The pandemic has forced many businesses to accelerate their digital transformation journey. Consequently, many services are now delivered almost entirely online via automated or semi-automated processes.
Companies are using robotic process (RPA) automation technology to improve business performance, reduce cost and help manage resource issues. These are all examples of ‘Intelligent Automation’.
At embracent we are seeing this firsthand as we automate key processes for our customers – allowing them to free up time to focus on more value-adding activities and drive toward process excellence.
We are also seeing that the success of these initiatives is wholly dependent on high-quality enterprise data and insight:
- Existing Data – feeding automated processes that must be fit for purpose to avoid process exceptions and human intervention
- New Data – created by automated processes, that can be mined, analysed, used to deliver true business insight
So how does a business approach this complex data challenge when they are focused on a fast-faced digital transformation?
The answer is to develop a foundational data strategy that:
- Identifies the critical data objects feeding your processes
- Prioritises data quality issues that need to be fixed
- Maps out the system data flows that must support your processes
- Defines a data insight capability needed to support intelligent automation
- Identifies the skills and competencies needed to both govern and exploit process data
- Sets out a realistic and achievable roadmap to drive through the data led change needed to sustain process excellence
Here at embracent, we have many proven tools and accelerators that can solve these data challenges without over-complicating them or costing a small fortune.