Data Governance category

Building a Winning Data Quality Strategy: Step by Step

5 min read - What Is a Data quality strategy? A data quality strategy details the processes, tools, and techniques employed to ensure your company’s data is accurate, consistent, complete, and up-to-date. A well-defined data quality strategy enables better decision-making based on reliable information and reduces risks associated with poor-quality data. In order to develop a successful data quality strategy, you will need to understand how high-quality information supports decision-making processes and drives growth across your organization, and what factors can impact or reduce…

DataOps Framework: 4 Key Components and How to Implement Them

6 min read - What is a DataOps framework? The DataOps framework is a set of practices, processes and technologies that enables organizations to improve the speed, accuracy and reliability of their data management and analytics operations. DataOps is often compared to DevOps and agile methodologies used in software development, as it aims to break down silos, encourage collaboration, and streamline workflows for data teams. The core philosophy of DataOps is to treat data as a valuable asset that must be managed and processed…

Data Accuracy vs Data Integrity: Similarities and Differences

4 min read - What is data accuracy? Data accuracy refers to the degree to which data is correct, precise, and free from errors. In other words, it measures the closeness of a piece of data to its true value. Data accuracy is a crucial aspect of data quality, as inaccurate data can lead to incorrect decision-making, poor customer service, and operational inefficiencies. The importance of data accuracy cannot be overstated. Accurate data ensures that these decisions and strategies are based on a solid…

DataOps Architecture: 5 Key Components and How to Get Started

4 min read - What Is DataOps architecture? DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows. A DataOps architecture is the structural foundation that supports the implementation of DataOps principles within an organization. It encompasses the systems, tools, and processes that enable businesses to manage their data more efficiently and effectively. In this article: Legacy data…

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