This case study deals with a global software company’s need for consistency in its data storage and usage practices. AIM’s Data & Analytics practice successfully delivered a custom-fit, federated data governance solution.
Business Challenge: Lacking Consistency in Data Storage and Usage
A major division of a global software and services company was maturing and growing in the marketplace and realized the need for a strong data governance framework.
The division lacked consistency in data storage, distribution, and usage practices and was utilizing four disparate data catalogs for these efforts across the organization.
The company operated within a federated enterprise governance framework. In a federated framework, enterprise governance broadly covers divisional governance, similar to the way the United States federal government and its laws apply broadly to each of its 50 states.
It’s not unusual for a division to be left to its own patterns and processes regarding individual governance needs while also being loosely connected to the enterprise governance framework.
As organizational leadership was reviewing the need to improve its data practices, the person leading data governance efforts in the division moved on to another role, leaving a pressing gap for management to address.
Both the business and engineering sides of the business wanted to take over the initiative, and a moderating influence was needed to bring both sides together.
Division leaders called on AIM Consulting’s Data & Analytics practice to create and lead a new data governance initiative.
Developing the Data Governance Framework
One of our data governance experts with a history of leading successful data governance initiatives across a wide variety of organizations stepped in to help.
They worked in conjunction with division executives and five internal teams to tailor a data governance framework and roadmap for the division.
From the analysis of dozens of interviews with division stakeholders and workers, our expert provided a current state assessment and detailed gap analysis and developed a roadmap outlining major priorities and direction for data governance across the organization.
The comprehensive data governance framework is a blend of:
- Data governance guidelines from Capability Maturity Model Integration (CMMI) best practices
- Data governance guidelines from Data Management Association (DAMA) best practices
- Experience from successful past implementations
- Adherence to high-level elements from the enterprise-wide data governance work.
The framework was tailored to the division’s needs, which differed from the needs of the overall enterprise.
The federated data governance framework consisted of the following elements:
- Management of Data, Information & Processes: addressing the overall data and information landscape and data & information process management.
- Data & Information Responsibilities: detailing executive sponsorship and data & information policies, standards, security, accountabilities, training and performance measures.
- Common Information Language: including the establishment of a data & information definition forum and a master data management framework.
- Data & Information Quality: recommending the establishment of data & information quality forums, framework, change topics, and measurement control.
- Data & Information Usage: encompassing the creation of business reporting and analytics framework and forums, and common data & information tools.
Sharing the Data Governance Solution
We presented our findings and recommendations to executives and key stakeholders, detailing the current state assessment and gap analysis, outlining the five major framework elements, and accentuating the importance of following through on the roadmap to effect change in the soonest possible timeframe.
The roadmap outlined short-term (6-month) and long-term (2-year) initiatives.
We separately detailed a set of data governance policies and guidelines covering:
- General policies
- Standards and forms
- Metadata and data dictionaries
- Collaboration among functional teams
- Defined workflows and assignments of tasks.
Guidelines for measuring the effectiveness of the new data governance initiative and a recommended communication plan for stakeholders were also presented.
One area of immediate improvement we recommended was to standardize the Azure Data Catalog service as the division’s central data dictionary and catalog tool.
We worked with engineers and engineering management to implement this tool and start consolidating data catalogs across the division as a strong first step of the roadmap implementation.
We also leveraged our resource network to bring in an expert on SOX (Sarbanes-Oxley) to ensure financial and accounting compliance across the division.
Results of Implementing the Federated Data Governance Framework
The data governance framework introduced by our Data & Analytics practice was well received by division executives.
The custom framework was a first of its kind, developed specifically to fit within the federated data governance framework in a big data environment.
The initiative helped the division organize its priorities and understand the differences in how various internal groups have viewed data governance.
We united the division’s business and engineering sides in the project, often a daunting endeavor in large enterprise data governance initiatives.
This balance between the business and engineering sides in managing data governance over the long term is a critical success factor in maturing any data governance initiative.
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