Data visualization is one of the most important capabilities of any business intelligence (BI) and analytics solution. It helps people translate complex data into a visual context, like a chart or a graph, identify trends numbers alone can't easily reveal, and discover hidden patterns in your dashboard. Data visualization also provides a wealth of additional benefits, such as enabling easier understanding of the correlation between operations and results.

A key part of business is the drive for continual improvement, to always do better. “Better” can mean different things to different organizations. It could be about offering better products, better services, or the same product or service for a better price or any number of things. Fundamentally, to be “better” requires ongoing analysis of the current state and comparison to the previous or next one. It sounds straightforward: you just need data and the means to analyze it.

We’re back with another Job of the Week – but this time, we’re taking a step back to cover a concept we’ve skipped over in previous segments: OAuth2 authentication. Richard’s demonstrations often show simpler shortcuts to accessing data – but these shortcuts may not always be practical in real-world examples. Never fear! We’ll arm you with the know-how you need to make your data hacks just as impressive in real life.

Imagine having self-service access to all business data, anywhere it may be, and being able to explore it all at once. Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested. Imagine independently discovering rich new business insights from both structured and unstructured data working together, without having to beg for data sets to be made available.

Data visualization helps people comprehend and attain insight into big data. It represents complex data in visually interesting ways that assist in our understanding, and paves the way for a greater link between the provided raw data, and our overall engagement with it. Nowadays, we accumulate data in ever-increasing sizes, so we need an intelligent way to understand such vast volumes of information. In analytics, we often use different types of data visualization to convey complex datasets.

The management of data assets in multiple clouds is introducing new data governance requirements, and it is both useful and instructive to have a view from the TM Forum to help navigate the changes.

How can you ensure data quality and security across your data analytics pipeline? With data governance – the exercising of authority and control over your data assets. It includes tracking, maintaining and protecting data at every stage of the lifecycle.

We are now well into 2022 and the megatrends that drove the last decade in data—The Apache Software Foundation as a primary innovation vehicle for big data, the arrival of cloud computing, and the debut of cheap distributed storage—have now converged and offer clear patterns for competitive advantage for vendors and value for customers.

In part 1 of this blog series, we looked at how Snowflake supports the GEOGRAPHY geospatial data type, which works with the earth as an ellipsoid, measuring distances over a curvature and plotting objects using the latest World Geodetic System, WGS84.