Data Engineers Spend 2 Days Per Week Firefighting Bad Data
September 13, 2022
Share this

Data professionals are spending 40% of their time evaluating or checking data quality and that poor data quality impacts 26% of their companies' revenue, according to The State of Data Quality 2022, a report commissioned by Monte Carlo and conducted by Wakefield Research.

The survey found that 75% of participants take four or more hours to detect a data quality incident and about half said it takes an average of nine hours to resolve the issue once identified. Worse, 58% said the total number of incidents has increased somewhat or greatly over the past year, often as a result of more complex pipelines, bigger data teams, greater volumes of data, and other factors.

Today, the average organization experiences about 61 data-related incidents per month, each of which takes an average of 13 hours to identify and resolve. This adds up to an average of about 793 hours per month, per company.

However, 61 incidents only represents the number of incidents known to respondents.

"In the mid-2010s, organizations were shocked to learn that their data scientists were spending about 60% of their time just getting data ready for analysis," said Barr Moses, Monte Carlo CEO and co-founder. "Now, even with more mature data organizations and advanced stacks, data teams are still wasting 40% of their time troubleshooting data downtime. Not only is this wasting valuable engineering time, but it's also costing precious revenue and diverting attention away from initiatives that move the needle for the business. These results validate that data reliability is one of the biggest and most urgent problems facing today's data and analytics leaders."

Nearly half of respondent organizations measure data quality most often by the number of customer complaints their company receives, highlighting the ad hoc - and reputation damaging - nature of this important element of modern data strategy.

The Cost of Data Downtime

"Garbage in, garbage out" aptly describes the impact data quality has on data analytics and machine learning. If the data is unreliable, so are the insights derived from it.

In fact, on average, respondents said bad data impacts 26% of their revenue. This validates and supplements other industry studies that have uncovered the high cost of bad data. For example, Gartner estimates poor data quality costs organizations an average $12.9 million every year.

Nearly half said business stakeholders are impacted by issues the data team doesn't catch most of the time, or all the time.

In fact, according to the survey, respondents that conducted at least three different types of data tests for distribution, schema, volume, null or freshness anomalies at least once a week suffered fewer data incidents (46) on average than respondents with a less rigorous testing regime (61). However, testing alone was insufficient and stronger testing did not have a significant correlation with reducing the level of impact on revenue or stakeholders.

"Testing helps reduce data incidents, but no human being is capable of anticipating and writing a test for every way data pipelines can break. And if they could, it wouldn't be possible to scale across their always changing environment," said Lior Gavish, Monte Carlo CTO and co-founder. "Machine learning-powered anomaly monitoring and alerting through data observability can help teams close these coverage gaps and save data engineers' time."

Share this

The Latest

June 01, 2023

The journey of maturing observability practices for users entails navigating peaks and valleys. Users have clearly witnessed the maturation of their monitoring capabilities, embraced DevOps practices, and adopted cloud and cloud-native technologies. Notwithstanding that, we witness the gradual increase of the Mean Time To Recovery (MTTR) for production issues year over year ...

May 31, 2023

Optimizing existing use of cloud is the top initiative — for the seventh year in a row, reported by 62% of respondents in the Flexera 2023 State of the Cloud Report ...

May 30, 2023

Gartner highlighted four trends impacting cloud, data center and edge infrastructure in 2023, as infrastructure and operations teams pivot to support new technologies and ways of working during a year of economic uncertainty ...

May 25, 2023

Developers need a tool that can be portable and vendor agnostic, given the advent of microservices. It may be clear an issue is occurring; what may not be clear is if it's part of a distributed system or the app itself. Enter OpenTelemetry, commonly referred to as OTel, an open-source framework that provides a standardized way of collecting and exporting telemetry data (logs, metrics, and traces) from cloud-native software ...

May 24, 2023

As SLOs grow in popularity their usage is becoming more mature. For example, 82% of respondents intend to increase their use of SLOs, and 96% have mapped SLOs directly to their business operations or already have a plan to, according to The State of Service Level Objectives 2023 from Nobl9 ...

May 23, 2023

Observability has matured beyond its early adopter position and is now foundational for modern enterprises to achieve full visibility into today's complex technology environments, according to The State of Observability 2023, a report released by Splunk in collaboration with Enterprise Strategy Group ...

May 22, 2023

Before network engineers even begin the automation process, they tend to start with preconceived notions that oftentimes, if acted upon, can hinder the process. To prevent that from happening, it's important to identify and dispel a few common misconceptions currently out there and how networking teams can overcome them. So, let's address the three most common network automation myths ...

May 18, 2023

Many IT organizations apply AI/ML and AIOps technology across domains, correlating insights from the various layers of IT infrastructure and operations. However, Enterprise Management Associates (EMA) has observed significant interest in applying these AI technologies narrowly to network management, according to a new research report, titled AI-Driven Networks: Leveling Up Network Management with AI/ML and AIOps ...

May 17, 2023

When it comes to system outages, AIOps solutions with the right foundation can help reduce the blame game so the right teams can spend valuable time restoring the impacted services rather than improving their MTTI score (mean time to innocence). In fact, much of today's innovation around ChatGPT-style algorithms can be used to significantly improve the triage process and user experience ...

May 16, 2023

Gartner identified the top 10 data and analytics (D&A) trends for 2023 that can guide D&A leaders to create new sources of value by anticipating change and transforming extreme uncertainty into new business opportunities ...