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Astera

A Guide to Automated Data Governance: Importance & Benefits

Automated data governance is a relatively new concept that is fundamentally altering data governance practices. Traditionally, organizations have relied on manual processes to ensure effective data governance. This approach has given governance a reputation as a restrictive discipline. But, as organizations increasingly adopt automation in their governance processes, this perception is changing.

An Introduction to Active Data Governance

The way that companies govern data has evolved over the years. Previously, data governance processes focused on rigid procedures and strict controls over data assets. But now, with the data-driven culture, modern enterprises are adopting an agile approach toward data governance that primarily centers around data accessibility and empowering business users to take responsibility for governing and managing data.

Data Lineage: A Complete Guide

Data lineage is an important concept in data governance. It outlines the path data takes from its source to its destination. Understanding data lineage helps increase transparency and decision-making for organizations reliant on data. This complete guide examines data lineage and its significance for teams. It also covers the difference between data lineage and other important data governance terms and common data lineage techniques.

What is API Monitoring? Best Practices to Track API Performance and Metrics

API downtime can cost businesses an average of $140,000 to $540,000 per hour. Maintaining reliable and high-performing APIs has become critical for any digital business’s success, with much at stake. This scenario is where API monitoring steps in. An important part of API management, monitoring API metrics allows organizations to detect issues rapidly and optimize their API performance.

Astera's Guide to Marketing Data Integration and Governance

Customer data provides a treasure trove of insights into their behavior and preferences. Marketers must leverage this information to drive strategic decisions and optimize marketing campaigns. Analysts must consolidate fragmented data across various systems to use customer data effectively. With proper data integration and governance, marketers can effectively transform their data assets into insights that inform their decision-making.

What is Streaming ETL?

Streaming ETL is a modern approach to extracting, transforming, and loading (ETL) that processes and moves data from source to destination in real-time. It relies on real-time data pipelines that process events as they occur. Events refer to various individual pieces of information within the data stream. Depending on the source and purpose of the data, an event could be a single user visit to a website, a new post on a social media platform, or a data point from a temperature sensor.

Data Catalog vs. Data Dictionary vs. Business Glossary: Key Differences

A data catalog is a collection of metadata that acts as a comprehensive inventory of an organization’s data assets. It provides an overview of all the data available, regardless of its location or technical details. This collection helps analysts and other data users find the needed data.

What is a database?

A database is a storage system that stores data in an organized manner for easy access and management. In just the last two years, 90% of the world’s data has been created, and the volume of global data doubles every two years. All this data is stored in databases. So, whether you’re checking the weather on your phone, making an online purchase, or even reading this blog, you’re accessing data stored in a database, highlighting their importance in modern data management.

5 Key Data Governance Principles for Effective Data Management

Digitalization has led to more data collection, integral to many industries from healthcare diagnoses to financial transactions. For instance, hospitals use data governance practices to break siloed data and decrease the risk of misdiagnosis or treatment delays. Therefore, managing data to protect its integrity and security leads to high-quality, reliable data that empowers organizations to make informed decisions.

Data Science vs. Data Analytics: Key Differences

Organizations increasingly use data to gain a competitive edge. Two key disciplines have emerged at the forefront of this approach: data science and data analytics. While both fields help you extract insights from data, data analytics focuses more on analyzing historical data to guide decisions in the present. In contrast, data science enables you to create data-driven algorithms to forecast future outcomes. These disciplines differ significantly in their methodologies, tools, and outcomes.