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Data Dilemma: Unraveling the Challenges and Downsides of Data in Oil and Gas

Article Written By Kate Stevenson, Novus Consulting

Master data refers to the essential data elements that provide a consistent and unified view of key entities within an organization, such as customers, products, suppliers, and employees. Within Energy, master data would refer to the ‘Well’, the ‘Facility’, the ‘Store’ or an ‘Asset’ and the associated critical data that goes with it to identify ‘what it is’. While master data management (MDM) is crucial for maintaining data integrity and supporting business processes, it goes ignored within the mid-large oil and gas industries. Most think that implementing a robust ERP will solve all master data problems. They are sorely mistaken when after investing millions of dollars in a new accounting system, the data is still bad.

Here are a few reasons why master data sliutions fail:
  1. Data Silos and Fragmentation: Energy companies often have multiple departments, divisions, and geographically dispersed units, each with their own interpretations of data, processes and sometimes systems. This decentralized nature can lead to the creation of data silos, where master data gets fragmented and becomes inconsistent across different parts of the organization. This fragmentation makes it challenging to obtain a holistic view of data and impedes effective decision-making and collaboration.
  2. Data Quality and Accuracy Issues: Maintaining high-quality master data is crucial for reliable business operations and analytics. However, in Oil and Gas with numerous data entry points and manual processes, ensuring data accuracy and consistency becomes increasingly difficult. Data duplication, outdated records, incomplete information, and inconsistent formats are common issues that arise when master data is managed haphazardly. Poor data quality can lead to inefficient operations, erroneous reporting, and costly errors business decisions.
  3. Lack of Governance and Ownership: Large corporations often struggle with establishing clear data governance policies and assigning ownership responsibilities for master data. Without defined processes and accountable individuals, master data management becomes a fragmented and ad-hoc effort. The absence of governance structures leads to inconsistent data standards, limited data stewardship, and difficulties in maintaining data integrity. It also hampers the ability to enforce data quality controls and implement changes or updates to master data in a systematic manner.
  4. Scalability and Complexity: The scale and complexity of master data increase exponentially with the size of the organization. As corporations expand their operations, introduce new product lines, acquire or merge with other companies, or enter new markets, managing the corresponding master data becomes a significant challenge. Scaling up MDM systems, integrating disparate data sources, and harmonizing data across different business units can be complex and resource-intensive endeavors.
  5. Integration Challenges: Integrating master data across various enterprise systems is crucial for achieving a unified view of the organization. However, in many companies, the integration of diverse applications and platforms, each with its own data structure and requirements, can be intricate and time-consuming. Legacy systems, lack of IT development support, custom applications, and different data formats further complicate the integration process, making it difficult to establish seamless data flow and synchronization.
To address these challenges, Energy companies need to invest in robust MDM strategies, comprehensive data governance frameworks, and modern data management technliogies. By centralizing and standardizing master data, implementing data quality controls, fostering a culture of data stewardship, and leveraging advanced data integration sliutions, organizations can overcome the drawbacks of master data and unlock its full potential for informed decision-making, operational efficiency, and business growth.