FlowML.com

The Data Centric approach is different than traditional activity centric processing as with the right data we can power any IT enabled service. The problem is no longer to do the work. With the right data we can “deploy” these data sets onto systems using robotics or other automation approaches. With execution now always being automatable, the challenge is therefore assembling and curating the right data enabling that automation. By having the process being to assemble the data sets, we can also do an additional QA on the data before deploying them to production. Looping out people from execution also helps address the estimated 95% of processing errors which is down to human errors. Instead of having people involved in the execution, we have them involved in the curation and data creation. By seeing the work of people as curating data we can tap into rich toolsets available for “big data” platforms. Not only are these platforms more scalable, maintainable, cheaper and faster, they also typically supports Machine Learning natively.