Bigeye’s Dependency-Driven Monitoring Boosts Reliability of CRM Data

Bigeye’s Dependency-Driven Monitoring platform, announced on March 25, is a data observability solution that allows enterprise data teams to see more trustworthy results from their CRM systems.

The process lets data teams connect analytics dashboards and map every dependency across modern and legacy data sources. It also enables analysts to deploy targeted data observability to stay reliable by default.

According to the Bigeye 2023 State of Data Quality Report, 70% of business leaders do not trust analytics dashboards to make critical decisions due to recurring data quality incidents. The company hopes to change that statistic with its optimized data observability capability for every column, powering essential dashboards of analytics and data products.

“We’ve spoken with hundreds of enterprise data leaders, and despite investing heavily in data quality tools and processes, they still struggle to deliver reliable data analytics to business users,” noted Bigeye CEO and Co-founder Kyle Kirwan.

The data observability industry has not yet solved the problem of handling the complexity and size of large enterprise data pipelines. Enterprise dashboards have a long list of dependencies that span modern and legacy technologies. Data observability platforms have yet to offer genuine support for the types of hybrid environments nearly all Fortune 500 companies have, he explained.

Importance of Data Observability

According to Kirwan, data observability enables organizations to understand the state of their data at all times so they can find and fix issues before they impact business operations, which helps customers answer questions like, “Where is data coming from and going within our pipelines?”

It also answers concerns such as “Is it arriving on time and with the volume we expect?” “Is the quality high enough for our use cases?” or “Are there any recent anomalies in the data that indicate it may have a problem?”

Bigeye provides enterprise-grade data observability access for modern and legacy data stacks. Its platform brings together data observability and end-to-end lineage with scalability and security.

The result gives enterprise data teams unmatched insight into the reliability of data powering their business, whether data is stored on-premises, in the cloud, or hybrid.

“All organizations use data to power strategic decision-making, user experience, and efficient operations. Incomplete or incorrect data too often makes it into these processes undetected — significantly impacting business performance, customer satisfaction, and employee trust,” Kirwan told CRM Buyer.

Bigeye helps eliminate these challenges while simultaneously improving efficiency and reducing the cost of data operations, he added.

How Data Observability Works

Data observability in a data pipeline is a complex task that depends on the sequence and dependencies of various processing jobs.

Bigeye addresses this complexity by integrating enterprise-grade lineage technology with data observability. This process enables automatic, column-level tracing of the entire data pipeline, including ETL stages, and across the cloud to on-premises environments, providing a thorough and secure monitoring system that you can rely on to catch any anomalies.

When issues arise, Bigeye immediately alerts the relevant data source owners via Slack or Microsoft Teams and can auto-generate tickets in IT service management tools like JIRA and ServiceNow for streamlined incident management.

Without precise knowledge of which columns are vital for business operations, data engineering teams often implement wide-ranging monitoring across numerous tables and columns to ensure they catch any anomalies. This broad monitoring approach, while thorough, leads to higher computing costs, excessive alert noise, and the burden of unnecessary monitoring.

Bigeye’s solution, however, allows data analysts and business users to initiate data observability from their essential dashboards and focus on monitoring only the crucial columns, thereby reducing overhead and enhancing efficiency.

Who Needs Data-Watching Features?

Kirwan noted that common industries utilizing Bigeye’s approach include financial services, insurance, health care, high-tech, and retail. He emphasized that managing data pipelines is a challenge faced by organizations of all sizes, including smaller companies. “Large enterprise pipelines are particularly tricky because of their size, complexity, and the breadth of data sources and tools they employ,” he clarified.

Bigeye Dependency-Driven Monitoring provides significant benefits compared to other data observability approaches. These include:

• Faster time to value and improved trust for data consumers
• Clear visibility into the health of the entire analytics data pipeline for analysts
• Reduced alert noise and faster issue resolution for data engineers
• Lower total cost of ownership and less compute overhead for data leaders

Better Dealing With Dependency-Driven Monitoring

For data consumers, Bigeye displays data health updates directly in users’ analytics dashboard to provide instant insight into the reliability of their analytics. Data engineering teams can then use Bigeye’s lineage-powered root cause and impact analysis to quickly trace the data problem to the source for fast triage and resolution.

Data lineage has become a ubiquitous feature for many data operations tools. Due to the complexity of mapping lineage in legacy or on-premises environments, most tools require the customer to use complex, custom APIs or manual entry to try and capture a complete picture of an enterprise pipeline.

Before this launch, Bigeye provided data quality monitoring, data pipeline monitoring, and data lineage for cloud data warehouses such as Snowflake, Databricks, and Google BigQuery and transactional data sources such as MySQL. The launch of Dependency-Driven Monitoring expands Bigeye’s capabilities in two significant areas:

• It lets users trace column-level lineage beyond the cloud data warehouse into traditional and on-premises data sources such as SAP HANA, Vertica, and Oracle with Bigeye Lineage Plus.
• Users can now identify specific dependencies for analytics dashboards and data products anywhere in the data pipeline and deploy recommended monitoring on them.

Data Quality Report Inspired Innovation

Bigeye’s 2023 State of Data Quality Report revealed the need for a better solution. Respondents told Bigeye researchers that building an in-house data quality monitoring solution would take 37,500 full-time equivalent (FTE) hours.

Roughly, that equates to one year of work for 20 engineers. Other highlights of that report include:

• 70% of respondents reported at least two data incidents that diminished the productivity of their teams.
• Data issues most commonly take one or two days to spot and fix, but long-tail jobs often last weeks and months.
• Respondents reported experiencing at least two severe data incidents in the last six months, which damaged the business/bottom line and were visible at the C-level.

“Insights from this report were part of Bigeye’s primary and secondary research to validate the need for Dependency-Driven Monitoring. In addition, we also spoke with hundreds of data leaders, industry experts, and partners,” Kirwan said about what led the company to develop the new technology.

He added that the Dependency-Driven Monitoring feature is a new capability available as part of Bigeye’s data observability platform.

Beyond Vertical Industry Use Cases

Bigeye focused on analytics reliability as the first solution. Poor analytics data quality is a pervasive challenge across large enterprises, Kirwan observed. Solving it delivers immediate, demonstrable value to our customers. He said the additional use cases for Dependency-Driven Monitoring are vast and can include monitoring dependencies for everything from ML models to data products and gen AI projects.

Bigeye Lineage Plus is the technology that enables Bigeye Dependency-Driven Monitoring. It is a complete data lineage technology built to handle the largest, most complex enterprise pipelines. It includes 50 connectors for transactional databases, cloud data warehouses, data lakes, ETL platforms, analytics tools, and more.

“Without complete, column-level lineage, we would be unable to trace the dependencies upstream from an analytics dashboard. The ability to maintain column-level precision across enterprise data technologies is uncommon, and only a handful of the largest data vendors offer anything like it,” he concluded.