Data is the lifeblood of any successful business. It is fundamental to the way we run our personal and professional lives. Virtually every encounter generates data, be it software applications, social media links, mobile communications, or the growing numbers of digital services. Each of these encounters then generate even more data.

It is estimated that the world’s data will grow to 175 zettabytes by 2025. Nearly 2.5 quintillion bytes of data was generated on a daily basis in 2020. With data being at the forefront, it is essential to remember that eventually data is as data does. Meaning, the true power of data will be realized only by what it achieves. Only by leveraging the scope, magnitude and exponential rise of data can we generate insights and tell apart the leaders from the laggards.

As it stands, most companies recognize the potential of data but they still struggle with mobilizing it for meaningful impact. It is in this context that automated data pipelines become such an integral element of the conversation.

 

Towards An Automated Data Pipeline?

In the simplest terms, a data pipeline is the process by which raw data is moved from a source to a destination after simple or complex transformations are performed on it. When it comes to cutting-edge technologies, fully automated data pipelines may not seem like a priority. However, if you want to unlock the full potential of your data universe by extracting business intelligence and real-time insights, you need better control and visibility into your data source and destination. Developing a true data-driven ecosystem comes when you can extract data from its source, transform it, integrate it and analyze it for business applications.

There’s eventually more to it than making the data people happy– disparate data can hold the entire company back. This is corroborated by the fact that 55% of B2B companies say their biggest challenge lies in leveraging data from disparate sources in a timely manner. Further, up to 80% of analytics projects still require manual data preparation and ingestion.

According to 76% of data scientists, preparing their raw data for analysis is the least enjoyable part of their job. However, while the challenges of manually extracting data from various applications, transforming formats with custom code, and loading them into siloed systems are real, they can hardly be set aside. As businesses move from managing data to operationalizing AI, Gartner estimates a 500% increase in streaming data & analytics infrastructure. Furthermore, the current wave of supply chain disruption is forcing several companies to automate their data pipelines for better insights and visibility.

Why Invest in Data Pipeline Automation?

There can be different types of data pipelines like batch, real-time, cloud-native and open source. More broadly speaking, they can also be manual or automated.

Automated platforms facilitate the implementation of even the most intricate data management approaches. You no longer have to worry about internal deployments; instead, you have access to a seamless, end-to-end environment for data collection, cleansing, and processing.

You can diversify your data sources without worrying about data silos. An automated pipeline would simplify the process of maintaining and monitoring custom scripts to automate big data processes, cut operating costs, and connect all the technologies in your stack seamlessly. It is also less error-prone and provides a unified and centralized view of the entire process.

Some of the major benefits include:

Improved efficiency: Automating a company’s big data pipeline allows you to redirect up to 20% of engineering staff to more value-added activities. It also enables you to accelerate the implementation of big data projects, replace manual scripting with automated workflow management and data integration, reduce development time, eliminate coding errors, and provide faster business processing.

Consolidated view: Automation also provides a consolidated view into workflows and real-time data visualization. It maximizes the performance of service-level agreements and enables IT to identify and correct potential issues, monitor and quickly identify the root cause of errors and failures, streamline various processes and consolidate steps.

Superior BI/ Analytics: A fully automated data pipeline design enables your organization to extract data at its source, transform it into a usable format, and combine it with data from other sources, thereby increasing data management, business intelligence, data processing, and real-time insights.

Dark data profitability: Gartner defines black data as “information assets (that) organizations collect, process and store during regular business activities, but fail to use for other purposes.” 7 Utilizing business intelligence and customer insights empowers businesses to generate revenue from dark data by strategizing and optimizing internal processes.

Increased data mobility: Data can be moved quickly across applications and systems in real-time with a fully automated data pipeline. Data pipelines deliver key performance indicators and other metrics for marketing, sales, production, operations, and administration.

Sharper customer insights: Full automation of the data pipeline eliminates the need to code or format data manually, allowing transformations to all take place on-platform, enabling real-time analytics and granular insights. Integrating data from different sources produces better business results.

Compatibility with cloud-based architecture: 90% of advanced analytics and innovation will be carried out in the cloud by 2022. Cloud-native technologies give businesses the flexibility to grow and adapt to changing conditions quickly. Data pipelines will become even more critical as new technologies emerge on the edge.

 

The Future of Data Pipelines

As businesses place greater demands on their pipelines, their construction and deployment will become easier. While designing a data pipeline architecture today requires assembling separate tools for data integration, transformation, quality and governance, the industry is rapidly moving to a scenario where everything can be bundled into an integrated pipeline platform for corrective action without the need for intervention.

At Valiance, our best practices have helped scores of clients leverage the benefits of data pipelines and  realize tangible benefits. Get in touch with us today to find out how we can use our AI and Analytics expertise to design the ideal pipeline architecture for your business environment.

 

 

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