The end goal of clinical technology organizations in the US and abroad is to use modern technology to bring life-saving new treatments to fruition. Leaders in this sphere help generate the evidence and insights to help biotech, pharmaceutical, medical device, and diagnostic companies accelerate value, minimize risk, and optimize outcomes. Life sciences clients recognize that technology is the answer to inefficiencies and delays in delivering new treatments to the public.

The term “observability” means many things to many people. A lot of energy has been spent—particularly among vendors offering an observability solution—in trying to define what the term means in one context or another. But instead of getting bogged down in the “what” of observability, I think it’s more valuable to address the “why.” What are we trying to accomplish with observability? What is the end goal?

Finance professionals know that data matters, but stories convey truth in ways that mere numbers simply cannot. Those who work in finance may describe themselves as “numbers people.” They have a natural affinity for quantitative information, as well as a knack for drawing meaningful conclusions when presented with a collection of numerical figures. Even so, finance team members probably understand and retain information more readily when it’s presented in narrative form.

To be effective, solo data analysts or engineers need to focus on refining algorithms and analyzing data, not building data pipelines.

Data visualization is a core element and key capability of embedded analytics. Embedded data visualization tools create real-time visualizations (charts, graphs, etc) and can be embedded into any software application or integrated into a cloud or standalone app, and provides every user quick and easy ways to visualize information. This article explores the benefits of using embedded data visualization tools in your applications, and discusses how it can improve upon the traditional analytics experience.

Since I joined Snowflake, I have been asked multiple times what data warehouse modeling approach Snowflake best supports. Well, the cool thing is that Snowflake supports multiple data modeling approaches equally. Turns out we have a few customers who have existing data warehouses built using a particular approach known as the Data Vault modeling approach, and they have decided to move into Snowflake. So the conversation often goes like this.

E-commerce is the future of B2C retail, with online retailers expected to generate a total of $5.55 trillion in sales in 2022. The benefits of e-commerce are almost endless: This business model overcomes geographical limitations, allows merchants to sell new products and services 24 hours a day, and eliminates many of the costs associated with managing a brick-and-mortar store. But do you know how e-commerce influences B2B analytics?

As data takes the center stage for many organizations, data experts and business leaders are figuring out how to analyze it all. The total amount of data created, consumed, and copied globally has reached 64.2 zettabytes in 2020 and forecasted a CAGR of 19.2% from 2022 to 2025. In other words, we need better tools to leverage it! Organizations today leverage their data to provide better services, build better products, and create new revenue streamlines.