Data scientists and machine learning engineers in enterprise organizations need to fully understand their data in order to properly analyze it, build models, and power machine learning use cases across their business. Due to the lack of tooling specifically designed for data discovery, exploration, and preliminary analysis, this presents a significant challenge for these teams.

Since 2015, the Cloudera DataFlow team has been helping the largest enterprise organizations in the world adopt Apache NiFi as their enterprise standard data movement tool. Over the last few years, we have had a front-row seat in our customers’ hybrid cloud journey as they expand their data estate across the edge, on-premise, and multiple cloud providers.

How we eat, exercise, work, and rest play an important role in influencing our health outcomes. It’s been established that healthcare and life sciences (HCLS) organizations can improve health outcomes when they have access to this type of data on patients to inform real-world evidence.

Embedded analytics can help companies gather as much information as possible and gain a competitive edge, be it via sophisticated data mining, data visualization, and reporting, capabilities. However, not all business intelligence (BI) partners are equal. This blog will help you identify what you should look for in an embedded analytics partner. Table of Contents.

Data visualization tools help turn complex data into intuitive charts, and enable faster understanding of key insights from large datasets. However, for most reporting needs, you need more than data visualization tools for effective business intelligence (BI). For example, not everyone in a team may be technically proficient in SQL, or know how to consume data using charts, graphs and heatmaps.

Editor’s note: The post is part of a series highlighting our partners, and their solutions, that are Built with BigQuery. To fully leverage the data that’s critical for modern businesses, it must be accurate, complete, and up to date. Since 2007, ZoomInfo has provided B2B teams with the accurate firmographic, technographic, contact, and intent data they need to hit their marketing, sales, and revenue targets.

If someone asked you what makes data “healthy”, what would you say? What IS data health? Healthy data just means data that is quality, accessible, trusted, and secure, right? Wrong. Let's dissect. Data health really has nothing to do with the data itself, if you think about it.

It’s hard to believe enterprise BI platforms have been around for three decades. In that time, they have served the purpose of collecting and analyzing large amounts of data to help businesses make more informed decisions. But in today’s data-driven economy, analysts struggle to keep up with the myriad of business intelligence reports from traditional BI tools – which fail to effectively and efficiently analyze and interpret data in real-time.

Machine learning (ML), more than any other workflow, has imposed the most stress on modern data architectures. Its success is often contingent on the collaboration of polyglot data teams stitching together SQL- and Python-based pipelines to execute the many steps that take place from data ingestion to ML model inference.

Embedded analytics has become a priority for many companies today. It means building data visualizations and business intelligence (BI) features right into public-facing apps. However, some businesses don't like this idea. They don't want to feature a third-party solution in their first-party applications - leading to a lack of data and analytics used.