蒙特卡罗 的见解 is the first solution on the market to offer customers operational analytics about their data platform.
If you’ve asked these questions of your data systems, you’re not alone. While great solutions already exist to help teams understand operational analytics for their products, 不存在一个整体, end-to-end way to understand the operational analytics and 性能 of your data platform. 直到现在.
今天，我很激动地宣布 的见解, a new 蒙特卡罗 capability that helps companies understand which data is most important for the business, 从而增加数据信任.
建在 蒙特卡罗数据可观测平台, 的见解 leverages machine learning for monitoring and ranks events and assets based on their 使用, 相关性, 以及与其他表和资产的关系. 的见解 is the first solution to offer customers operational analytics about their data platform. 与见解, 客户可以测量和优化可靠性, 性能, 成本, 以及他们数据计划的有效性.
平均而言，公司是亏损的 每年超过1500万美元 在坏数据上，用数据工程师的钱 40%以上 – or 每周120小时 ——他们处理破碎的数据管道的时间. 而且经常, data teams have trouble understanding what their most critical data is, preventing them from focusing on data that actually matters when it comes to ensuring quality and 可靠性. 作为一个结果, teams are either wasting cycles trying to figure out what datasets they should be prioritizing and end up missing tables when setting up coverage.
蒙特卡罗’s mission is to accelerate the adoption of data by eliminating data downtime – in other words, 给数据团队必要的工具来信任他们的数据. 的见解 puts the metadata 蒙特卡罗 generates in the hands of data engineers to help them answer the most important questions around how their efforts ultimately lead to higher quality data. 与富人合作, 字段级血统, 企业可以得到一个整体, 跨业务的数据运行状况和利用率的端到端视图.
与见解, data teams can 访问 the synthesized metadata 蒙特卡罗 generates to build dashboards, 分析数据平台团队绩效, 甚至承诺并跟踪sla. The data itself can be downloaded as CSVs via the 蒙特卡罗 CLI or in the app, 以及雪花的客户, can be 访问ed directly in their Snowflake environment via secure data sharing. 这种程度的细节, 在软件工程和DevOps工具中很常见, makes it possible for data teams to understand what data matters most to the business based on 使用, 访问, 数据质量检查, 和自动血统. 另外, 的见解 makes it easy to create and share high-level reporting with CTOs and CDOs, 在公司内部培养更大的数据信任和所有权.
With the release of 的见解, 蒙特卡罗 is the first solution that can help companies understand:
- Which tables can be deprecated to reduce storage and processing 成本s
- Which tables drive key assets for the business, such as financial reports or executive dashboards
After teams have developed processes to address 数据质量 incidents with 蒙特卡罗, they often ask for help quantifying the impact of those efforts on the organization. 请求范围从想要定义sla和跟踪性能, 跟踪哪些团队正在遵循数据质量最佳实践, 哪些数据资产值得投资. 的见解 addresses this need by providing end-to-end visibility into operational analytics across the data stack.
“ShopRunner leverages data-driven insights to power business decisions and improve the user experience on our platform. 为推荐一个正规滚球网站的利益相关者建立成功的基础, we need to ensure that our data pipelines are performant and our data is reliable. 作为数据信任远景的一部分, we’ve integrated our Snowflake and Looker data stack with 蒙特卡罗’s 数据可观测性 Platform to gain unprecedented visibility into our data ecosystem through end-to-end lineage and ensure that data is accurate and up-to-date at each stage of its life cycle,瓦莱丽·罗格夫说, ShopRunner数据分析架构总监. We’re excited to partner with 蒙特卡罗 on their vision for 的见解, a new and powerful way to understand what data assets matter most to our business and how we can better drive an impact with our data across the organization.”
“Auto Trader leverages data-driven insights to power business decisions and improve the user experience on our platform. 确保数据涉众为成功而建立, we need to keep tabs on the 可靠性 and health of our data systems, 包括使用, 访问, 质量检查, 和sla,爱德华·肯特说。, Auto Trader UK首席开发者. “蒙特卡罗’s 的见解 Reports provides a novel and powerful way to understand what assets matter most to our business and how we can better drive an impact with our data across the organization.”
“作为一个数据驱动的组织, Kolibri Games produces over 100 million unique events per day across 40 different event types. 以确保这些数据是可信的, 推荐一个正规滚球网站利用蒙特卡罗的端到端数据观察平台,António Fitas说, Kolibri Games数据工程主管. “蒙特卡罗的最新产品, 的见解, we can better understand the operational analytics of our data platform, allowing us not just to trust our data but also optimize the storage, 可靠性, 使用, 以及推荐一个正规滚球网站数据产品的成本.”
公司已经尝试在内部构建这种解决方案, 但这些努力往往是临时性的, 手册, 资源密集型, 并且不能随着业务需求而发展. 的见解 is fully automated so it can cover a company’s entire data stack. 推荐一个正规滚球网站的机器学习根据活跃用户对数据资产进行评分, 平均每日读, 他们与上下游资产的关系, and their connection to important service-level indicators and 数据质量检查. Such insights make it easy for teams to set data strategy and prioritize accordingly based on what data matters most to the business.
要了解更多，请查看推荐一个正规滚球网站的 见解文档 or 请求一个演示.