ACTION PLAN
WHY DATA MANAGEMENT?
THE PROBLEM - UNSOPHISTICATED IT
Departmental silos are not new in oil and gas, but they are a huge barrier to master data management, and cause larger business problems like bad decision making, compliance issues, or inaccurate forecasting and risk analysis. Because many important processes rely on information that is located in departments spread across the company, information systems that don’t communicate can hinder important
collaboration: NRI could be one number in one department’s system, and a different number in each of three others.
Usually, companies are already gathering more than enough information to achieve the results they’re looking for. But because systems are set up based on departments rather than an asset’s lifecycle, data from each phase as an asset moves from discovery to production is locked in the department that covers only one piece of the puzzle. With this isolation, numbers can change in these different systems without being updated in others. This creates a nightmare for data quality management, because tracking down that information involves hoping that the game of telephone involved in calling the right people will gather accurate information. Even the most meticulous method of tracking down information via email, file shares, and thumb drives is a non-permanent form of knowledge transfer.
More likely than not, a disconnected set of systems will result in company-wide inaccuracies that can lead to larger issues. This is because by the time reports are generated upon which decisions are made, they are out of date, or if the interested party gathered that data from the wrong person or place, it could simply be wrong and misleading. In the technology world we call this concept multiple versions of the truth. It can be extremely dangerous, because it leads to unfounded conclusions that seem to be based on real information.
Entrance strives to make sure that everyone is on the same page, company-wide via Master Data Management that ensures the quality of the data upon which important decisions are based.
MASTER DATA MANAGEMENT IN 6 STEPS
Data management can seem like a headache, but putting it off for the long term can lead to immediate financial and legal risk. These six steps will help you develop an information-driven process that
automates compliance. People will only have to become involved when critical thinking and action are required, not to perform data entry.
STEP 1: REQUIREMENTS ASSESSMENT-DISCOVERY
First, a Requirements Assessment is performed as part of a Discovery Phase. Critical business needs are enumerated, and information sources, needs and parameters are defined for each need. When
considering the goals of data management, more than immediate pressing needs should be considered. This Discovery phase produces requirements for the information needed, as well as the current state of that data. Gaps are also defined where current systems will not be able to meet the goals at hand, and a road map is created to move through the next 6 steps most effectively.
STEP 2: DATA MAPPING
Once the type of information needed is defined, the location of essential quantitative information storage is determined. Then, in collaboration with stakeholders, systems of record are designated for each type of data. Systems of record are the authority on a particular piece of information, so that if there are multiple versions of that information in different locations across the organization, the system of record houses the authoritative copy. Finally, data is mapped to each destination system, so that when the information is updated in the system of record, that same information can be pushed out to any other storage location. This method of pushing the best data out is used to synchronize key information from disparate systems into a final data management solution.
STEP 3: DATA CLEANSING
During the process of driving all of the key information to the master data management system, it is important to evaluate whether the data being sent over was accurate. Data quality is extremely important when making high-value decisions based on that information, so it is critical to notice any inaccuracies prior to automation. Once errors are identified, inconsistencies are corrected so that the system starts with a clean data set. The check also serves as a validation of the systems of record. Many times, this process of master data management creation solves larger data quality problems across the organization than just those needed for the business case currently being addressed, and can positively impact
STEP 4: INTEGRATION
Integrating and linking various systems is essential because it creates the flow of information that keeps disparate systems in sync. Integration pushes the pieces of information from the systems of record out to the systems that depend on the information. During this phase decisions about how frequently and on what schedule data is synchronized are made and the system configured accordingly. Additionally, there are certain systems that may require a human in the loop before a data change is permitted. The master data management system can accommodate this case. The combination of the most up to date
information being pushed out from the system of record, ensures that all data storage locations have the same information, based on the master copy. This synchronization also enables end-users with the most up-to-date and accurate information, thereby providing one version of the truth.
STEP 5: INFORMATION MANAGEMENT PLANNING
With clean information in place, a plan for keeping information intake clean is created. Information management is automated by capturing and standardizing initial data sources. Prevention of unclean data creation is controlled by carefully managing data input sources with tools that facilitate the gathering and validation of data from humans and other systems. A go-forward workflow is then established to maintain an accurate inventory of data across your company. As new scenarios are noted, associated workflows can be captured and added to the system so that information quality control is always up to date.
STEP 6: CUSTOM CONFIGURATIONS
Finally, business owners are brought into the loop. After determining which users within the organization are associated with which sets of data, each set of employees is assigned to respective specific
information scenarios and objectives. Different asset teams are identified as well as an escalation path that defines the needed information at each level of decision making. Next, cases are defined for custom configuration needs like reporting, alerts, workflows etc.
These scenarios are specific for each client, but resemble the following: An alert needs to automatically be sent based on specific changes to specific data, which should give users enough time to solve the
problem; this is tempered by only surfacing information that requires action. A workflow needs to be created to escalate safety scenarios from the department they are in to various levels of management based on their urgency. Some, but not all situations require reports and alerts to take place.
By connecting business owners with the information they need in order to act quickly, the data management solution automatically provides valuable information on a pro-active basis, potentially before a problem would otherwise have been noted.
DATA MANAGEMENT CASE STUDY
The following is a case study describing how our data management implementation skills helped one client solve pressing compliance scenarios while also creating a better long term information
management platform for company growth.
THE PROBLEM
Our client is one of the leading producers in the Eagle Ford shale in South Texas. They produce 28,000 barrels of oil a day and operate on thousands of leases. They are also currently moving into a large exploratory position in the Southern Alberta Basin in northwest Montana.
As an E&P company, the client collects massive amounts of data about every well they drill, from the SPUD date to the perforation date. This information was siloed amongst departments, and those in the land department, for example, had no access to the production department’s data. As a result, multiple versions of the “truth” were held across departments, and no one was operating with the best information available.
THE SOLUTION
By standardizing information sources across multiple systems in multiple departments, Entrance documented the client’s specific information architecture related to compliance. In this case, the production department is the system of record for production volumes and well status. Drilling systems host the authoritative information on SPUD dates, casing dates, perforation dates, rig release dates, fracture dates, and other time and volume related data. The land department is responsible for
information about provisions, both common clauses and more complicated custom provisions that large land tracts are known for.
Each company and compliance scenario are different, and other types of information can also be brought into the solution. For example, the marketing department may supply price data or Division of Interest information that is important to determine royalty payout. If there’s a Royalty Escalation provision, it’s important to verify that any change is pushed into the Enterprise Resource Planning system and
implemented. With clean data management, information is correctly sourced and put into the right hands at the right time.
What began as a complicated set of unformatted information that had no formal interaction has become an interlocking system that provides only the highest quality information to those who need it. The speed of knowledge capture and information transfer has been noted as one of the system’s largest value-adds to the client’s organization.
