Qualytics

If you don’t trust your data, how can you trust your decisions? A recent study from HFS Research found that 75% of business executives don’t fully trust their data—and honestly, it’s not hard to see why. ?? Common data pain points we hear all the time: ?? Your reports don’t match. Finance and operations teams spend hours reconciling numbers. ?? Customers are frustrated. Bad data in CRM or billing systems leads to incorrect invoices, poor service, and lost trust. ??AI & analytics teams are spinning their wheels. They spend more time fixing bad data than building new models. ??Regulators are watching. Data errors put compliance at risk, leading to hefty fines and reputational damage. Poor data leads to poor decisions. Qualytics empowers businesses to identify and fix data issues before they disrupt operations—ensuring accuracy, efficiency, and confident decision-making. #qualytics #dataquality #datamanagement #dataobservability #GenAI #MLops #bigdata

One Customer, Many Names — Sound Familiar? One of the most common (and frustrating) challenges is ensuring that your organization has a single, trusted view of the customer. But here’s the problem: Your CRM calls them “John A. Smith”, billing knows them as “J. Smith”, and support logs them as “Jonathan Smith”. Multiply this across thousands (or millions) of records and suddenly… ?? Your reporting is off ?? Your customer experience suffers ?? Your compliance risk increases This is where Entity Resolution becomes your best friend. By reconciling duplicate or inconsistent records across systems, entity resolution helps create that elusive golden record, ensuring data integrity across the enterprise. Without Qualytics: It would take roughly 4 to 6 hours for a mid-level data engineer or data scientist to script an entity resolution check. If you’re new to dedupe or fuzzy matching workflows, it might take a full day (6-8 hours) to figure out blocking, labeling strategies, and threshold tuning. With Qualytics: You can create an entity resolution check in under 3 minutes (no code) and depending the size of the dataset, scans can be completed in under 5 minutes. Minimize manual effort while delivering data confidence. Learn more: https://lnkd.in/eUXnBeTP #dataquality #mdm #dataobservability #datagovernance #dgiq #qualytics #genai #mlops #bigdata

Qualytics: charting the course to better data governance-because in the high seas of data quality, you need more than just a compass… you need a trusted vessel to steer clear of compliance icebergs and keep your data seaworthy. #dataquality #dataanalytics #datagovernance #genai #dgiq

How a Leading Asset Manager Automated Data Quality for Real Estate Investments ?? A top asset manager, overseeing billions in real estate, struggled with inconsistent property data, rent roll errors, and financial discrepancies—leading to valuation risks and reporting delays. With hundreds of data feeds from property managers, financial institutions, and market sources, manual data validation wasn’t scalable. The Fix? Data Quality Automation with Qualytics ? Continuous anomaly detection across property, financial, and rent roll data ? Automated reconciliation for cleaner transactions & valuations ? ML driven rules to adapt to new data sources ? Collaborative platform engaging business and technical stakeholders The Impact ? 90% reduction in manual data validation ? 99% increase reconciliation accuracy ? Millions saved on engineering time Is your firm still relying on manual checks? Let’s talk. ? https://lnkd.in/e7quVqg7 #RealEstate #AssetManagement #DataQuality #Automation #AI #Qualytics

Empowering Business Users to Take Control of Data Quality Data quality has long been seen as a technical problem—but the real impact is felt across the business. When data is unreliable, operations slow down, compliance risks increase, and decisions become guesswork. The solution? Put data quality in the hands of business users. With Qualytics, non-technical teams can: ? Identify and resolve data issues without relying on engineers. ? Set business-driven data quality rules to ensure accuracy. ? Gain real-time visibility into anomalies and inconsistencies. ? Collaborate seamlessly with data teams to maintain trust in critical reports and insights. Data quality is no longer just an IT function—it’s a business imperative. When organizations empower teams across the company to engage in data quality, they unlock better decision-making and stronger outcomes. How is your company involving business users in data quality? Let’s discuss. https://lnkd.in/e4m-X5Bk #dataquality #dataobservability #dataops #mlops #dgiq

New in Qualytics: Materialized Operations SMEs and analysts can now persist transformed data in their chosen datastore without repeatedly running complex transformations. By materializing the results of user-defined transformations (like computed tables & computed fields), teams can efficiently perform downstream data quality analysis on pre-processed data. Business Impact: Empowers SMEs – Access transformed data without waiting on repeated processing. Boosts Data Trust – Ensure accuracy with persistent transformation results. Optimizes Performance – Eliminates redundant transformation processing. Drives Better Decisions – Efficiently analyze complex data structures (like parsed JSON) for reporting and compliance. Interested in seeing it in action? Let's connect: https://qualytics.ai/demo/

Building a Stronger Data Foundation: Why Data Warehouse Governance Matters A well-governed data warehouse is the backbone of trusted analytics and AI—yet many organizations struggle with inconsistent data, access bottlenecks, and compliance risks. Effective data warehouse governance ensures: - Data Quality – Prevent inaccuracies before they impact decisions - Access Control – Enable the right people with the right data - Operational Efficiency – Reduce duplication and streamline workflows - Regulatory Compliance – Stay ahead of evolving data laws Want to improve data reliability and scalability? Check out this article on the 7 best practices for data warehouse governance. #dataquality #qualytics #dataobservability #bigdata #datagovernance #AI #genai

Marc Benioff recently posted on X: "What breathes life into AI is the data & metadata that describes the data to the model—just like oxygen for us". Couldn't agree more, data is THE strategic asset of our time - yet 73% of executives are unhappy with their data, more than half of them admitting to making a bad decision based on bad data in the last 6 months. But why? Well, data quality is hard. The status quo of the reactive approach - data teams waiting to spot an issue (or wait to be yelled at), and then do something about it - simply cannot scale. Business teams and data stewards know their data and its idiosyncrasies the best, but best they do is to create a ticket for someone in dataops to write a rule. The real fix? Bridge the gap between data and business teams. 1. Make DQ a business conversation 2. Prioritize proactive monitoring 3. Shift from silos to shared responsibility AI / BI success depends on a fundamental truth: trusted data is non-negotiable. Qualytics #dataquality #datagovernance

Data Observability or Data Quality? Why Not Both? Most teams struggle with either reactive observability or rigid data quality rules: ? Observability alerts you after something breaks. ? Traditional data quality frameworks require constant rule maintenance Qualytics combines both, ensuring: ? Real-time observability – Detects volumetric anomalies, schema drift, and data inconsistencies before they spread. ? Proactive data quality enforcement – Adaptive learning, dynamic anomaly detection, and integrity scoring to prevent bad data from impacting your business. No more surprises. No more firefighting. Just trusted, high-quality data every time. Learn more: https://lnkd.in/eaPV5XHy #dataquality #dataobservability #qualytics #data #genai #ml #datagovernance #dgiq

If you don’t trust your data, how can you trust your decisions? A recent study from HFS Research found that 75% of business executives don’t fully trust their data—and honestly, it’s not hard to see why. ?? Common data pain points we hear all the time: ?? Your reports don’t match. Finance and operations teams spend hours reconciling numbers. ?? Customers are frustrated. Bad data in CRM or billing systems leads to incorrect invoices, poor service, and lost trust. ??AI & analytics teams are spinning their wheels. They spend more time fixing bad data than building new models. ??Regulators are watching. Data errors put compliance at risk, leading to hefty fines and reputational damage. Poor data leads to poor decisions. Qualytics empowers businesses to identify and fix data issues before they disrupt operations—ensuring accuracy, efficiency, and confident decision-making. #qualytics #dataquality #datamanagement #dataobservability #GenAI #MLops #bigdata