Systems | Development | Analytics | API | Testing

Protecting sensitive data and preventing unintended data exposure is critical for businesses. However, many organizations lack the tools to stay on top of where sensitive data resides across their enterprise. It’s particularly concerning when sensitive data shows up in unexpected places – for example, in logs that services generate, when customers inadvertently send it in a customer support chat, or when managing unstructured analytical workloads.

The cloud data platform combines data warehouse and data lake capabilities to support the exploding world of analytics. Like a data warehouse, the cloud data platform structures, transforms, and queries data. Like a data lake, it classifies multi-structured data objects in an elastic object store. The cloud data platform provides an ideal launchpad for modern business intelligence (BI) projects that need fast, flexible access to lots of varied data. As you might expect, this is a tall order to fill.

We’re excited to announce the general availability of the unstructured data management functionality in Snowflake. We launched public preview of this functionality in September 2021, and since then we have seen adoption by customers across industries for a variety of use cases. These use cases include storing and securing call center recordings, securely sharing PDF documents in Snowflake Data Marketplace, storing medical images and extracting data from them, and many more.

Companies nowadays are well aware of the importance of embedded analytics when it comes to being data-driven. Today, building your own analytics infrastructure into your software applications for your customers is not the only option anymore. There is a growing market of embedded analytics tools that offer purchasable solutions for data analysis.

The term “AI-first” has received its share of attention lately, especially in the boardroom where strategies to gain a competitive advantage are always welcome. But before a company embarks on an AI-first strategy, it pays to understand what it is and how it will transform the organization.

To this point, AI has been applied to augment analytics in a somewhat bifurcated fashion. On one hand, we have seen natural language support the business consumer that requires simple answers to known questions, helping them quickly take action. And, on the other, AI helps content authors and BI developers auto-suggest charts and automate data preparation, improving efficiency and reducing manual workloads. But, there’s a gap, and the value is huge.