Validio

Coalesce by dbt Labs is over for this year. ✅ We had a blast meeting you, chatting all things data, and watching you whack bad data in our game. Special shoutout to Lisa Tang at Veronica Beard for earning the high score in the game, and to Steven Caruthers at Heartland for being the lucky winner in the survey raffle. Hope you enjoy your Apple Watches! Until next time 👋 #dataobservability #dataquality #metricsmonitoring #dbtlabs #dbtcoalesce

Frontend Engineers, look here 👋 Are you passionate about creating stunning, user-friendly interfaces while leveraging backend knowledge to deliver seamless experiences? Do you want to make an impact by solving tomorrow's data quality challenges? We're looking for a talented Frontend Engineer to join our growing team. If you're excited about combining frontend expertise with backend capabilities and thrive in a dynamic, innovative environment, we want to hear from you! Read more and apply: https://lnkd.in/dWefHyUC ⚡️

Who else is ready for another day of Coalesce by dbt Labs? The Validio team sure is! 🤩 We’ll be at booth 404 (it’s not an error, we promise) for another day of chatting data quality, observability and automated business metrics monitoring. See you soon ⚡️ #dbtcoalesce #dbt #dataobservability #dataquality #metricsmonitoring

New week, new conference. This time the Validio team is at Coalesce by dbt Labs 👋 Are you also in Las Vegas? Come by booth 404 to play a game of whacking bad data and talk all things data quality, observability and automated metrics monitoring. #dbtcoalesce #dbt #dataobservability #dataquality #metricsmonitoring

Learn more about Elias Djurfeldt, one of our Software Engineers 🤩 💜

Thrilled to see Validio once again recognized as a High Performer in both Data Quality and Data Observability by G2! 🎉 A huge thank you to our incredible customers—your support and dedication drive us as we continue building the command center for the modern data stack. #dataquality #dataobservability #G2recognition

Have you tried to build Gen AI solutions to solve a particular problem, but they only work about half the time? We are proud to present Fredrik Bäckström, Co-Founder of Magnowlia and ex-CTO of Zettle, as one of the speakers for Heroes of Data Meetup #13 hosted by AWS Startups 🥳 In this session, Fredrik will share how you can improve the reliability of Gen AI solutions, allowing you to put your solution into production, and walk through a number of best practices on how to increase accuracy of Gen AI applications based on top of SQL data sources. Sounds interesting? Make sure to secure your ticket via the link below before they run out 🏃 https://lnkd.in/gFCMZc8u Not a member yet ? Sign up via the link below. https://lnkd.in/gDbDkN3K

Learn more about Sophy Ting , our Senior Technical Writer 🤩 💜

Common data quality pitfalls and how to avoid them The other day, one of our customers said: Data is our second most valuable asset after people. For companies where data plays a critical role, getting full control over your data quality isn't a luxury; it's a necessity. Below is a list of three common mistakes when it comes to data quality that I have identified through hundreds of conversations with data leaders, and my recommendation of what to do instead. #### 1 #### ❌ DON'T wait for data issues to escalate. Reactive data management means problems are only addressed when they're too big to ignore. Many times when I ask data leaders about their timelines to put something in place for data quality, they respond “yesterday, we have had a lot of big issues recently”. That is not a good position to be in. ✅ Instead: Have a proactive approach towards data quality. People usually don’t prioritise data quality until sh*t  has hit the fan. Make sure that you have a proactive approach to detect potential data issues and address them before they impact your business negatively. This is especially true if you want to become data- and AI-driven. #### 2 #### ❌ DON'T let a single team own data quality. When data quality is siloed and isolated to a specific team, the success rate is very low. The reason for this is that the root causes of data issues can be related to many different teams within an organisation, ranging from data producers (software engineers, product teams, etc.), data teams (data engineers, analytics engineers, etc.) to data consumers (data scientists, business end-users, etc.). The team who owns the part of the data pipeline that causes the issue is usually the best one to resolve it. ✅ Instead: Distribute ownership of data quality to different stakeholders throughout your data pipelines. Make data quality a shared responsibility by distributing the ownership of data quality across the data pipeline based on who can actually influence and resolve the issue. #### 3 #### ❌ DON'T treat all data as if it’s equally important. 90 % of enterprise data is never being used. Almost all of the value that the average enterprise gains from data comes from just 10% of the data that they have. Unused data should not be your top priority when it comes to data quality, since it will not have any impact on your business. I see that most companies perform data quality checks across all of their data, as if it’s all equally important. That creates noise and distractions and takes focus away from safeguarding the data that actually matters. ✅ Instead: Prioritize your most important data assets. Identify the data assets that are highly utilized and/or feed into critical downstreams use cases. Validate these data assets proactively with in-depth data quality monitoring and put thorough processes in place to resolve issues promptly as they are identified. What would you like to add to this list? Happy to hear your thoughts!

✨ Introducing role-based access control ✨ Today, we’re making it easier to manage roles and permissions in Validio. In this update, we are introducing three distinct roles: Editors, Viewers, and Admins. ✏️ Editors can create, edit, and manage validators - suitable for technical users who work directly with data. 👀 Viewers are granted read-only access, making it the ideal role for stakeholders and decision-makers who need to overview data but not alter it. 🔐 Admins have full access, allowing them to manage settings across users and teams. We’re also launching namespaces where you can customize permissions for specific teams or projects. This allows for efficient group permissions assignment, moving beyond individual user settings. 💻 Try it out and share your feedback. We're always happy to hear from our users. 💬 📎 Read the full release post in the link in the comments. #rolebasedaccesscontrol #dataquality #dataobservability