Centralizing an organization’s data in a cloud data warehouse gives all stakeholders big-picture access to everything going on at the company.
Join the DZone community and get the full member experience. The past year was a rough one for companies large and small. As the pandemic spread and living and working indoors became the norm, customer preferences and demands changed seemingly by the minute. The companies that were able to respond quickly to those changes thrived. The key to their agile response? They utilized the most up-to-date, current data to generate deep and timely insights into their business. If data is siloed or dated, it can’t be used effectively across an organization. Centralizing an organization’s data in a cloud data warehouse gives all stakeholders big-picture access to everything going on at the company, including customer data, product information, and research. In today’s rapidly changing climate, this is crucial. DataOps has emerged as an effective process to manage all this data because it automates data flows and transformations throughout the business. DataOps comprises two main ideas: governance and automation. Governance means bringing best practices from software development to data analytics. This includes: Each of these best practices enables the trust and maintainability of data pipelines. Achieving trust and maintainability enables a data engineering team to move onto other projects, knowing that they will be alerted whenever something goes wrong in their existing processes — before it impacts the business. Data governance is important because it is the process companies use to manage the utilization, security, and integrity of their data. Key to any new initiative is the trust and standardization of data using best practices. Data governance has proven to be critical in industries (aerospace, pharmaceuticals, and many others) where the most up-to-date and accurate data is essential to achieving both quality and safe results. Many projects are undertaken with a “get it done” ad hoc process in place — for example, someone’s personal Excel spreadsheet, with no institutional knowledge of how it was created.
