View From the Top by Douglas McDowell Chief Strategy Officer
A Data-Driven Culture Starts with Intelligent DataOps
Every business operating today is data-driven by necessity. Companies need accurate data to make sound business decisions, deliver innovative products, manage field operations—and conduct countless other imperative business functions. But removing barriers to a smooth data pipeline can be a challenge because of siloed teams, poor processes, and inadequate tools.
A well-functioning DataOps practice consists of these four core pillars:
- Data integration—Connecting to disparate data sources is a constant requirement for most businesses. Good data integration processes start with having the right integration technology, such as high-performing SSIS components for managing data warehouses.
- Data validation—Data-centric applications should be tested with the wildest-possible data. This step is often overlooked in many data pipelines, but it’s critical to ensuring that your business leaders are making decisions based on sound data. Setting up an automated testing framework is a cost-effective way to restore confidence in your business data.
- Metadata management—Creating a clear map of your data estate is a first step in gaining control of your data. With the right technology, you can consistently document databases and track data lineage, which is important for projects such as preparing for a cloud migration or ensuring compliance with data privacy regulations.
- Observability—More than just monitoring, observability involves building efficiencies into the data pipeline from the start. With good observability practices in place, you’ll see opportunities to improve availability and deliverability throughout the pipeline, starting with development.
Building a robust DataOps practice can transform the way your company manages data—and dramatically increase ROI.