Artificial intelligence (AI), automation and machine learning (ML) are rapidly transforming the analytical experience for everyday business users in 2021. Whether it’s automated visualizations, continuous analysis, or reduced time-to-insight, there are many practical benefits of augmented analytics that are well documented and fully realized today.
You may have read about Snowflake’s IPO last year. But you probably didn’t hear about all the work that the Snowflake security team did in preparation. Our corporate security program went through a security analytics review to ensure that it satisfied the new security policy requirements resulting from the IPO. Here are a few lessons that we learned when setting up automated security control validation on our Snowflake security data lake.
Keep the bigger picture in mind as you build and use your analytics dashboards. Between devices, websites, applications, online service providers, and platforms of all kinds, modern businesses rarely have a single data source to analyze in our continuously connected world. That’s why how information is presented is almost as important as the quality of the information itself, making the difference between leading with confidence or simply flying blind.
When you’re providing APIs to your customers, you want to ensure they are getting value from them. At the same time, the best APIs are designed to be fully automated without requiring human intervention. This can leave your customers in the dark on whether your API is even being used by the organization and if you’re meeting any SLA obligations in your enterprise contracts.
Businesses today have a growing demand for real-time data integration, analysis, and action. More often than not, the valuable data driving these actions—transactional and operational data—is stored either on-prem or in public clouds in traditional relational databases that aren’t suitable for continuous analytics.
Continuous evaluation—the process of ensuring a production machine learning model is still performing well on new data—is an essential part in any ML workflow. Performing continuous evaluation can help you catch model drift, a phenomenon that occurs when the data used to train your model no longer reflects the current environment.
The data lakes concept has come back into popular focus with Amazon Athena, an innovative, serverless solution. But does it fit into your organization’s data stack? This article covers Amazon Athena capabilities, pros and cons, competitors, and use cases.
COVID-19 vaccines from various manufacturers are being approved by more countries, but that doesn’t mean that they will be available at your local pharmacy or mass vaccination centers anytime soon. Creating, scaling-up and manufacturing the vaccine is just the first step, now the world needs to coordinate an incredible and complex supply chain system to deliver more vaccines to more places than ever before.
