Organizations in the financial services sector face a unique set of challenges as they consider how to wrangle and process the vast amount of data they collect. During our Financial Services Summit, I was lucky enough to speak to Brian Anthony, chief data officer for the Municipal Securities Rulemaking Board (MSRB), to learn how the MSRB is integrating technologies such as artificial intelligence (AI) and machine learning to modernize its data.

Machine learning (ML) models have become key drivers in helping organizations reveal patterns and make predictions that drive value across the business. While extremely valuable, building and deploying these models remains in the hands of only a small subset of expert data scientists and engineers with deep programming and ML framework expertise.

At Snowflake, putting the customer first is an essential company value. But “customer-centric” is more than just a buzzword: We use a data-driven, outside-in lens on everything we do, at all levels of the company. In particular, here’s how Snowflake Support is listening to you—our customers—and continuously improving the Snowflake customer experience at every touchpoint.

The business world is rapidly pivoting all the time. Strategic shifts, reprioritization and being first all require being smart while moving fast. The value of agility has never stood out more due to the need to react to new realties in everything from public health, remote and in-office business policies and workflows, to broader economic concerns like supply chain as we move into recovery and revitalization.

29 experts share their take on what’s a good profit margin for a new business to help you understand what you should be aiming for your own company.

It's a week after Black Friday, and the results are in! While online spending didn't break a new record this year, it still totaled a massive $8.9 billion, making Black Friday one of the biggest sales days ever for digital merchants. Online sales were even healthier on Cyber Monday, totaling $10.7 billion. But how did paid marketing contribute to all those holiday shopping season sales? Some e-commerce retailers struggle to measure the effectiveness of their paid campaigns.

ETL (extract, transform, load) is the backbone of modern data integration, efficiently migrating massive quantities of information into a data warehouse like Snowflake. But with so many Snowflake ETL tools on the market these days, how can you choose the best for migrating your data? Below we’ll discuss our favorite Snowflake ETL tools, including their pros, cons, and user reviews so that you can make the choice that’s right for your situation.

In part 1 of this blog post, we discussed the need to be mindful of data bias and the resulting consequences when certain parameters are skewed. Surely there are ways to comb through the data to minimise the risks from spiralling out of control. We need to get to the root of the problem. In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI.

ETL pipelines help companies extract, transform, and load data so it is ready to provide insights and value to the company. But running a smooth data operation depends on building reliable and scalable data ingestion pipelines. SaaS vendors like Keboola and Azure Data Factory take away the heavy lifting.