Analytics

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.

Simple, Sustainable, and Secure Storage for Mid-sized Enterprises

The mid-sized enterprise is the fastest-growing market opportunity for data storage. But not just any storage system will do. These days, mid-sized enterprises must handle the complexities of unremitting data growth and distributed infrastructure, meet sustainability goals, manage the diverse storage needs of mission-critical applications, and respond to user requirements. Oh, and they need uninterrupted access to their data no matter what.

Where Does Data Governance Fit Into Hybrid Cloud?

At a time when artificial intelligence (AI) and tools like generative AI (GenAI) and large language models (LLMs) have exploded in popularity, getting the most out of organizational data is critical to driving business value and carving out a competitive market advantage. To reach that goal, more businesses are turning toward hybrid cloud infrastructure – with data on-premises, in the cloud, or both – as a means to tap into valuable data.

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.

hDs Chapter 5 - Mastering the Data Journey: Quality, Governance, and Lineage for Informed Decision-Making

In the digital age, data is the lifeblood of organizations, driving strategies, innovation, and decisions. However, harnessing its power requires more than just collecting the data. It demands meticulous management of data quality, governance, and lineage. These pillars form the backbone of informed decision-making, enabling organizations to transform raw data into actionable insights. According to Gartner, poor data quality costs organizations an average of $12.9 million every year.

AWS and Confluent: Meeting the Requirements of Real-Time Operations

As government agencies work to improve both customer experience and operational efficiency, two tools have become critical: cloud services and data. Confluent and Amazon Web Services (AWS) have collaborated to make the move to and management of cloud easier while also enabling data streaming for real-time insights and action. We’ll be at the AWS Public Sector Summit in Washington, DC on June 26-27 to talk about and demo how our solutions work together.

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.

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.

Snowflake: Automate tuning for data cloud speed and scale

40% of companies surveyed will increase their AI investment because of advances in GenAI (McKinsey). And 80% plan to maintain or increase their investment in data quality/observability (dbt). With this in mind, Unravel is hosting a live event to help you leverage data observability to achieve speed and scale with Snowflake. Join Unravel Data for this event about automating tuning with AI-powered data performance management for Snowflake with Eric Chu, Unravel Data VP of Product, and Clinton Ford, Unravel Data VP of Product Marketing.