August 2020

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Digital: Why an effective master data strategy is key to digital transformation in oil and gas

Digital transformation is the “buzzword du jour” in every industry, but nowhere is it more prevalent than in the oil and gas industry.

Roberts, R., Opportune LLP

Digital transformation is the “buzzword du jour” in every industry, but nowhere is it more prevalent than in the oil and gas industry. Long an asset-heavy manual process industry, oil and gas is ready for a change—a big shift to digital. However, what happened to that promise of “the digital oilfield” in the 2000s? Shouldn’t that have been our digital transformation story? It likely should have, but it fell far short, in large part, due to one key element: data. Data is key to any digital transformation, and the foundation of all data is master data; the information about materials and products, customers and vendors are the bedrock of a digital framework. Oil and gas companies need a strategy to manage that master data before they begin building their digital transformation dreams upon it.

What is master data?

Master data is the core data that provides meaning or context to transactions and analytics. It can certainly include data that are defined outside an organization, either by industry organizations or other centralized entities, such as governments, the International Organization for Standardization (ISO) or the United Nations. In the first case, consider your suppliers/vendors, employees, customers, materials/products and organizational data (e.g., companies, business units, plants, consolidating entities). In the latter cases, reference data such as country names, state/provincial names and codes, currencies, UN location codes and units of measure are all examples.

Some master data relates to other types of master data. Regarding materials and products from within a company, one attribute may be its classification as determined by the United Nations Standard Product and Services Code (UNSPSC). Master data such as this is essential for companies to exchange information as customers and suppliers. Clearly, the geographical information that is standardized by governments and international standards organizations is critical to determining the addresses of suppliers and customers (this also helps determine duplicates.)

The key elements of an effective master data strategy

First and foremost, support (and enforcement) must have full management approval at the enterprise level. Support from business units is also needed, but is secondary to support from the top of the organization. Enterprise support is also vital to the second element, the elimination of data silos, which also allows for a full data inventory. Master data and its processes are often locked within business unit silos, which usually are system-driven (e.g., a global system for customer master data is SAP, but one or more business units have Salesforce CRM with its own customer master data that does not tie to SAP).

By breaking down the walls hiding pockets of data, a full data inventory can be completed so that rules can be developed and applied. These rules may govern data field requirements, special coding or the definition of a duplicate record. In many cases, the enforcement of these rules can be handled by a centralized master data management or governance tool. Such a tool would capture all required master data and publish to the various systems that require it, giving all such systems a common master data record.

The next element of the master data strategy is data rule definition. This is usually mandated by a management or governance system, but is also key to process changes absent any system. Data rule definition generally includes naming conventions, common abbreviations, and punctuation and rules for determining data duplication. In many cases, master data within the same silo structure will have significant inconsistencies (i.e., upper and lowercase used in some records, and all uppercase in others).

A primary example most companies can point to is how the telecommunications company AT&T is set up. Generally, depending on the age of the system, you will likely encounter the following: “AT&T,” “AT and T,” “A.T. & T.,” “American Telephone & Telegraph,” and possibly others. The same holds true for companies that have merged, been acquired or simply changed names. These can often fall into the “duplicate” category, but are harder to assess. Defining consistent data entry rules can resolve these issues.

Finally, consider the evaluation and cleansing of existing master data. While this is a daunting task, it must be undertaken. At the same time, any new data coming into the master data ecosystem would follow the same rules and data duplication assessment. This could be an instance where taking data sets from one master data category for evaluation and cleansing may be the most sensible alternative, rather than addressing all data sets simultaneously.

Master data: Downstream applicability

Depending on which sector of the oil and gas industry you operate in, the strategy may require a slightly different approach. For instance, a fully integrated company that crosses multiple sectors will identify some classes of master data as more important than others (i.e., counterparty data—representing customers, vendors and trading partners—generally carries more weight than other master data). Looking at a picture of the master data landscape, the image is closest to that of a cone, with upstream at the top of the cone and downstream at the broad base of the cone.

The downstream oil and gas sector has the largest inventory of master data because its processes are spread among so many different systems and locations—from raw inputs at refineries and chemical plants, to refining and storage, to distribution (both primary and secondary) and finally to retail and other industrial customer endpoints. This downstream data set includes everything from various inventory locations and materials types at a refinery to intermediate and output materials transformed by the refining processes. Finally, it represents all different distribution types (again) coming to the racks and the industrial endpoints.

While the data inventory may look very similar to what has been discussed here, the downstream sector also has multiple systems requiring representation of the same data elements. For instance, a refinery’s processing systems require material records for different types of crude oil, crude oil equivalents and other inputs to the refining process. These material records also exist in an enterprise resource planning (ERP) system—or a separate inventory control system—but have either a different code or consist of different granularity (e.g., “generic crude” vs. “WTI” or “Basrah Light”). This immediately causes a mismatch. Similarly, inventory or processing locations may have different usage for the same master data.

In the processing or planning systems of a refinery, this location may only represent a holding area, while an ERP system may pass this information to accounting for tax determination. The master data strategy, therefore, must consider how the attributes of the master data will also be used in processes throughout the entire enterprise.

Formalizing the strategy across the enterprise to build the master data foundation

Enterprise means enterprise—all of it. You cannot have a master data strategy without involving the whole organization. Many organizations will try to experiment with their strategy by rolling it out in one region or in one business unit at a time. Doing so immediately breaks down the elements that have been laid out here in the fundamentals of an effective strategy. It also reinforces the hazards of the data silos previously mentioned. To be effective, a master data strategy should be a “Big Bang” across all business units and regions. If a need for experimentation exists, select a single data entity (perhaps materials) and roll out the strategy globally. Doing so will allow the organization to adjust rules, processes and workflows, and weigh the impact of building a foundation with a common master data strategy.

A master data strategy to begin an organization’s digital transformation

Deploying an effective master data strategy across the enterprise is a good start, but it is not the sole basis of digital transformation. While we hinted at it in the master data examples, we did not address the need for solid integration between systems and processes. Integration, along with sound data practices, is what makes digital transformation work. Without that integration, the elements of robotic process automation (RPA), machine learning and artificial intelligence (AI) cannot be effectively applied to the oil and gas industry. HP

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