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.
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.
In 2010, Eric Schmidt, then CEO of Google, made the startling claim that every two days we humans generate as much information as we did from the dawn of civilization to today, or about five exabytes of data. At the time, we had TB disk drives and could only imagine an exabyte, which is one million terabytes. The next increments from TB is the peta byte and then the zettabyte, which is 1,000 exabytes. By the end of 2010, the world had crossed the zettabyte threshold.
In a previous article, we talked about the lost art of questioning and its importance when working with data and information to find actionable insights. In this article, we will expand on this topic and explain how questioning differs depending on what stage in the process you are from transforming data and information into insights.
At Talend, we tend to describe poorly organized, unhealthy data as “digital landfills.” But we don’t often talk about actual landfills. That’s right, the ones filled with trash. As anyone watching real estate prices will know, land is a finite resource. It’s crazy to think that we’re still dedicating land to storing our garbage, where it will sit releasing pollutants and greenhouse gases for decades to come.
At Mercado Libre, we are obsessed with unlocking the power and potential of data. One of our key cultural principles is to have a Beta Mindset. This means that we operate in a “state of beta”, constantly asking new questions of our data, experimenting with technologies and iterating our business operations in service of creating the best experiences for our customers.
Without a central place to manage models, those responsible for operationalizing ML models have no way of knowing the overall status of trained models and data. This lack of manageability can impact the review and release process of models into production, which often requires offline reviews with many stakeholders.