“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.
Eliminate duplicate processes and reduce administrative costs by defining clear roles and responsibilities for data management.
Create a digital roadmap detailing the current environment, capability gaps, business strategy, and more.
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.
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.
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results in reliable, up-to-date reports, a reduction in the risk of unlawful access, and the secure destruction of out-of-date data aid in building customer.
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.
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.
A data quality policy provides a base definition of data quality within your company and establishes responsibilities for different data quality management processes.
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.
Metrics and the measurements they create are essential to the success of every data governance program and every data stewardship effort.
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.
How much of an impact does date and time have on the data?
Does the data conform to the respective standards set for it?
How well does the data reflect the person or thing identified?
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.
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 users.
Business rules, Definitions, Terminology, Glossaries, Algorithms and Lineage using business language Audience: Business users
Defines Source and Target systems, their Table and Fields structures and attributes, Derivations and Dependencies Audience: Specific Tool Users – BI, ETL, Profiling, Modeling
Information about application runs: their frequency, record counts, component by component analysis and other statistics Audience: Operations, Management