What Does Bad Data Cost You & How to Make Bad Data Healthier?

Data fuels the growth of any modern organization, but what happens when the fuel goes bad? The growth stops. Data-driven enterprises rely heavily on their collected information to make important business decisions, but if this information contains errors, the organization may have to suffer huge losses. In 2021, Gartner reported that organizations incur an average loss of USD 12.9 million due to poor-quality data.

Accelerate data modernization initiatives with Talend Change Data Capture

In times of economic uncertainty, businesses need to get the most value from their data, while minimizing pressure on their systems and databases. But with the exponential growth of the variety and volume of data, extracting business value out of that data is only getting increasingly difficult. The result is data transfer latency, data loss, high cost of managing such data, data sources, leading to an inability to use data to make high-ROI business decisions.

Yellowfin 9.8 Release Highlights

Introduced in 9.7 as the simplest way to ask questions of your data, Yellowfin 9.8 delivers exciting updates to Guided NLQ, and additional improvements to our report builder. The latest release makes Guided NLQ even more powerful and simpler to use, with new question types (Cross-tab), more intuitive questions with field synonyms and default date periods, and protection against long-running queries - in addition to faster report building.

G2 Fall 2022 Reports: Keboola rated top in 7 categories

Since the beginning, Keboola has been designed to be a world-class data platform as a service. Based on the results of this season’s G2 statistics, we have succeeded yet again. The purpose of the Keboola platform is to make our customers’ data processing simple, reliable, and transparent throughout the company. We love our customers and value the feedback they've given our teams over the years.

Demo: Unravel Data - Automated Troubleshooting for Job Failures

For DataOps teams, job failures are common. But finding the issue is (traditionally) where things get even worse. It can take hours or days to troubleshoot a job failure. Unravel Data provides a single view where DataOps teams can locate exactly where–and why–a job failed, along with precise recommendations to troubleshoot the error. DataOps teams are now able to both diagnose and troubleshoot job failures in minutes instead of days or weeks.

Demo: Unravel Data - Data Pipeline Optimization (The Easy Way)

Data pipelines fail all the time for a variety of reasons; service downtime, data volume fluctuations, etc. Diagnosing these failures manually is very difficult and time consuming. Unravel Data allows DataOps teams to troubleshoot pipeline failures automatically – showing exactly where and why a pipeline failed, and precise recommendations to remedy the issues. Using Unravel, DataOps teams can now diagnose and fix data pipeline failures in a fraction of the time.