"Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods."
Y&L has vast experience in implementing enterprise data governance strategies across various business domains. Through these experiences, we’ve been able to identify what works, what doesn’t, and how to mitigate risk. Data Governance serves as the foundation for leveraging powerful analytics and introducing advanced technologies, such as artificial intelligence and machine learning to ultimately optimize competitive advantage.
Reduce Operational Cost – Eliminate duplicate processes and reduce administrative costs by defining clear roles and responsibilities for data management.
Data Process Standardization – Streamline data so it’s easily accessible to everyone who needs it to further BI objectives and meet organizational goals.
Improve Decision-Making – Data is used to make decisions. Wrong decisions happen with incomplete or erroneous data; not to mention exposing the organization to significant operational and legal challenges.
Controlled Access – Internal and external regulations regarding “Need to Know” access to data have made data governance programs vital for maintaining access safeguards. Employees should only have access to the data they need in order perform their duties.
Peace-of-Mind – Making the protection of personal information a priority can have a positive customer satisfaction impact; building trust between the customer and the organization. Audit results in reliable, up-to-date reports, a reduction in the risk of unlawful access, and the secure destruction of out-of-date data aid in building customer.
Trusted Insight – Well-governed data is more accessible and reliable, making it easier for queries and integration with other data sets. Predictive Analytics based upon sound data can point a trustworthy path forward.
Internal Data Governance Benefits
Roles / accountabilities defined
Improved decision making, remediation, and data lineage
Policies provide governance bodies (data management and data stewards) with
a foundation and enforcement authorization. Policies should provide direction
and guidelines for specific types of data to be managed.
Key Policy Areas Include:
Master Data Management
Meta Data Management
Data Quality Policies
A data quality policy provides a base definition of data quality within your company and establishes responsibilities for different data quality management processes.
Key Policy Areas Include:
Data Quality Program Assessment
Establishment of a Data Quality Policy/Charter
Targeted Data Quality Profiling
Establishment of base-line data quality metrics
Establishment of targeted data quality programs and processes
Establishment of authoritative data sources, edits, definitions
Establishment of standard processes for extraction, completeness and accuracy checks
Establishment of correction processes, reports, alerts and links to data stewards
Establishment of quantitative data quality value metrics
Establishment of management tracking reports
DG Performance Monitoring Dashboards in PowerBI
Data Governance programs can be hard to manage and measure without the proper tools in place, and as a stakeholder, it's vital to be able to track your organization's investments. Our approach includes a Data Governance dashboard to provide measuring, promote systematic monitoring, drive continuous improvement, and to serve as the presentation point for your Data Governance meetings.
Data Governance Metrics
Metrics and the measurements they create are essential to the success of every data governance program and every data stewardship effort.
Number of Data Incidents Reduced
Number of Redundant Processes Eliminated
Reduced Operational Cost/Reduction in Data Rectification Costs
Improved Report Quality
Improved Data Quality Scores:
Completeness – It’s important that critical data (such as names, phone numbers, email addresses, etc.) be completed first since it doesn’t impact non-critical data significantly.
Timeliness – How much of an impact does date and time have on the data?
Validity – Does the data conform to the respective standards set for it?
Accuracy – How well does the data reflect the person or thing identified?
Consistency – How well does your data align with a standardized pattern? For example, if you capture birth dates, they share common consistency in the U.S., MM/DD/YYYY. However, if you also do business in Europe, it is recorded as DD/MM/YYYY.
Different Data Structures for Different Users
Identifying and organizing your company’s information from both a business and a technical perspective is a critical step for providing and presenting information to a wide-range of
Business Meta data
Business rules, Definitions, Terminology, Glossaries, Algorithms and Lineage using business language Audience: Business users
Technical Meta data
Defines Source and Target systems, their Table and Fields structures and attributes, Derivations and Dependencies Audience: Specific Tool Users – BI, ETL, Profiling, Modeling
Operational Meta data
Information about application runs: their frequency, record counts, component by component analysis and other statistics Audience: Operations, Management
MDM, Data Quality, and Data Governance Implementation
Due to rapid growth, our client’s product and customer data was not easily shared between systems, resulting in duplicate and inconsistent naming conventions. Y&L analyzed data sources across various systems and created a single data model. A source-to-target mapping document showed the migration from source systems to master tables.
Embedded within the integrations were a series of Informatica Data Quality jobs designed to shape and create unique, “golden records”. To supplement technical components, detailed recommendations outlined data governance opportunities within applications and processes.
Tools Used: Oracle, Informatica Data Quality, Salesforce, Proprietary Applications