Master data management can mean a variety of things depending on who you talk to. The root of master data management is ensuring data is entered correctly the first time and shared consistently across the organization. For example, the land department for an E&P company enters the land records one time and the records are pushed out across all other systems, ensuring accuracy. Many companies use systems to help manage master data. However, before taking on large amounts of expenses associated with purchasing a system, a company should take three simple steps to prepare for master data management.
The governance model establishes rules around identifying what a company’s master data is, as well as who owns, uses, maintains, and holds responsibility for accuracy of data. Robust data governance allows for clear lines of communication and accountability across all key data assets.
For upstream oil and gas companies, data related to the well life cycle process comes from a variety of departments and is often changed throughout the process. Without strong data governance, the well master data can quickly become duplicated or inconsistent, resulting in unreliable data.
With data governance established, companies can begin examining the data to ensure accuracy, cleanliness, and the ability to perform what is required of it.
Companies capture and produce enormous amounts of data. Data is often captured in different formats and some data may be unnecessary. It is important to identify resources who are knowledgeable across key data assets to comb through and determine what data is necessary. Defining critical data points enables the company to eliminate non-value adding data. Master data cleaning pays dividends in implementing master data management, future system implementations, and instilling confidence in users of key data.
Trenegy has worked with a wide range of clients with varying levels of maturity in master data management. Clients who prioritized data cleanup achieved higher confidence in analytics-based decision making and reduced the preparation time for system implementations.
By conducting performance process pilots, a company takes on a small-scale effort to manually test data collection and consumer delivery at each stage of the data lifecycle. Appropriately testing the process associated with initiating, maintaining, and retiring master data helps ensure the data will be compatible with future tools to help automate master data collection and delivery.
This is where businesses often put the cart before the horse, selecting a tool and resources before completing necessary process preparations. Selecting a tool too early burdens companies with immense rework due to compatibility issues between the master data, collection methods, and automation tool.
Master data management is necessary in today’s environment of exponentially growing data and complex system architecture. However, the effort to achieve master data management does not have to be as arduous as it seems. By defining data governance models, cleaning master data, and performing pilots, companies gain a solid foundation for effective master data management. The combination of these three components provides reliable data and supports streamlined processes with far fewer variables for future technology projects and system upgrades.
Trenegy has helped many organizations prepare for master data management and ultimately drive business value. Contact us for a free consultation at info@trenegy.com.
