Telmai

Better Together 🤝❣ With Google's mission to organize the world's data and Telmai's commitment to ensuring reliable data, we enable users to transform vast data into trustworthy and actionable outcomes. 📽 Check out this video where our co-founder and CEO Mona Rakibe discusses how the synergy between Telmai's AI-driven data observability platform and Google Cloud (GCP) is a perfect fit for empowering businesses to harness data for success by: • 🔄 Ensuring a continuous, reliable data flow with sophisticated quality workflows, such as circuit breakers and DQ binning, directly within Cloud Composer. • 📊 Streamlining data governance by adopting both user-defined and ML-driven data quality rules, enhanced with comprehensive data health reports. https://lnkd.in/ggibSziX #dataobservability #genai #googlecloudplatform #datamanagement

Keep your data shining bright 🪔 ✨ & anomalous frights at bay cause Telmai's got your back 🎃 👻 Team Telmai wishes you a joyful Diwali & a fun Halloween!

Data quality breaches and compliance failures often make headlines. But poor data quality doesn’t just affect reports—it drives hidden costs, inflating cloud and infrastructure budgets. Most teams stay stuck in reactive mode, fixing errors downstream but what if these issues could be resolved at the source? In his article, Andy Sawyer discusses the Shift Left approach, explaining how embedding data quality checks early can reduce downstream corrections. Andy highlights the hidden costs of reactive data management and how proactive measures could avoid the challenges of broken data pipelines, allowing data teams to focus on high-value tasks as well as avoid the pitfalls of data silos and rework cycles. Ready to stop data errors from derailing your operations? Find the link in the comments to learn more. #DataQuality #ShiftLeft #CloudSpending #DataManagement #DataGovernance #DataEngineering #DataOps

As Appal Badi, Head of Enterprise Data Products at Silicon Valley Bank, shared in a recent podcast series, "You can have the best model in the world, but without the right data, you're only halfway there" In financial services, trust is built on reliable data. Even one misplaced value can ripple through reports, trigger fines, and undermine credibility. With regulators demanding precision, data quality isn’t just important—it’s non-negotiable. Financial institutions are increasingly turning to AI and ML for better forecasting and decision-making. However, as Appal Badi pointed out, the path to reliable financial operations starts with proactive data governance.  Check out this article that explores how financial services firms can embed data quality into pipelines using modern data observability tools to detect issues before they escalate to ensure compliance, boost operational efficiency, and maintain trust with stakeholders—because, without clean data, even the best algorithms can’t deliver meaningful insights. Click on the link in the comments section to learn how ensuring data quality could maximize the dividends from your data. #DataQuality #AI #ML #DataObservability #FinancialServices #DataGovernance #DataDriven #FinancialForecasting

𝟮 𝘄𝗲𝗲𝗸𝘀 to deploy, 𝟯 𝗵𝗼𝘂𝗿𝘀 to profile and validate complex critical data assets, and less than 𝟭 𝗱𝗮𝘆 to get actionable insights as well as generate tickets to address DQ issues. These figures represent the key success metrics ZoomInfo achieved after integrating Telmai’s data quality framework into their workflows. ZoomInfo leveraged Telmai to build a scalable architecture with automated data quality monitoring across 1 billion data records with 268 nested JSON attributes spread over a heterogeneous data architecture that includes open table formats like Apache Iceberg as well as systems like Snowflake and Google Bigquery. Record-level anomaly detection ensured their pipelines remained reliable, accelerating ZoomInfo's time-to-value. Interested to know more? Click on the link in the comments section to access the complete case study. #Dataquality #Dataobservability #Opentableformats #Apacheiceberg #Snowflake #ZoomInfo

AI applications depend on vector databases to efficiently manage and retrieve high-dimensional data. From powering recommendation engines to enabling semantic search, Vector databases are essential for conducting similarity searches, connecting disparate data points, and delivering relevant recommendations. However, their effectiveness is only as strong as the quality of the data they store, and corrupted vectors and stale data can quickly affect your data-driven outcomes. In this detailed piece, Maxim Lukichev, co-founder of Telmai and CTO, explores the core data quality problems plaguing vector databases. He discusses how outdated vectors or misaligned metadata can impact AI-driven applications, leading to biased recommendations and poor search results. Max outlines practical strategies to maintain data integrity, ensuring that vector databases consistently deliver accurate and relevant outputs by focusing on real-time data monitoring and proactive management. Want to know more? Click on the link in the comments section to access the complete article. #DataQuality #VectorDatabases #DQ4AI #DataObservability

Is your data team spending more time firefighting than building scalable solutions? 🚒 Are your data pipelines clogged with redundant transformations and struggling to meet evolving SLAs? The lack of alignment between data sources and user needs could be the root cause of your pipeline challenges. Animesh Kumar, CTO and co-founder, and Travis Thompson, Chief Architect of The Modern Data Company, lay out a framework for turning data into a product that actively shapes infrastructure. They share how aligning SLAs with aggregated data products creates faster, more reliable systems. By shifting from reactive customizations to a proactive data product model, teams can reduce complexity and deliver insights efficiently. If you’re looking for practical strategies to transform your data infrastructure, this guide is a must-read. Click on the link in the comments section to know more. #DataProducts #DataInfrastructure #TechDebt #SLAs #DataEngineering

A table shows it was updated an hour ago—fresh data, right? Not so fast. A timestamp alone won’t tell you if the data is still accurate if only a few rows were updated, or worse, anomalous or junk data slipped in during the update. Various techniques exist to help you monitor and understand your data's health, depending on factors like how often you access your data, the volume of data you handle, and the need for real-time insights. Check out this article, where we dive into two essential techniques for monitoring data health: Data Observability and Metadata Observability. We break down how each method works, highlight their unique benefits, and discuss how using both together ensures you get a full, accurate picture of your data landscape. 📊🔍 Ready to get the full picture? Click the link in the comments to read the full article. #DataObservability #MetadataObservability #DataQuality

Data-driven decisions are only as good as the data behind them. Small and unnoticed shifts in data can have massive consequences, including misleading forecasts, failed predictions, and costly outcomes. Tracking trends against historical data can reveal drifts that might otherwise go unnoticed, ensuring the accuracy of your data-driven initiatives. Data drifts can manifest as changes in input features, shifting relationships between variables, or evolving target values. In this article, we explore how SQL—a tried-and-tested language for data-related operations can be used to spot these drifts early through statistical comparisons and range checks. We also explore how increasing data volumes and evolving pipeline complexities may require complementing SQL with more advanced technologies to ensure consistent and scalable data quality. Want to dive deeper? Click the link in the comments section! #DataDrift #DataQuality #SQL #DataPipelines #DataObservability

With fragmented systems, scaling AI initiatives becomes a challenge, not an opportunity. Managing numerous pipelines leads to more problems: higher overhead, operational inefficiencies, and slower time to value. Taylor Brown, COO of Fivetran, explains how fragmented data architectures create redundant workloads, duplicated governance, and conflicting insights. He discusses how a unified architecture simplifies data management, reduces governance complexities by bringing automation into the mix, and aligns all data processes to ensure consistency across batch and streaming operations. With less time spent managing infrastructure, teams can focus on driving data-driven initiatives and achieving faster time to value. 📖 Click on the link in the comments section to catch the full story on why a unified architecture matters for AI #DataArchitecture #DataLakes #DataPipelines #Fivetran #AI