Data pipelines can be a strenuous job, and building them for Big Data can become even  more difficult. But to really solve the complexity issue behind building data pipelines, we must understand their purpose. 

Because history repeats itself, we are facing the same challenges civilizations faced some 7,000 years ago, when the concept of tracking data was starting to flourish. Accounting was introduced in Mesopotamia in order to record the growth of crops and herds. Fast forward to the 20th century, when the first major data project was created in 1937, ordered by Franklin D. Roosevelt. After the Social Security Act became law in 1937, the government had to keep track of contributions from 26 million Americans and more than 3 million employers. IBM got the contract to develop punch card-reading machines for this massive bookkeeping project. And even though this concept was a necessity for many years, only 90% of the available data has been created in the last two years.

Naturally, the loads of data companies are collecting every day has left them scrambling with how to simplify it. Additionally, organizations have to split the process of data into three (at least) systems: data generating, data analyzing and data utilization. In order for these systems to work, analysts need a ‘vehicle’ not only to move data but also to take action with it.. This is where end to end data pipelines come in. As we describe them: 


A data pipeline’s job is to ensure these steps all happen reliably to all data. These processes should be automated, but most organizations will need at least one or two engineers to maintain the systems, repair failures, and update according to the changing needs of the business

Unfortunately, the solution it provides in the market ends at integrating and processing data, overlooking the real challenge: to connect its output into business applications. That’s the gold mine. According to Gartner this is one of the main reasons 85% of big data projects fail.

Going back to IBMs punch card, and even though it provided such a significant profit stream, instrumental to IBM’s rapid growth in the mid-twentieth century, it was only data collection and although hard, possible to analyze. If a company had the massive amounts of data collected by IBM and a data pipeline that could be operationalized and put into production in minutes, it would have been revolutionary. Imagine being able to segment employees to understand their efficiency based on any type of variable, and take this information to run ML predictive models that could provide key insights on their actions and behaviors to then act upon them to increase efficiency across an organization. 

Actionable pipelines reduce the complexity of working with data by 10X, increasing operational efficiency across departments, and introducing new collaborative methods. 

By centralizing, cleaning, operationalizing and taking action based on its outcome, empowers teams to work with data, saving the company time and money from building in-house systems that typically tend to fail. Learn more about data pipelines here.