Digital transformation is the “buzzword du jour” in every industry, but it’s none 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. But 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 to one key element—data. Data is key to any digital transformation, but the foundation of all data is master data. The information about materials and products, customers, and vendors are the bedrock of a digital framework. However, 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 gives 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, ISO, or The United Nations).

In the first case, think about 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 of this 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 with each other as customers and suppliers. Clearly, the geographical information that’s standardized by governments and international standards organizations is critical to determining the addresses of suppliers and customers (this also helps to determine duplicates.)

What Are The Key Elements Of An Effective Master Data Strategy?

First and foremost, support (and enforcement) needs to have full management approval at the enterprise level. Support from business units is also needed, but it’s 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. Oftentimes, master data and its processes are locked within business unit silos. These are often system-driven (e.g., a global system for customer master data in SAP, but one or more business units have Salesforce customer relationship management (CRM) with its own customer master data that doesn’t 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 it 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 it’s 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, all uppercase in others).

The number one 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 have 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 these are harder to assess. Defining consistent data entry rules can resolve these issues.

Finally, there comes the evaluation and cleansing of existing master data. While this is a daunting task it is one that must be done. 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 attacking all data sets simultaneously.

How Can Formalizing The Strategy Across The Enterprise 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 we laid out 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 there’s a need for experimentation, 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.

Is A Master Data Strategy All I Need To Start My Organization’s Digital Transformation?

Deploying an effective master data strategy across the enterprise is a good start, but it’s not the sole basis of digital transformation. While we hinted at it in the master data examples, we didn’t 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.

In a future article, we’ll discuss the levels of integration that are practical today and those we must strive for tomorrow to complete the oil and gas digital transformation journey.