Posts by Angsuman Dutta
Data Matching Software: A 2026 Buyer’s Guide
Choosing data matching software used to be relatively straightforward. A team had duplicate customer records, inconsistent supplier names, or two databases that needed to be compared, so it implemented rules to identify likely matches. Enterprise environments in 2026 are more complicated. The same business entity can appear across CRM, ERP, billing, data warehouses, lakehouses, third-party feeds, operational applications, and analytics systems.…
Read MoreAWS Billing Glitch: A $1.5 Trillion Lesson in Data Reconciliation
A billing glitch turned Cost Explorer into a horror movie for a weekend. Nobody paid a cent, but the failure underneath is one for every data team, and every AI agent, should recognize. What Happened During the AWS Billing Glitch On the night of July 16, AWS customers watched their Cost Explorer dashboards do something no finance team ever wants…
Read MoreManaging Tariff Implications Through Data Integrity in Global Supply Chains
In today’s global marketplace, supply chains span continents. From consumer electronics to industrial machinery, companies rely on global sourcing and distribution to stay competitive. But with this reach comes complexity—especially when it comes to tariffs.
Read MoreAgentic Data Trust: Next Frontier for Data Management
As data grows exponentially, ensuring accuracy, security, and compliance is increasingly challenging. Traditional rule-based data quality checks—whether downstream (reactive) or upstream (proactive)—still produce substantial manual overhead for monitoring and resolving alerts. Agentic Data Trust addresses these issues by leveraging intelligent agents that minimize human intervention through automated oversight, rule updates, and data corrections. The result…
Read More5 Emerging Data Trust Trends to Watch in 2026
As organizations accelerate their data-driven initiatives, data quality is evolving from a manual, back-office function to a core business priority. By 2026, we’ll see a new generation of data quality capabilities seamlessly integrated into analytics pipelines, AI models, and decision-making frameworks. Here are five emerging trends reshaping the future of data quality: 1. Data Trust…
Read MoreChallenges With Data Observability Platforms and How to Overcome Them
Core Differences Between Data Observability Platforms and DataBuck Many organizations that initially embraced data observability platforms are now realizing the limitations of these solutions, especially as they encounter operational challenges. Although data observability platforms started strong—tracking data freshness, schema changes, and volume fluctuations—their expansion into deeper profiling has created significant drawbacks. Below, we explore the…
Read MoreImproved Data Quality Trust with DataBuck over “Spray and Pray DQ”
In the world of modern data management, many organizations have adopted data observability solutions to improve their data quality and accuracy. Initially, these solutions had a narrow focus on key areas such as detecting data freshness, schema changes, and volume fluctuations. This worked well for the early stages of data quality management, giving teams visibility…
Read MoreData Errors Are Costing Financial Services Millions and How Automation Can Save the Day?
Data quality issues continue to plague financial services organizations, resulting in costly fines, operational inefficiencies, and damage to reputations. Even industry leaders like Charles Schwab and Citibank have been severely impacted by poor data management, revealing the urgent need for more effective data quality processes across the sector. Key Examples of Data Quality Failures: These…
Read MoreData Integrity Issues in Banking: Major Compliance Challenges and Solutions
What Are Data Integrity Issues in Banking? Banks face a high cost when data errors slip through due to inadequate data control. Examples include fines for TD Bank, Wells Fargo, and Citigroup due to failures in anti-money laundering controls and data management. The main reasons for these data issues include non-scalable data processes, unrealistic expectations…
Read MoreEmpowering Data Excellence: the Role of Cloudera Data Lake, Features & Benefits.
In today’s data-driven world, organizations are collecting more information than ever before. But the true value of data lies not just in its quantity, but in its quality and trustworthiness. This is especially true for vast data repositories like Cloudera Data Lake. This guide explores Cloudera Data Lake, a platform designed to store and manage…
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