Gartner defines hyperautomation as an approach “that enables organizations to quickly identify, examine, and automate as many processes as possible using technologies such as robotic process automation (RPA), low-code application platforms (LCAPs), artificial intelligence (AI), and virtual assistants.”
Applied to the data world, what stands out about hyperautomation? The optimal use of data.
Should the data pipeline be better automated or hyperautomated?
The data pipeline can be optimized from start to finish: from data preparation to prediction. The challenge for companies is to reinvent their processes in order to feed their data strategy.
Data pipeline automation is already a reality, at least in part. ETL, data marts, predictions based on Machine Learning: many companies have already taken advantage of these first steps towards autonomy.
That being said, it is possible to go even further to support your company’s data-driven strategy. That same data pipeline can be made hyper-intelligent, by applying automation at the process level. And that makes a big difference.
Apply hyper-intelligence to the data pipeline, from one end to the other.
Any data pipeline within an enterprise can be broken down into 5 distinct stages.
Each of these elements already have some form of automation.
The next step is to add a layer of intelligence, in order to automate the processes. In short, it is to hyperautomate data engineering.
In this webinar, Damien Mahuzier US General Manager of Indexima explains how this can be implemented, and why it represents the future for any data-driven company.
Key points of this webinar:
- Hyperautomation, or intelligent automation– what are the differences?
- How hyperautomation can be a powerful fuel for the data pipeline?
- Is an autonomous data pipeline the future for the data-driven enterprise?
Find out more in our white paper dedicated to this topic.
Duration: 30 minutes
Damien Mahuzier, Indexima VP of US Operations
[To download the recorded webinar, please complete the form.]