Case Study: Data and Analytics
SITUATION & BUSINESS CHALLENGE
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. Formed three years prior, the division lacked consistency in data storage, distribution, and usage practices and was utilizing four disparate data catalogues for these efforts across the organization. Although a major consulting firm was working with the company on enterprise-wide data governance, their framework provided light guidance at best and did little to help the division mature data governance capabilities.
The company operated within a federated enterprise governance framework. In a federated framework, enterprise governance broadly covers divisional governance in much the same way that 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.
SOLUTION
An AIM data governance expert with a history of leading successful data governance initiatives across a wide variety of organizations 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, the 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 framework is a blend of data governance guidelines from Capability Maturity Model Integration (CMMI) and Data Management Association (DAMA) best practices, experience from successful past implementations, and 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 framework consisted of the following major 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.
AIM presented its 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.
Additionally, AIM separately detailed a set of data governance policies and guidelines covering general policies, standards and forms, metadata and data dictionaries, collaboration among functional teams, and 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.
In addition, AIM created a SharePoint site to highlight the new data governance policies and processes. The SharePoint site was well received and is in wide use throughout the division for sharing policies internally.
An area of immediate improvement recommended by AIM was to standardize on the Azure Data Catalog service as the division’s central data dictionary and catalogue tool. AIM worked with engineers and engineering management to implement this tool and start consolidating data catalogues across the division as a strong first step of the roadmap implementation.
AIM also leveraged their resource network to bring in an expert on SOX (Sarbanes-Oxley) to ensure financial and accounting compliance across the division.
RESULTS
The data governance framework developed by AIM’s Data & Analytics practice, developed for the division while conforming to enterprise-wide data governance policies, 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.
AIM united the division’s business and engineering sides in the project, often a daunting endeavor in itself 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 of maturing any data governance initiative.
