Data can be a source of strategic advantage for any enterprise, but only if you can unlock its full potential. The paradox is a simple one: more data complexity leads to less visibility, making it challenging to deliver value from data at scale.
For the IBM® Chief Data Office (CDO), getting value from data is essential. Data is the fuel of IBM’s work to become a more productive company, and core to this strategy is the integration of data from various sources to deliver insights through data pipelines. A data pipeline is a sequence of operations that extracts, transforms, and loads data from diverse sources into a data warehouse or data lake for analysis—providing data consistency and efficiency.
For the CDO team, responsible for managing and maintaining the company's nearly 4,000 data pipelines, the traditional approach to managing pipeline health was cumbersome and time intensive. This involved manual monitoring and troubleshooting of issues which consumed a substantial amount of time and was prone to human error. “Managing pipeline health across our large volume of pipelines was a significant challenge, especially when teams are using different products,” explains Ashley Delport, senior engineering manager for CDO. “We needed a solution that could continuously monitor data health, proactively identify and resolve issues such as missing data and notify our teams if service was going to be impacted."
The team required something more efficient and effective as they continued to scale data pipelines for business insights and AI use cases. They wanted an innovative solution that provided real-time visibility into pipeline performance, automated monitoring and enhanced collaboration. Most importantly, the team didn’t want to lose the trust that they had earned from tens of thousands of users who relied on them for accurate, timely, high-quality data to run the business.