Document Parsing - Using generative AI to assist in Data Management

Dear Community,

We would like to provide an update on our ongoing efforts to address the challenges associated with unstructured data. We have developed this closely with AkerBP while concurrently identifying areas where such a tool could deliver significant business value. Throughout the process we have identified that this tool could be used in the following applications such as Material Master data management, Brownfield modification, and management of Asset Lifecycle Information. 

Significance of the Initiative:

The primary benefit is a reduction in the time spent on wrestling with tedious data extraction, allowing users to allocate more time to meaningful tasks. Our document data extraction tool not only saves time but also serves as a safeguard against errors often associated with manual input. Furthermore, we are looking to develop tooling and processes to mitigate and eliminate errors generated by the LLM.

Issues Addressed:

• Problem: Manual data work resembling an endless endeavor.
  Key Value Driver: Time savings 
• Problem: Errors and discrepancies resulting from a high volume of manual input
  Key Value Driver: Error minimization
• Problem: Applications hindered by sparse data
  Key Value Driver: Establishment of a robust data foundation for applications

Our focus is on streamlining processes to establish a singular, definitive digital version, eliminating the need for navigating through multiple iterations. This approach adds tangible business value, particularly in terms of time savings and error reduction, benefiting users who rely on accurate data for various applications. The implementation aims to provide enhanced efficiency and reliability in data handling.

For more information on the Data Management journey in Aker BP, feel free to watch the webinar here.