“数据治理 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, 当, 在什么情况下, 使用什么方法.”
Y&L has vast experience in implementing enterprise data governance strategies across various business domains. 通过这些经历, 我们已经找到了有效的方法, 什么不, 以及如何降低风险. 数据治理 serves as the foundation for leveraging powerful analytics and introducing advanced technologies, such as artificial intelligence and machine learning to ultimately optimize competitive advantage.
减少运营成本 – Eliminate duplicate processes and reduce 管理istrative costs by defining clear roles and responsibilities for data management.
数据流程标准化 – Streamline data so it’s easily accessible to everyone who needs it to further BI objectives and meet organizational goals.
改善决策 —数据用于决策. Wrong decisions happen with incomplete or erroneous data; not to mention exposing the organization to significant operational and legal challenges.
控制访问 – 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.
内心的宁静 – Making the protection of personal information a priority can have a positive customer satisfaction impact; building trust between the customer and the organization. 审核结果可靠, 最新的报告, 降低非法访问的风险, and the secure destruction of out-of-date data aid in building customer.
值得信赖的洞察力 – Well-governed data is more accessible and reliable, making it easier for queries and integration with other data sets. Predictive 分析 based upon sound data can point a trustworthy path forward.
- 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.
A data quality policy provides a base definition of data quality within your company and establishes responsibilities for different data quality management processes.
- 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
数据治理 programs can be hard to manage and measure without the proper tools in place, 作为利益相关者, it’s vital to be able to track your organization’s investments. Our approach includes a 数据治理 dashboard to provide measuring, 促进系统的监控, 推动持续改进, and to serve as the presentation point for your 数据治理 meetings.
Metrics and the measurements they create are essential to the success of every data governance program and every data stewardship effort.
- Reduced Operational Cost/Reduction in Data Rectification Costs
- 完整性 – 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.
- 及时性 – How much of an impact does date and time have on the data?
- 有效性 – Does the data conform to the respective standards set for it?
- 精度 – How well does the data reflect the person or thing identified?
- 一致性 – 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
|定义源系统和目标系统, 表和字段的结构和属性, 派生和依赖关系
Audience: Specific Tool Users – BI, ETL, Profiling, Modeling
|Information about application runs: their frequency, 记录计数, component by component analysis and other statistics
“由于快速增长, 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. “嵌入在集成中的是……”