Data is important to optimize how a company functions; it makes processes efficient and effective. And while there are stages to using said data, companies must begin at the top – collection. Today, it’s easy to find a divide between companies that easily manage the collection or generation of data as a by-product of their service or product and others who struggle to achieve the same.

Take companies like Google or Facebook, which provide a compelling enough offering to have people willingly provide them with data in exchange for free access to some of their products.

On the flipside, other companies might require external data sources to provide them with most of their data. In addition to having similar processing or storage needs as companies that have their data flowing through from their own users, these organizations require ways to interface with the underlying data providers in a consistent and reliable manner.

In either scenario, both companies require a methodology to manage their respective data pipelines effectively.

What is the Importance of Managing a Data Pipeline?

Two analogies. If data is akin to lifeblood for a data product, then data pipelines are its veins and organs. Another one? Data is oil to your organization, and oils need pipelines. They are critical in ensuring that data products function as they were meant to by design, providing continuous value to the business while staying up-to-date with real-world changes.

If you ask a data scientist – who might prefer spending his or her time in the algorithmic modelling of data – what occupies most of their time, the answer is more likely to hover around aspects of data munging, cleaning, and management. Of course, smarter companies have their ways – varying in nature – of reducing that data grime, allowing their scientists to be more focused on analysis and modelling.

But if you break a pipeline down for the sake of simplicity, you begin to see it for its value and importance. It starts with data acquisition followed by data storage, retrieval, alerting, and monitoring.

      1. Data Acquisition
        1. As we had previously mentioned, everything starts with the process of acquiring data. Some industries churn high amounts of data on a minute by minute basis, while others struggle to maintain that base. Regardless, data is produced frequently enough, and companies must possess an effective way of gathering said data.
      1. Data Storage
        1. It doesn’t make any sense to have a data product without data storage, but given our present industrial condition, it is possible to store data with well-developed, robust, qualitative, low-cost solutions. Take S3 and PostgreSQL for example, scalable file-storage systems that can handle multiple writes and reads at the same time.
      1. Data Retrieval
        1. Applications that require data to train models, perform analysis, or make predictions, must have an effective means to obtain data from a database management system. This could be via hard coded rules or through feature-based models such as machine learning.
      1. Alerting & Monitoring
        1. Things go wrong, and they always do. It’s a way of life, which is why you need to be able to make sure that you can fix them as soon as possible. That’s what this component of a data pipeline is all about. Larger technology companies are known to use custom build alert configurations, SMS, PagerDuty, and even email among many others.

In conclusion, data pipelines are important and critical for data products. Consider data pipeline a valuable part of the modern business; it’s what you can use to run your organization efficiently. And trust us when we say, it’s possible even with simple metrics.

At GlobalEdge, we use data to manage our offerings effectively across industries, namely telecommunications, consumer electronics, semiconductors, automotive, and industrial automation. Contact us to learn more about how we can help your organization at s.rakesh@globaledgesoft.com.


Rakesh Sankar
He can be reached at s.rakesh@globaledgesoft.com.