Data is rapidly becoming the grease for mining’s wheels. Following the September 2016 edition of the MinExpo Trade Show in Las Vegas, Engineering and Mining Journal highlighted the advances in technology being showcased at this quadrennial event. Many of the advances discussed in the article were related to data, either in terms of:
- Data, connectivity and processing
- Analytics and Intelligence
- Robotics and Automation
In a previous blog, I indicated that I have moved across from the Mining Industry to Datum360, a company well set in the Oil and Gas and Process sectors. In mining, I was involved in the process of collection and exchange of day to day operational and transactional technical data to optimise mining production. Datum360 provides tools to manage existing structured information to ensure optimal use of an engineered asset. The most significant observation I can make from this move is that the Oil & Gas and Process Engineering managers have put a lot more thought into the management of data than the mining sector. This recent article from Paul Mitchell on LinkedIn indicated that 57% of a webcast audience surveyed, indicated that the Oil & Gas sector as the one sector that the mining industry can learn most from.
So, this blog, and the next few, will have a look at the benefits that Mining could be deriving from the structured and integrated approached used in Oil & Gas and the Process industries.
Process Industry folk have long recognised that if you are collecting the same sort of data on a repetitive basis, then consistency becomes very important. To be able to quantify this consistency, you need a standard against which to measure and this is achieved by Mechanical and Process Engineers using the concept of a Class. A Class is the definition of all the data types (or Attributes) normally associated with an object such as a pump or a valve, or even a cable harness. Classes can be grouped together into a Library. So, a Class Library (also known as a Reference Data Library or RDL) is simply a set of definitions of the data that would be expected to be collected for each object type in a project.
The Class Library is then used as the template to generate (and maintain) a database into which the data associated with each object in the project is accumulated. Because the attributes are now defined for each object, there are many benefits which accrue, simply from the existence of the Class standard, for instance:
- Equipment suppliers can be issued with the Class Library, or just those Classes relevant to the equipment they are supplying, as part of the Handover Specification. The procurement contract is then written to ensure that the required data is supplied in accordance with the Class definition. This ensures that suppliers are contractually bound to supply the required data for each component, in an acceptable format.
- Continuous visible measure of completeness of data is possible by object, supplier, project area, department and so on.
- The Class definition provides a clear understanding of the scope of data being collected, and with the right application, easy access to and management of the collected, structured data.
- The Class definition ensures the owner of the data is always be in control of the data, in terms of the supply and dissemination of that data, because they can always quantify the completeness of the dataset.
This approach to data management is widely accepted in the Process Industry sector and is now even being considered for a more industry-wide standardisation as part of the move toward BIM processes. We, here at Datum360, alongside other software vendors are actively involved in the CFIHOS initiative, where we are working alongside companies such as BP, EDF, Chevron, Shell, Total to establish a Handover Specification for Capital Facilities all built around the concept of a standardised Class Library approach.
So, could the Mining industry be benefiting from a Class Library approach to data collection? Most data collected in a day-to-day mining operation is certainly repetitive data, and rather than being involved in the collection of data for handover, the data collected is for exchange and sharing, so consistency should be even more important. Could Class Libraries help in this regard?
Consider the case of all mining data being collected against a standard set of attributes; If a borehole was always described in exactly the same way, then the exchange of that borehole data between departments, operating facilities, operational software and stakeholders would be so much more efficient. But that is not the true value of the Class approach, the true value is that the Class approach is that it tells you what is there, but also what is not there. An indicator reporting that only 42% of all expected or normal attributes were present in a particular borehole dataset immediately raises a flag. Without a Class approach, there is no mechanism to instantaneously gauge the completeness of a dataset.
Taking a broader view, demonstration of completeness by component parts of a project dataset may well be enough to engage/disengage the interest of an investor in competitive projects? And an Insurer? What conclusions could Legal affairs draw from quantifiable measures of the completeness of an operation’s data used in reports? And demonstrate able proof of Compliance in HSE matters …
Completeness, of course, only tells you, well… how complete the data is! To measure how good the data is, over and above the obvious QA/QC controls, requires additional thought. And that is the topic for my next blog!
If you would like to get in touch with me, you can contact me here, or just call our UK office on +44 3333 447 882
In the meantime, here's a few more blogs and news items from the Datum360 team that you might like:
|How do you support maturity tracking of completeness through a project?|
|Maersk Oil awards four year contract for Culzean Development|
|Datum360 Appoints Mining Industry Expert|