Posts by Angsuman Dutta
Achieving Superior Data Quality Management Using Databricks Validation
What is Databricks? Databricks is a cloud-based data storage, management, and collaboration platform. Its cloud-based nature makes it remarkably fast and easily scalable to meet a company’s growing data needs. It runs on top of existing cloud platforms, including Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. Unlike other data storage solutions, Databricks combines…
Read MoreAlation + DataBuck: Advanced Data Quality With Integrated Data Governance
Have you heard about the new industry initiative to improve data quality? It’s called the Alation Data Quality Initiative, and it aims to bring together the best features of the industry’s biggest players. The goal is to let customers integrate the best data quality tools from multiple companies in a straightforward and easy-to-use fashion –…
Read MoreHow to Build a Framework for Data Governance?
Good data is the lifeblood of your organization. As data volume continues to rise, your ability to manage it could increasingly spell the difference between success and failure. Data governance is a framework that helps businesses ensure that they’re managing their data efficiently and effectively. Data governance encompasses all the people, processes, and technology that…
Read MoreAnomaly Detection: A Key to Better Data Quality
Why Data Quality Matters and How Anomaly Detection Helps? Maintaining data quality is important to any organization. One effective way to improve the quality of your firm’s data is to employ anomaly detection. This approach identifies data anomalies—outliers that are likely to be irrelevant, inaccurate, or problematic for analysis. Understanding how anomaly detection works can…
Read More12 Top Data Governance Tools & Software for 2025
Is your organization looking to employ new data governance tools in the coming year? Data governance is necessary to ensure the accurate and reliable information your company needs to make the best possible business decisions. When you want robust data governance, here are 10 top-rated software solutions to consider, all of which can help your…
Read More10 Best Data Catalog Tools for Enterprises to Consider in 2025
Many tools help build and manage data catalogs. Here, we outline key features, capabilities, and components of 10 well-known data catalog tools. In today’s data-driven world, managing data sprawl across various databases and repositories poses significant challenges for organizations. Without effective data management, BI and data analytics initiatives struggle to yield insights. Data catalogs offer…
Read More3 Ways to Solve Last Mile Data Integrity Challenges With Advanced Observability
Data quality issues have been a long-standing challenge for data-driven organizations. Even with significant investments, the trustworthiness of data in most organizations is questionable at best. Gartner reports that companies lose an average of $14 million per year due to poor data quality. Data observability has been all the rage in data management circles for a…
Read MoreProven Strategies for Achieving Cloud Data Quality: A Modern Enterprise Guide
Previously published in Entrepreneur.com You’ve finally moved to the Cloud. Congratulations! But now that your data is in the Cloud, can you trust it? With more and more applications moving to the cloud, the quality of data is becoming a growing concern. Erroneous data can cause all sorts of problems for businesses, including decreased efficiency,…
Read More5 Proven Strategies to Ensure Data Quality and Trust Across Pipelines, Warehouses, and Lakes
If data is the new oil, then high-quality data is the new black gold. Just like with actual oil, if you don’t have good data quality, you’re not going to get very far. In fact, you might not even make it out of the starting gate. So, what can you do to make sure your…
Read MoreEstablish Autonomous Data Observability and Trustability for AWS Glue Pipeline in 60 Seconds
The Challenge of Ensuring Data Quality in AWS Glue Pipelines Data operations and engineering teams spend 30-40% of their time firefighting data issues raised by business stakeholders. A large percentage of these data errors can be attributed to the errors present in the source system or errors that occurred or could have been detected in…
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