360workx - Data Governance

The difference between data governance policies and standards is simple. Standards are templates, and policies are upper and bottom limits. Data governance standards are ready-to-use templates that can be applied. They meet the best practices in data management and can be activated out of the box in vetted tools. Of course, we may face challenges when applying them to particular datasets. Some tables are too big, others change too often, yet another table is not worth the effort. That is when the data governance policies come to the rescue. A policy should provide the bottom and upper boundaries. 👇 The bottom boundaries are the must-have requirements that all data assets should meet. ☝️ The upper boundaries are the sanity limits to avoid drowning in useless activities. Are there any other activities for which we should have the boundaries set? I could fit only seven areas to make the picture readable. Check the comments for some interesting links. #dataquality #dataengineering #datagovernance

Data Governance is the foundation of a strong data management strategy, especially in large data driven organizations. A robust framework standardizes data operations, boosts accuracy, increases security, and ensures compliance with regulations.

Building a Federated Data Office? Beware of the pitfalls ❗ A Federated Data Office is at the heart of Data Governance Programs for most enterprises. However, to truly drive steady value growth, it must be continuously "tuned" to ensure all teams stay aligned with shared goals. Without this alignment, you risk: ❌ Communication challenges ❌ Inconsistencies in the operating model ❌ Misaligned processes, roles, or terminology ❌ Duplicated efforts and wasted resources ❌ Higher risks of privacy issues and non-compliance This is where Data Governance Councils and Stewardship Communities come in. These teams play a critical role in ensuring consistency, collaboration, and clarity across your organization. #DataGovernance #DataOffice #BusinesValue #DataIntelligenceTip Collibra

How to improve data governance: 14 strategic steps Enhancing data governance in your organization to track and curate access to data involves multiple steps, including establishing policies, building a governance framework, and leveraging the right tools. Here are some steps to consider: *Develop a data governance strategy and policy. *Establish a data governance framework. *Understand and map your data landscape. *Establish a data governance committee. *Appoint a data governance lead or team. *Define data governance roles and responsibilities. *Leverage data governance tools. *Implement data classification and cataloging. *Create and enforce data access policies. *Implement data quality measures. *Implement data lineage and metadata management. *Conduct regular audits and reviews. *Train and educate employees. *Monitor and measure success.

As organizations embrace the potential of AI and data-driven innovation, the importance of robust Data Governance cannot be overstated. Governance provides the structure, processes, and culture necessary to manage data as a strategic asset – across Businesses, ensuring it is accurate, secure, and accessible. Without it, data initiatives risk becoming fragmented, slow, and expensive, impeding the organization's ability to scale its use of data and AI effectively. In this second article of our 3 article series “Building the Foundation for a Data-Driven Future”, we’ll dive deeper into the components of a Data Governance framework, explore what it takes to set it up, and explain why it’s essential for enabling transformation toward a data-driven enterprise. I particularly want to thank my colleague Bernd Gill, our CTO & Innovation Director DACH, for his valuable contribution to this series of articles. #rackspace #FAIR #AI #transformation #datadriven #GenAI #data #DataGovernance

⭐️ 2024 was a year of revelation for Data Governance : 💎 People are starting to see real benefits from it. 🛠 Initiatives and toolings are getting sexier thanks to AI agents. 👥 More companies are recruiting data governance experts or looking for external resources. Here are 5 trends to watch in 2025 : 1️⃣ LLM for Data Governance : no surprise, but many vendors are including these functionalities now 2️⃣ More automation for Data Governance : say bye to manual data tasks leading to many errors 3️⃣ Increased focus on data transformation : business need to restructure, reformat, clean 4️⃣ Data modeling is core : data models are no longer optional to guarantee consistency and common language 5️⃣ Data quality first for machine learning : AI performance is increased by good data -- Want my full Top 10 trends ? I'll send it to you this Thursday on my newsletter : - Subscribe here : https://lnkd.in/eV2bfB83 #datagovernance #data