Organizations utilize many systems to store, access and retrieve information. To do this, end users search these systems by utilizing system attributes which are populated with critical metadata (e.g., document origin, classification.) Metadata describes the who, what, where and how of the stored information.
System attribute requirements differ, with the accuracy and reliance of the populated attribute being of utmost importance to allow for identification of information to perform activities.
Unfortunately, system attribution is not always mapped correctly to the original source information with accurate metadata, which can lead to utilizing and sharing incorrect information.
Organizations also face the challenge of migrating information from and to systems, or implementation of new numbering schemas, which will require substantial mapping and cleansing activities to reflect source information correctly. When not managed and implemented correctly, this can contribute toward inaccurately representing information.
When system attribution is missing or inaccurately mapped to system attribution, this contributes toward:
There is also the additional challenge of migrating information from and to systems, where organisations will need to manage:
Data wrangling services
Data cleansing and mapping technologies need to accurately detect, correct, eliminate and transform metadata to align with source information and system attribution requirements.
Data wrangling incorporates adaptable technologies that enable the cleansing and mapping of metadata extracted from systems and documents, allowing for alignment to system attribution and global taxonomies. This includes detection and alignment inconsistencies which may have been originally caused by user entry errors or differing definitions of similar entities.
With data wrangling services, organizations are able to consider mapping, retaining and managing metadata such as:
After cleansing and mapping activities, organizations can ensure that data is consistently applied and compatible with system requirements. The data will also be transformed into a system compatible load sheet.
Data wrangling approach
Data wrangling services coupled with vision analysis, deep learning, machine learning technologies and domain SME engineering IMDC knowledge enables “en masse” cleansing and mapping of information and its associated data.
Data wrangling processes will be adaptive and configurable to organizational requirements. This enables seamless data transformations and standardization activities, such as:
The integrity of data would not be compromised during cleansing and mapping activities, which would also be closely monitored with key metrics.
Mapping, cleansing, and transformation of data is a pre-requisite for accurate identification of critical data. Data wrangling ensures that organizations work and share true source data while reducing the likelihood of risk to personnel, incidents, or downtime and respective cost impacts.
Consulting Practice of Energy, Natural Resources, Utilities and Engineering & Construction
Janine Murray is an IM Consultant with over 15 years of experience in the O&G industry. She has extensive FE/Operations and Major Capital Project (MCP) Information Management experience. She also possesses deep experience with IM brownfield modifications, greenfield enhancements, MCP joint ventures, Closeout, and MCP handover to Operations. Additionally, she is experienced with document cleansing and data extraction techniques for digitizing O&G legacy assets.
She can be reached at: email@example.com