What does this mean for those of us managing or trying to understand fast-growing companies?

This is a guest post with exclusive content by Bill Inmon, Mary Levins, and Georgia Burleson. Bill “is an American computer scientist recognized by many as the father of the data warehouse. Inmon wrote the first book, held the first conference, wrote the first column in a magazine, and was the first to offer classes in data warehousing.” -Wikipedia.

The data landscape is exploding with tools. As data professionals we have at our fingertips specialized tools for anything: from specialized databases (graph, geo, you name it) to tools for SQL-driven transformations (looking at you, dbt). Yet, a lot of data work is about provisioning, selecting, administering, and just maintaining those tools. Which is just a pain. As Pavel Dolezal, CEO and co-founder of Keboola said: The answer is in how the Modern Data Architecture is built.

Data integration is the data engineering process of combining data across all the different sources in a company (CRM, SaaS apps like Salesforce, APIs, …) into a single unified view. The data integration process includes data extraction, data cleansing, data ingestion, data validation, modeling, and exposing ready-to-be-consumed data sets to other users and applications for business intelligence or data-driven activities.

Unravel now pulls in data quality checks from external tools into its single-pane-of-glass full-stack observability view.

Think about the times you've wished you had more insight into your business data. Or all of the times you wished you could answer questions about your business performance without waiting for someone else to get back to you. Gone are the days when businesses rely solely on IT staff to provide reports and analytics. With self-service business intelligence (BI), users can create their own reports, dashboards, and data visualizations without relying on IT help.

Digital transformation. Everyone has their own ideas about what digital transformation means, so I decided to look up a few definitions.

If you’ve already centralized your log analysis on BigQuery as your single pane of glass for logs & events…congratulations! With the introduction of Log Analytics (Public Preview), something great is now even better. It leverages BigQuery while also reducing your costs and accelerating your time to value with respect to exporting and analyzing your Google Cloud logs in BigQuery.