Monte Carlo的动态

Saying yes to everything will ALWAYS lead to a data team burning out. When you say yes to every request from stakeholders, something will suffer. Usually, that is the quality of the work you are providing... Data quality checks and observability are the first two things that suffer. Less tests are put into place... Less time is spent monitoring model run time... Less documentation happens... And then your data models start failing, become a black box, or aren't useful because nobody understands them. We need to change the narrative to focus on quality > quantity, which includes getting stakeholders on board as well! Less requests accepted, more time spent on the work deemed of high importance, and more scalable data environments that we can continue to build upon.

“I know quality data is important, but where do I start?” Embarking on a journey to optimise your data management doesn't have to be overwhelming. By breaking it down into a straightforward three-step process – Assess, Resolve, and Monitor – we can efficiently organise and safeguard your valuable data. https://lnkd.in/ehBMtRa5 If you would like to discuss your data management or request a product demo. We’d love to hear from you.

Shoutout to the data teams that work tirelessly every day to build the data pipelines that drive our companies forward. ?? (And that’s on top of firefighting data quality incidents as they're discovered) We wanted to make their jobs (just a little) easier, so we released Incident Resolution metrics to help data teams track their successes with two key metrics: Total number of incidents opened vs closed and average time to resolution. Now—whether it’s for an OKR or just to frame on the wall—data teams can: Review the total number of incidents without needing to pull data from ticket-tracking tools Use the time previously spent on incident resolution to scope additional headcount or work planning P.S. You can check out more Incident Resolution use cases in the comments. ?? #dataengineering #analyticsengineering #dataanalytics

Let's face it, cleaning up data can feel like conquering a mountain, only to find another peak of inconsistencies waiting. This frustration often comes from a critical oversight: the lack of a data governance strategy. Data is dynamic. New information, human error, and system updates can quickly make even the cleanest data sets obsolete. Sonar bridges the data governance gap. Our robust solution empowers your organization to: - Automate data validation: Eliminate manual error checks and streamline data entry. - Implement data quality checks: Gain real-time insights into data health, ensuring consistent accuracy. - Establish clear data ownership: Foster accountability and empower teams to maintain data integrity. With Sonar, you can make data-driven decisions with confidence. #Data #LetUsHelp #MeetSonar #PoweredbyAscendco #SPD #SimplyInstrumental #TheMoreYouKnow

Struggling to make sense of your data? We totally get it. VAULT recently tackled a migration of 28 million records for a client, transforming messy, raw data into actionable insights. Our custom data mapper made it possible—and a tool like this could help your business, too. Stay tuned as we unveil our latest case study next week, where we’ll dive deeper into how this solution works and how it can streamline your own data processes.

Ready to supercharge your data reliability? Our latest workbook is here to guide you through building a robust data reliability program! Discover the six-step process to ensure your data quality is top-notch: ?? Profiling Data: Dive deep into understanding your data. ?? Defining Policies: Create clear guidelines to maintain high standards. ?? Detecting Anomalies: Catch issues before they escalate. ?? Monitoring Impact: See how data quality drives business performance. ?? Notifying Experts: Ensure timely interventions from the right people. ?? Continuous Optimization: Keep refining your data processes. Our workbook includes practical exercises and checklists at each step to make implementation straightforward and effective. https://lnkd.in/g_wPjxtj

Today, confidence in data quality is critically important. The growth of #ArtificialIntelligence and #MachineLearning demand granular-level data quality. Read more to learn how executives can build a strategy for ongoing robust #data quality management.?https://hubs.li/Q02cpbsX0 #UserExperience #ArtificialIntelligence

Discover the key to unlocking impeccable data quality with our latest exploration, 'Essential Steps for Superior Data Quality Assurance.' ?? In today’s data-driven landscape, the caliber of your data can significantly influence your business outcomes. Our in-depth guide delineates critical steps to enhance your data quality, ensuring accuracy, reliability, and actionable insights. From establishing rigorous data governance protocols to leveraging cutting-edge technologies for data validation, these strategies are designed to fortify your organization's data integrity. Dive in to transform your data quality practices and drive unparalleled business value. ?? [https://lnkd.in/dp9f4Wax] #DataQuality #DataGovernance #BusinessIntelligence

Elevate operational efficiency and decision-making by prioritizing data consistency! Learn key strategies with Artha Solutions to overcome data inconsistencies and drive optimal business outcomes. #DataConsistency #DataQuality #OperationalEfficiency #BusinessIntelligence #DataManagement

Ever found yourself scratching your head over why the modern data stack (MDS) is so complicated? Well, it's apparently because of this micro-specialization trend. Every category is jam-packed with a ton of tools, making it a bit overwhelming. We had a chat with Sanjeev Mohan, and he's got some awesome tips on how to streamline and upgrade your data stack & processes! Full episode: https://lnkd.in/gQAqi4Vg