Why You Need a Fully Automated Data Pipeline
The four main reasons to implement a fully automated data pipeline are:
When you think about the core technologies that give companies a competitive edge, a fully automated data pipeline may not be the first thing that leaps to mind. But to unlock the full power of your data universe and turn it into business intelligence and real-time insights, you need to gain full control and visibility over your data at all its sources and destinations.
A data pipeline is at the heart of the critical data architecture that enables the seamless flow of information between systems and applications and can unlock better business intelligence and advanced analytics, improving organizational decision-making speed and capabilities.
According to Gartner, 87% of organizations have low BI and analytics maturity. Almost all organizations struggle to extract the full value they need from their data and to gain critical insights that can drive organizational efficiency and improved performance and profitability. A fully automated data pipeline allows your organization to extract data at the source, transform it into a usable form, and integrate it with other sources before shipping it to a data warehouse or lake for loading into business applications and analytics platforms. By automating these processes, you can establish better data management practices, improve business intelligence, and capture quality real-time insights. Tools for non-technical business users allows companies to become data-driven companies by democratizing the control and ownership of data across the entire organization.
As digitization speeds up, the amount of data companies collect increases, but most companies are still not close to fully utilizing their data and gaining deeper insights and real-time visibility from advanced analytics. Their data architecture is slowing them down and creating data bottlenecks and silos. They are losing data in the process rather than enriching it via integration and data mining. In older systems and architectures, costly engineering talent must be deployed to move it and prepare it for integration and analysis.
According to a survey by IDG, the amount of data companies collect increases by 63% each month. And yet, according to Gartner, 55% of data collected is “dark data.”
“The information assets organizations collect, process and store during regular business activities, but fail to use for other purposes, for example, analytics, business relationships, and direct monetizing.”
Turning dark data into business intelligence and customer insights can yield significant outcomes for companies, who can use the information to strategize, improve internal processes, and generate revenue. Advanced analytics can point to opportunities to act quickly on emerging trends, or enlarge and scale profitable products and services. But how do organizations manage the data that is streaming in from every direction? Analyzing data in real-time from many different sources requires a fully automated pipeline that can easily process and orchestrate multiple events.
For example, when one Xplenty client needed better tools to reach their target markets and improve customer satisfaction, they knew they needed to improve their data mining and business analytics strategy. By adopting Xplenty’s ETL solution they were able to:
Learn more about three real-life applications where companies were able to improve their BI and data mining capabilities by implementing Xplenty’s data integration platform and pipeline toolkit.
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After mining and analyzing your data, how do you get it back into the hands of the business users that need it? A fully automated data pipeline allows your business to function efficiently, moving data with ease across applications and systems, providing strategic BI and performance insights to leadership.
