Data pipelines, essential for managing Big Data, face challenges that echo those encountered since the early days of data tracking in Mesopotamia. A significant historical development occurred in 1937 when the U.S. government, under Franklin D. Roosevelt, commissioned IBM to create punch card-reading machines for Social Security bookkeeping. Today, though 90% of data has been generated in the last two years, companies struggle to simplify and utilize it effectively. Modern data pipelines are designed to automate and streamline the collection, analysis, and utilization of data, yet many projects fail because they don't integrate these outputs into business applications. The potential for revolutionizing efficiency lies in actionable pipelines, which can enhance operational efficiency by centralizing, cleaning, and operationalizing data, thus empowering teams and reducing the need for often unreliable in-house systems.