Unlock the Value of Your Big Data Platform With an Automated Customer Data Pipeline

Big data platforms, such as Microsoft Azure, provide much of the power for business analytics. But as more data is generated from more sources — think the Internet of Things (IoT), edge computing, and a wide and diverse variety of devices and apps — it becomes more difficult to unlock the platform’s true value.

At Innovizant, we believe that automated data pipelines are the answer. A data pipeline moves data (typically customer data) through these complex analytical ecosystems, helping organizations attain results faster, more efficiently and more cost-effectively.

But extracting information from various sources and delivering it to a big data platform for analytical processing is an ongoing challenge for many companies.

Part of this is due to the sheer volume of data being generated. Ninety percent of the data that exists today was generated within the last two years, and that pace of growth shows no signs of slowing down. When we talk about “Big Data,” it isn’t an exaggeration!

Another challenge is that all this data comes in multiple formats. As SaaS adoption has increased, so has the amount of unstructured data being generated and collected. And for analytics in a big data platform, data must be converted into a common format.

But dealing with a combination of structured and unstructured data, with XML, flat files, text files, video and more can burn up your staff’s limited time. And the more time it takes, the more it costs.

The traditional practice of extract, transform, load (ETL) is resource-intensive and inflexible. It requires a team of engineers and data scientists to spend months developing workflows that are stagnant and lack efficiency in managing multiple, disparate data sources that, these days, are changing at the speed of business. It also requires layers of customized technology that focus on lowering storage costs by limiting data.