Blogs

Integrating Data Governance and Big Data with Business Processes

See an example of applying business processes for integrating data governance and big data from the oil and gas industry

Founder and Managing Partner, Information Asset, LLC

In their 2009 research note, “Warning: Don't Assume Your Business Processes Use Master Data – Synchronize Your Business Process And Master Data Strategies," Rob Karel, Clay Richardson, Connie Moore, and Charles Coit from Forrester Research stress the importance of integrating business processes and data governance. Organizations tend to create and govern their data and processes separately—but there is a lot of value in bringing them together. This article maps data governance policies to a simple process to monitor oil field sensor data. Table 1 provides a description of these milestones and activities for this process of managing oil field sensor data (see figure). Integrating Data Governance and Big Data with Business Processes A simple process enables monitoring of oil field sensor data. Table 1. Milestones and activities associated with monitoring oil field sensor data

Seq.

Milestone/Activity

Description

1. Sensors installed Oil and gas companies install sensors on facilities as well as the seabed to monitor production, the state of the facility, health and safety, and adherence to environmental regulations. The sensor-control systems typically support the OLE process control (OPC) protocol, a standard that specifies the communication of real-time plant data between supervisory control and data acquisition (SCADA) systems from different manufacturers.
1.1 Install sensors on facility The modern oil facility might have more than 30,000 sensors that capture numerous types of real-time big data from the exploration process such as flows, revolutions per minute, voltage, watts, temperature, and pressure.
1.2 Install sensors on seabed Companies may also install sensors on the seabed to monitor environmental conditions such as flow, temperature, and turbidity. Turbidity is a measurement of water quality based on the cloudiness of water caused by individual particles that might not be visible to the naked eye.
2. Production monitored Organizations need to monitor production of oil and gas. The oil company acting as the operator also calculates the production allocation to each owner of the facility.
2.1 Monitor production at facility Operators install sensors to monitor oil and gas production at each facility.
2.2 Create production dashboards Oil and gas companies also create dashboards to monitor energy production across facilities. Oil and gas companies create common operations centers so they can monitor production from a central location.
3. Equipment monitored Facilities use sensors to monitor equipment.
3.1 Monitor equipment on facility Operations departments monitor equipment such as pumps and valves on each rig. Typical questions include, “Given a brand of turbine, what is the expected time to failure when the equipment starts to vibrate in the manner now detected?”
3.2 Conduct preventive maintenance Operators conduct preventive maintenance if their predictive models indicate that a particular piece of equipment is likely to fail.
4. Environment monitored Oil and gas companies use sensors to monitor the environment.
4.1 Monitor environment on seabed around facility Environmental sensors may be in operation before, during, and after the operating life of the platform.
4.2 Monitor environmental pollution over time Companies need to answer such questions as, “Do the levels of salinity and turbidity in the water around the facility indicate an oil spill?”

Table 2 summarizes the key data governance policies associated with managing oil field sensor data. Table 2. Key data governance policies for oil field sensor data.

Seq.

Milestone/ Activity

Big Data Governance Policy

1.1 Install sensors on facility The data governance program should work with corporate security to ensure that the SCADA systems are properly secured against the possibility of cyberattacks. This discussion is not academic given recent cyberattacks on industrial infrastructure such as the Stuxnet worm.
2.2 Create production dashboards The data governance program needs to ensure consistency of the business terms within production reports. Key business terms include “well” in addition to associated child terms such as “well origin,” “well completion,” “wellbore,” and “wellbore completion.” The data governance program should leverage standard models such as the Professional Petroleum Data Management (PPDM) Association model for well data and definitions.
3.1 Monitor equipment on facility In the past, a rig might have had approximately 1,000 sensors, of which only about 10 fed databases that would be purged every two weeks due to capacity limitations. Today, oil and gas companies need to keep sensor data for a much longer period. For example, the health, safety, and environment (HSE) department may need to re-create a picture using three-month old information to explain why a particular decision was made in the field. The data governance program should leverage standard models such as ISO 15926 for systems and equipment on oil and gas production facilities, and associated definitions. The data governance program also needs to play a key role to determine how much information needs to be kept—and for how long—to satisfy both internal needs and regulatory compliance. Also, note that the rig may generate a lot of unstructured information such as video, pictures, and sound.
3.2 Conduct preventive maintenance If a specific type of equipment fails on one rig, the oil company must quickly pinpoint where else the same equipment has been deployed so that it can initiate the appropriate preventive maintenance. However, if the same asset is referred to by different names on different rigs, then it will be difficult to locate these assets in time. This is one reason why data governance is critical to ensure consistent naming for asset data.
4.1 Monitor environment on seabed around facility As discussed earlier, oil exploration and production activities generate a lot of structured and unstructured environmental information. This information needs to be maintained well after the lifetime of the facility itself to demonstrate adherence to environmental regulations. As a result, this information may need to be stored for 50 to 70 years, and up to 100 years in some cases. While storage is cheap, it is not free. The data governance program must establish retention schedules for specific types of information as well as the appropriate archiving policies to move information to cheaper storage, if possible.

Although this process is highly simplified here, you get a good illustration of how an organization can map data governance policies to key business terms.

[followbutton username='IBMdatamag' count='false' lang='en' theme='light']