Data Quality and data integrity are both important aspects of data analytics. With the rapid development of data analytics, data can be considered one of the most important assets a business owns. As a result, many organizations collect massive amounts of data for research and marketing purposes. However, the value of this data depends on […]
RWDG Webinar: How to Govern Glossaries, Dictionaries, and Data Catalogs
Download the slides here>> This webinar is sponsored by: About the Webinar Business glossaries, data dictionaries, and data catalogs are valuable tools that enable organizations to drive active value from the definition, production, and usage of their data. These tools house metadata including the inventory, business and technical context, stewardship, rules, and understanding of […]
Data Lineage Tools: An Overview
Data lineage is the process of tracking and tracing data from its origin to its destination. It provides a detailed understanding of how data moves through an organization’s systems, applications, and processes. Organizations can use this information to maintain Data Quality, ensure regulatory compliance, and improve decision-making. Data lineage also helps organizations identify potential bottlenecks and inefficiencies […]
Maximize Data Impact with an Effective Data Lineage Strategy
High-quality data can produce powerful insights that enhance decision-making. For data-driven organizations, this leads to successful marketing, improved operational efficiency, and easier management of compliance issues. However, unlocking the full potential of high-quality data requires effective Data Management practices. That’s why a robust data lineage strategy is essential – it helps increase information utility and maximize data […]
Data Ethics: Safeguarding Privacy and Ensuring Responsible Data Practices
The importance of data ethics cannot be emphasized enough in the digital era. With so much data being created, processed, and stored, people and businesses must prioritize privacy and maintain appropriate data practices. Data collection, storage, and use raise ethical concerns that must be addressed to preserve individuals’ rights and sustain public confidence. Below, we […]
Data Governance Best Practices for Your Business
Data Governance is a strategic approach to managing, protecting, and using a firm’s data assets. It encompasses processes, policies, strategies, and tools that govern how a firm collects, stores, accesses, and leverages data. With Data Governance best practices, a firm can ensure data integrity, mitigate associated risks and challenges, and improve Data Quality. This can […]
AI Regulation Must Focus on Building Public Trust
Mere months after generative AI captured the world’s attention, leaders like OpenAI’s Sam Altman and Google’s Sundar Pichai testified before Congress with a simple message: Prioritize AI regulation before the technology gets out of hand. The message surprised many – especially coming from the leaders who unveiled the revolutionary tools themselves – but it has become […]
Navigating the Risks of LLM AI Tools for Data Governance
The sudden advent of large language model (LLM) AI tools, such as ChatGPT, Duet AI for Google Cloud, and Microsoft 365 Copilot, is opening new frontiers in AI-generated content and solutions. But the widespread harnessing of these tools will also soon create an epic flood of content based on unstructured data – representing an unprecedented […]
Data Mesh: A Pit Stop on the Road to a Data-Centric Culture
The noble effort to build a “data-centric” culture is really a journey, not a destination. With that perspective, we can understand that no matter how good a given environment seems to be –especially compared to whatever existed before – there’s always room for enhancement. As more technologies, strategies, and disciplines emerge, the ongoing evolution ensures constant improvement. […]
Data Governance: The Oft-Overlooked Pillar of Strong Data Quality
What are the most common causes of Data Quality issues? The conventional answer to that question includes problems like inaccurate data, duplicate data, or data containing missing values. These are the sorts of issues that organizations tend to focus on when they want to improve the quality of their data so that they can leverage […]