Pantomath

Data Quality has meant many different things to different people over the years. While the ultimate goal—ensuring reliable, accurate, and trustworthy data—remains unchanged, the approach to achieving it has adapted to meet modern demands. See how Pantomath's approach to solving data quality & reliability issues is the evolution enterprises have needed for a long time 👉https://lnkd.in/gTdruPSn #DataQuality #DataObservability #BigData

Have you ever wondered what makes your data truly reliable and trustworthy? Check out "The 5 Pillars of Data Observability" as defined by data observability leader Pantomath. It's a must-read for anyone looking to enhance their data reliability and ensure that their data operations run smoothly -- https://bit.ly/4feXCAv The article breaks down the five key pillars that form the foundation of robust data observability: -- Pipeline Traceability: An end-to-end view of data pipelines from data producers to data consumers. Granular technical job & data lineage to highlight every relationship mapping across the data ecosystem. -- Operational Observability: Monitoring data in motion components within a data pipeline including data ingestion, transformation, orchestration, and refresh jobs for latency, not started jobs, data movement, and failure issues. -- Data Observability: Monitoring data at rest components within a data pipeline to ensure the data is complete and fresh. -- Data Profiling: Monitoring data distribution and segmentation to ensure the data is well structured. -- Data Quality: Regular checks and validations to ensure your data is accurate, complete, and trustworthy. Implementing these pillars can significantly enhance your organization's ability to trust and utilize data effectively. As we move towards more data-driven decision-making, having a solid data observability framework becomes crucial. I highly recommend giving this article a read and reflecting on how you can apply these principles to your data strategy. What steps are you taking to improve data observability in your organization? #data #ai #dataquality #pantomath #theravitshow

Top data, analytics, and AI executives from Boston gathered to discuss “The Impact of Data Reliability and Data Quality Issues on Achieving a Data-Driven Culture” at CDO Magazine’s Executive Boardroom Dinner on July 16. Thank you to our esteemed speakers and partner Pantomath for facilitating such enriching conversations. ➡ Read the recap article to get your key takeaways now: https://lnkd.in/gXAMAuNS Jordan Alcott; Kishore Aradhya; Alex Aronov; Jane Chen; Snimar Chhabra; Venkata Chirravuri; Veronika Durgin; Brian Finn; Ashwini Ghogare,Joshua Glastein; Ben Joseph; Sravan Kasarla; Kiran Kodali; Brent Mahan; Jennifer McGhee; Raj Nimmagadda;  Carlos Peralta; Kenneth Rispoli; Prashant Shah; Tarun Sood, DBA; Prem Swaroop; Bharat Tripathi; 🇷⋅🇺 Renato Umeton,; Krishna V.; Connor Glazier; Somesh Saxena; Paul Walker;Dmitri Adler; Steve Wanamaker; Franklin Kessler; Camille Prado | Data Society Group

Who is responsible for maintaining trust in data & ensuring data reliability in an organization? This is a challenging question for most large and complex organizations to answer. As we implemented and rolled out Pantomath with several large F500 customers and enabled them to be successful with a first-of-its-kind platform, a RACI framework for data reliability has emerged. This framework designed by Craig Smith, Pantomath's Head of Customer Experience, highlights who in the organization needs to be Responsible, Accountable, Consulted, and Informed when it comes to data reliability and quality issues. Read more about it in our latest blog. https://lnkd.in/ggmcU48F

Excited to see Pantomath featured in Gartner's 2024 Market Guide for Data Observability Tools. Pantomath's unique focus on end-to-end data pipeline observability & traceability goes way beyond traditional data observability and has enabled several Fortune 500 enterprises to trust their data and eliminate data reliability issues.

"Pantomath enables our teams to focus on building new solutions rather than getting bogged down in operational tasks. We’re seeing a significant uptick in productivity while ensuring our data is reliable and trustworthy." - Rick Huff, CIO at Paycor Learn more about how Pantomath enabled a data-driven culture at Paycor through data observability. https://lnkd.in/g9CUsUDK

When working with enterprise data, data engineers and analysts know the feeling of dread when you're met with the age old question, "why does my report look wrong?". This happened countless times during my time leading Enterprise BI at General Electric. At Pantomath, our customers have found that 90% of the time, the issue has nothing to do with the data itself, but was caused by a "leaky" pipeline creating and moving bad data. Data Quality monitoring should be a feature within your data pipelines, not a stand-alone product. Without end-to-end traceability and proper incident response, Data Quality solutions are only reactive and do not solve the problem. Learn more about our approach to Data Quality here.

As a data leader, I saw my technical teams, business stakeholders and executives struggle with data quality and reliability challenges. And nothing in the market came close to solving this complex and nuanced problem. Talking to peers in the industry I got the validation that the problem is widespread and has a huge impact on businesses. One day, I decided to spend an entire weekend in my home office away from all the market noise, asking myself what an ideal solution would look like. And that’s where Pantomath was born!   Read more about the founding story of Pantomath and my motivation behind leaving my job at General Electric to build a data pipeline observability & traceability platform. https://lnkd.in/gFkaBKCE

Great article on the startup and support ecosystem in Cincinnati. Fortunate to be surrounded by industry leading companies, innovative leaders, great talent and a strong partner network.

Day 1 at the Snowflake Summit where I had the pleasure of interviewing Somesh Saxena from Pantomath on The Ravit Show. Here’s a rundown of our discussion: - Somesh introduced himself and shared insights about Pantomath - We explored how Data Observability has evolved over the last four years - Discussed the key pillars of Data Observability - Somesh shared some intriguing use cases with our audience - We delved into how AI is making a significant impact in the Data Observability space. Stay tuned for more insights and conversations from the Snowflake Summit! #data #ai #snowflakesummit #snowflakeflake2024 #theravitshow