Analytics

Multivariate time series forecasting allows BigQuery users to use external covariate along with target metric for forecasting.

I just got an email from a venture capitalist. For about the hundredth time, the venture capitalist told me they were anxious to invest money in us. The only qualification was that we needed to already have at least $10 million in sales. If we had $10 million in sales, we wouldn’t need to be talking with the venture capitalist. How stupid is that? I suggested to the venture capitalist that they go invest in IBM or ATT because they do have $10 million in sales.

Do you know where your data is? Most organizations store data in various destinations (in-house databases, SaaS locations, cloud-based apps, etc.), which makes running analytics far more complicated. Imagine pulling data from all these destinations into one data warehouse or data lake. Life would be so much easier... "But doesn't this require a lot of code?" you may ask. Not necessarily.

There are few topics in the world right now that are hotter than data and its related fields. As technology, machine learning, and computer algorithms continue to expand, so does the way that companies can use this information to benefit their business. Extract, Transform, and Load (ETL) remains one of the most important processes in the area of big data. This area is absolutely booming, and so is the demand to learn more about its various processes and components.

Recently, I published a blog on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.

Google Cloud customers who want app-level encryption in hybrid cloud data warehouses can encrypt and decrypt that data outside BigQuery. Here’s how to do that securely.

The Chief Data Officer is arguably one of the most important roles at a company, particularly those that aspire to be data-driven. CDO appointments and the elevation of data leaders have accelerated in recent years, and the role has morphed as perceptions of data have evolved. Responsibilities span strategy and execution, people and processes, and the technology needed to deliver on the promise of data.

Organizations have been focused on enhancing customer experiences to enable quicker responses to services and to provide localized behavior for many years now. However, with the Internet of Things (IoT), Smart Cities, Gaming technologies and Self-Driving Cars going more mainstream, there is an even greater need for organizations to react faster to customer behavior and bring solutions closer to the customers.

Built with BigQuery: How to Accelerate Data-Centric AI development with Google Cloud and Snorkel AI.