🚨Chapter 2 of our Data Pipeline Automation Guide has just dropped! 📚✨ This time, we’re diving deep into the world of data lineage diagrams. In this chapter, we explore: 🔍 Techniques to effectively visualize your data journey ✅ Best practices for maintaining data integrity and transparency 💡 Tips to enhance collaboration between teams Whether you’re a data novice or a seasoned pro, this chapter is packed with insights that will elevate your data management skills. 👉 Read the full chapter here: https://hubs.la/Q02N63Y90 #DataPipeline #DataLineage #DataManagement
Pantomath
Software Development
Cincinnati, OH 2,776 followers
Data pipeline observability and traceability platform for automating data operations and improving data reliability
About us
Pantomath is a data pipeline observability and traceability platform for automating data operations and improving data reliability through automated real-time monitoring and cross-platform pipeline lineage. Automated root-cause analysis helps resolve issues, minimizing data downtime, while automated impact analysis helps prevent poor decision-making with bad data, eliminating any possible business impact. Implementing Pantomath is simple; through a wide range of connectors for the most popular data tools that integrate seamlessly into complex data stacks providing autonomous pipeline lineage in minutes. Eliminate data reliability issues and automate data operations today.
- Website
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https://www.pantomath.com/
External link for Pantomath
- Industry
- Software Development
- Company size
- 11-50 employees
- Headquarters
- Cincinnati, OH
- Type
- Privately Held
- Founded
- 2022
- Specialties
- Data Observability, Data Traceability, Data Monitoring, Data Lineage, Data Pipeline Observability, Data Pipeline Testing, Data Pipeline Troubleshooting, Data Operations, and Enterprise SaaS
Products
Pantomath
Data Quality Software
Pantomath is a data pipeline observability and traceability platform for automating data operations and improving data reliability through automated real-time monitoring and cross-platform pipeline lineage. Automated root-cause analysis helps resolve issues, minimizing data downtime, while automated impact analysis helps prevent poor decision-making with bad data, eliminating any possible business impact. Implementing Pantomath is simple; through a wide range of connectors for the most popular data tools that integrate seamlessly into complex data stacks providing autonomous pipeline lineage in minutes. Eliminate data reliability issues and automate data operations today.
Locations
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Primary
Cincinnati, OH 45040, US
Employees at Pantomath
Updates
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🚀 Unlocking Efficiency with Data Pipeline Automation Tired of manual data processing? Discover how modern automated data pipelines go beyond simple job scheduling to include advanced features like data observability, self-healing capabilities, and pipeline traceability. These tools not only streamline data operations but also ensure data quality and accuracy at scale. If you're managing complex data environments and want to stay ahead, this is a must-read. 👉 Dive deeper into how to future-proof your data processes - read the full blog here: https://hubs.ly/Q02Lp0-N0 #DataAutomation #DataOps #TechInnovation
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📊 Data observability gives you the ability to understand and troubleshoot your data system's health and performance. To truly understand how effective your data observability program is, you need to rigorously measure key metrics. However, with so many numbers and metrics to monitor, it's easy to suffer from measurement paralysis. 🤯 In this blog, we'll cover the ten most critical metrics for data observability and why they matter most. 👇 https://hubs.la/Q02KxgMC0 #DataObservability #DataQuality #DataAnalytics
10 Essential Metrics for Data Observability
pantomath.com
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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
The Evolution of Data Quality
pantomath.com
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Pantomath reposted this
Founder & Host of "The Ravit Show" | LinkedIn Top Voice | Startups Advisor | Gartner Ambassador | Evangelist | Data & AI Community Builder | Influencer Marketing B2B | Marketing & Media
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
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Pantomath reposted this
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
Boston Data Chiefs Assess Data Quality’s Impact on Data Culture at CDO Magazine Dinner
cdomagazine.tech
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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
Data quality roles and responsibilities | The data reliability RACI
pantomath.com
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Pantomath reposted this
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.
It has been over 2 years since Gartner started a research in Data Observability market. I have seen this technology growing tremendously. Not really a surprise, because it bridges a gap that traditional monitoring tools cannot do. If you think GenAI is #1 technology in the market catching the spotlight, I will say Data Observability is #2. Take a look at the Gartner new research “Market Guide for Data Observability Tools”, that provides the market definition ( 4 critical features + 5 observation categories), market direction (growing in demand and expending in coverage areas), market analysis (different from data quality solutions, and APM tools), and market segments (standalone/pure player and embedded capabilities), and representative vendors. Big thank you to my co-authors Jason Medd, Lydia Ferguson, Michael J. Simone! “Market Guide for Data Observability Tools”: Access the research from https://lnkd.in/gTMXj9fu (Available for Gartner members only) #dataobservability #datamanagement #gartnerda #dataquality #GartnerDnA
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"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
Taking Action Sooner: How Pantomath Helped Paycor Stay Ahead of Data Issues
pantomath.com
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Pantomath reposted this
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.
The Pantomath Data Quality Framework
pantomath.com