Without a system to ensure good data quality, errors such as dropped columns, duplicated data, and inconsistencies across disparate systems prevent your organization from making smart business decisions using data-driven insights. Worse, poor data quality can lead to missed strategic opportunities: you can’t deliver great insights if you don’t trust your data. Anomalo automatically monitors … Continued
What do new ML-powered applications, advanced analytics, and improved cybersecurity have in common? Three words: high-quality data. Many enterprises today rely on the Snowflake Data Cloud to centralize all their business data and facilitate access, governance, and collaboration, letting them build faster than ever. Data-powered businesses know that quality is key: you can’t deliver great … Continued
Modern businesses are built on data. For a company to successfully operate at scale, they need a way to manage their data at scale, too. That’s why so many organizations use data lakehouses like Databricks to efficiently store data for both machine learning and business intelligence applications. And because companies use their data to drive … Continued
In the age of big data, managing and governing data assets has become increasingly complex. Data is spread across different systems and departments, making it challenging to locate, understand, and verify. To address this issue, many organizations have turned to data catalogs, which are centralized repositories of metadata about data assets. Last year, Databricks launched … Continued
Data quality is a mission-critical reliability concern. Teams small and large trust Anomalo to deliver relevant, timely insights into their data quality, but making the most of those insights requires being ready to take action. ‍ Incident management platforms like Opsgenie, included with Atlassian Open DevOps, allow IT teams to respond to operational issues more … Continued
In today’s digital era, data is increasingly becoming the most valuable asset for businesses. Enterprises are collecting data from various sources, including transactions, customer engagement, and other data points. However, this growing volume of data presents unique challenges for organizations, such as how to manage, catalog, and ensure it can be trusted. This is why … Continued
For a modern business, data drives effective decision-making processes. Companies need to store large amounts of data, then efficiently run machine learning and BI analyses against their dataset. In addition, they need to protect their data quality and prevent issues like corrupted or missing data that could skew the results of their models and dashboards. … Continued
Data incidents are a part of doing business at scale, and that’s why so many companies turn to Anomalo for data quality monitoring. Many of our customers also use Jira to track issues and manage the incident resolution process. Today, we’re excited to announce a public preview of our Jira integration, that empowers users to … Continued