus, accountability and environmental sensitivity to enhance the economy and vitality of North Carolina”
--North Carolina Department of Transportation

The above mission statements from public sector agencies have one thing in common – CUSTOMERS. Public agencies exist to provide public services, which presents many challenges for them. One challenge is not realizing how to manage a master customers repository, maintaining quality of the data or maintaining an effective and efficient master database. It is vital for public agencies to strive towards keeping highquality dependable data to operate as efficiently as possible. In the context of a Public Sector Government Agency, the Customer Master Data is the critical business information entity for supporting the Transactional Operations, operational reporting and fact-based decision making. All of this requires processes, technology and people to manage it.

This white paper will present why and how a Master Data Management solution should be relevant to a Public Sector agency.

A public agency may be at the Local, Regional, or National Government level, but they all are mandated to provide public services for their citizens. The services could be direct or in-direct services, like licensing, permits or information material subscriptions, zoning, parcels, etc. To establish a basic definition - anyone using these Services is a Customer. The customers could scale and get complicated according to the jurisdiction and mandate of the agency.

A familiar story A State agency is administering many program services which come into direct contact with the citizen Customers: License-Permits, Point of Sales, Facility use, delivery of out-reach and education material, to name a few. The customers are not limited to using a single service, but as users they avail many services from the agency. With a desired requirement to maintain a Customers Relationship Management system across the agency, IT realized that it could not simply switch to a CRM since there were many issues related to its Customers’ data, including:

  • The systems which allow delivery of these services to the customers are mostly isolated, i.e., no data is shared between the systems leading to a wide-spread duplication.
  • Each transactional system maintained different data entry validations and rules of system engagement. This led to poor data quality.
  • The redundant data are never cleared in the systems, like fully un-subscribed Customer still existed in some databases. Accuracy of the data has always been a concern.
  • The agency could not produce an analysis that would lead to better program delivery, such as reporting high-utilizers of services, revenue analysis from a customer, etc. 


This story is not isolated and there are many Public Sector agencies that are faced with these challenges. Fragmentation, Inconsistent and distributed Customers Data hides insights through KPIs, inefficiencies in dispersing services to the customers, negative impacts on inter or intra -program coordination, spending, and public reach-out. Also, IT-level risks of redundant storage, archiving, application development, etc. There are many solutions to overcome these challenges, such as better data governance practice, custom unified master database, and custom applications

The Customers are an essential business entity, thus managing their data is important. An efficient and effective solution to do this is to set in place a Master Data Management (MDM) framework.

An MDM would not be limited to Customers and could also include products, assets, locations, etc. The following are benefits and reasons for an agency to stand up an MDM solution and maintain it:

  • Reduce the cost of maintenance and sharing of data between divisions
  • Higher demand for better services & expectations from citizens
  • Need for better Customers’ data management
  • Policy compliance and Risk management
  • Customers Data insights


The MDM solution focuses on the two main Aspects-- Technical and Management. Neither of these aspects is dispensable and are required to be implemented in some form. Technical aspects are the Master Data System’s Architecture – Database Servers, Management console applications and Data Structure – Database, Schema.

Management aspects deal with the Data Governance and Data Processes (workflows). The key to success of a MDM is Data Quality management. The solution presented here is developed on Microsoft SQL Server Master Data Services. Here are the high-level MDM system components that are developed into coordinated workflows and processes:

  1. SQL Server Data Quality Services (DQS): Used for data quality improvement by creating a knowledge base data driven cleaning workflow, enrichment and cleaning. The DQS database also contains the Source Metadata information under the Metadata Model Management process.
  2. SQL Server Integration Services (SSIS): Data Extract, Transformation, Loading from source systems into Data Quality Services Databases as well as de-duplication.
  3. SQL Server Master Data Services (MDS): This is the core component of the solution where the final Customers Master data is loaded from DQS.
  4. Addresses enrichment services: The addresses are enriched or corrected using a geocoding web service.

In addition to developing the above Technical aspect of the solution, we also provide a Data Governance and Data Stewardship plan and workflow to help the client to manage, engage and carry forward the MDM solution and manage its lifecycle.

Timmons Group has a proven MDM methodology and solution implementation experience which can be tailored for a public agency’s MDM needs. This  MDM solution architecture framework for customer data has been evaluated on different platforms that include open source technology as well. Our engagement on MDM implementation consists of four steps: Business Assessment, Discovery, Solution Design, and Construction.
Timmons Group will help you with a with a simple, intuitive, and cost-effective solution.


Jitendrudu Lacaraju
Jitendrudu is the Lead Software Architect at Timmons Group with over 16 years of experience in Software Development and Data Management. He is leading the efforts on providing Data Driven solutions that enhance the business and experience of our clients in their respective Industry areas. He is proficient in MS SQL Server, .NET, R, SQL, Python, GIS, Tableau and Power BI. He has a Master's degree in Remote Sensing and GIS from ISRO, Department of Space, India.

Andrew Rosa
Andrew is an experienced data scientist and quantitative analyst at Timmons Group. His interests are in creating data driven strategies for businesses and organizations. He has a history of working with start-ups and corporations in technology, manufacturing, and consumer electronics industries. Andrew is proficient in statistical modeling, time-series analysis, data manipulation, web scraping, and database management. Skilled in R, SQL, python, SAS, Tableau, PowerBI and SPSS, Andrew is a strong technology professional who recently completed a Master of Science with a focus in Data Science through Utica College. Andrew’s skills and experience include regression modeling, decision tree modeling, cluster analysis, A/B testing, hypothesis testing, time-series analysis, and ARIMA Modeling.