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Government agency empowers a data transformation

Government agency empowers a data transformation

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How a government agency streamlined data governance, reduced costs and accelerated their digital transformation with erwin® Data Modeler by Quest®

Challenges

Prior to implementing a data modeling team, the agency’s application developers all had their own database structures and were primarily focused on application ease of use and not really concerned with the data. This resulted in several challenges:

  • Inconsistent data governance: Without standardized data definitions and centralized management, different departments may interpret data differently. Inconsistency creates gaps in data quality and impedes governance initiatives.
  • Manual and inefficient processes: Reliance on manual data modeling and management tasks increases the likelihood of errors and slows down the deployment of new data initiatives, impacting overall operational efficiency.
  • Interdepartmental misalignment: When data is managed in isolation, communication and collaboration between departments suffer. This misalignment can hinder decision-making and strategic planning.
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My advice to someone considering this solution is to go for it. It’s easy. The functionality is fantastic. It’s easy to pick up. It basically does everything you could want it to do.

IT Specialist at the large government agency

Solutions

The government agency has leveraged erwin Data Modeler to consolidate its fragmented data environments by standardizing data definitions and implementing a centralized governance framework. This comprehensive approach not only enhanced interdepartmental collaboration but also accelerated the deployment of new data initiatives.

Benefits

  • Enhanced data governance and compliance
  • Increased operational efficiency and cost reduction
  • Improved data integrity and consistency

The Story

This government agency is tasked with managing extensive and diverse data sets across the organization. The primary use case of the team was development and maintenance of the models that directly serve the application development teams. They are asked to determine what the requirements of the business are, build the data models and then progress them into actual physical database items. This comes with a number of challenges, but an IT specialist at the organization stated “erwin [Data Modeler] is by far the best tool I’ve ever used… It basically does everything you could want it to do.”

Overcoming silos across functional teams

This organization experienced a disconnect that many face: the priorities and goals of the data modeling team and the application development team differed. While they all were working towards a common goal, the developers tended to be less concerned with the data and were more concerned with ensuring that that everything “just worked” within their application as easily as possible. The IT specialist continued to mention that “having the tool and a team build around it, really helped us to make sure that we’re following best normalization processes, we’re not duplicating data and we have a standard naming scheme that everyone follows.”

The benefits of standardization

With erwin Data Modeler, the agency significantly improved their enforcement of standards and database design best practices. Before adopting erwin, each application development team maintained its own distinct database structures, leading to inconsistent naming and duplication of data. Now, with the naming standards and the centralized data modeling capabilities of erwin Data Modeler, they consistently reduce complexity and streamline their application development processes.

Gains in efficiency and productivity

The agency repeatedly mentioned that the accuracy and speed of transforming complex designs into well-aligned data sources absolutely made the cost of the tool worth it. The ease of setup and use allowed for immediate return on investment, and they have never looked back. Taking a look at the efficiency gains that this particular government agency is experiencing with erwin Data Modeler, there are significant cost reductions to be had hypothetically: given the agency mentioning that the tool was used across 10 desktops among five data modelers (and five designers using data models in read-only versions for enhanced collaboration and version control), we can assume the following:

Expense

Dollar Amount (U.S.)

Annual salary1 of a data modeler

$97,000

Time savings of 40% per data modeler per year

$38,800

Multiplied by 5 team members per year

$194,000

3 years of team savings

$582,000

1 Mean salary for a data modeler provided by Glassdoor.

Additionally, the IT specialist stated that erwin Data Modeler was extremely scalable. “Our environment has hundreds of tables.” Between the scalability in terms of volume and complexity of data, combined with the ability to connect to a growing list of data sources, erwin Data Modeler is truly a future-proof solution at the foundation of data and AI initiatives for organizations across the globe.

Conclusion

This government agency has overcome a slew of challenges and achieved a level of standardization that comes with a true, market-leading data modeling solution. With erwin Data Modeler, the agency has realized a number of benefits including:

Enhanced governance and compliance: By standardizing data definitions and implementing a centralized governance framework, the agency ensured robust audit trails and version control, which in turn bolstered its ability to meet strict regulatory requirements while maintaining high data integrity,

Increased operational efficiency and cost reduction: The automation of manual data processes and consolidation of fragmented data environments enabled the agency to accelerate deployment cycles, reduce errors and optimize resource allocation, leading to significant operational savings and justification of investment.

Improved data integrity and consistency: With a single source of truth established through erwin Data Modeler, disparate data sources were unified, eliminating inconsistencies and enhancing overall data quality across departments. Thereby facilitating better decision-making and collaboration.

Note: The content of this case study was developed based on the PeerSpot review of Mike Matthews, IT Specialist at a government agency with 10,001+ employees.