GDD’s Technical Philosophy

The GDD Difference

Over a period of 30+ years, our staff have worked on a broad range of exploration, open pit and underground mining projects.

Over the same timeframe, we have been involved in the development and commissioning of many large-scale geological and mining database application systems.

Our dual qualifications, skills and experience set us apart in the data collection, management and analysis workspace.

GDD's vision is to help companies to:

·      Understand the true value of their data

·      Effectively collect, manage and analyse their data

·      And ultimately save time and money leading to increased profits and reduced project risk.

·      We encourage innovation, application and cooperation.

·      We share our knowledge with clients to help them better create and manage their data asset.

·      We focus our efforts on research and development to deliver the best data environments using the most current but rigorously tested technologies.

·      Our database and application systems are built to assist in every stage of the mining life cycle.

Very simply - We believe in doing things once and doing them right!

GDD’s Fundamental Data Philosophy

The Ultimate Goal

There are several key requirements for your master technical data if it is to provide the best return on investment made in collecting it. It should be –

·      Complete

·      Accurate

·      Relevant

·      Consistent

·      Timely

·      Accessible

·      Secure

·      Auditable

Over the years GDD have developed a number of core data management philosophies; these guide strongly the design and implementation of the data environments we build, and the database applications and utilities that GDD have created and are currently making available.

They seek to address the following questions relevant to the technical data universe!

In summary these are -

1 – Data Analysis is DATA Analysis!

·      Understand that all data analysis processes and applications, even the newly emerging technologies, rely on a complete, sound underlying master technical data set. …Which is why it’s called ‘data analysis’

2 – Know What Constitutes ‘Master Technical Data’ –

·      Understand what master technical data consists of; these days; it’s much more than the letters and numbers!

·      What aspects need to be considered to assemble all of the relevant data types and attributes useful to the project?

·      Think JORC ‘Materiality’

3 – Understand Your Technical Data’s Value And Importance

·      Understand the full and fundamental importance and significant value of your technical data

4 – Learn How To Implement Your Master Data Environment

·      Know how to structure and implement your master technical data / database

·      Understand and provide the data management characteristics and attributes, as dictated by the type of data being assembled

·      Understand the data collection, management and analysis processes that are required

5 - Segregate Your Master Technical Data

·      Keep your master technical data and database separate from and independent of all application and work folders and files

6 - Maintain That ‘Single Point Of Truth’

·      Ensure that the master or ‘gospel’ copy of each dataset type exists in one place only

7 - Make Data Access Simple And Intuitive

·      Make data access simple and intuitive to all those needing to make use of the data

·      Think JORC ‘Transparency’

8 - Data Validation And Integrity Checking

·      Ensure that complete and sound data validation processes are in place and active during data entry,

·      Provide data  integrity checking tools that further monitor and check the data once inside the database

·      Also ensure they can be rerun at any time to verify the validity and veracity of the data collection

9 – Standards And Procedures

·      Design, implement, document and enforce data management standards and procedures and related tools and guidelines

·      Maximise your project value, and reduce project risk inherent in, your technical data collection?

·      Determine how these should best be implemented to ensure they are used, and ‘encouraged’

·      To that end, establish some technical data governance guidelines; these are in effect standards and procedures… for your standards and procedures!

·      Think JORC ‘Competence’

10 – Capture And Retain Data Provenance

·      Carefully retain the data provenance (where it came from) and ensure it is auditable

·      Again, JORC ‘Transparency’