Compromising precission is the final decision to made or not to be made. To support the decision making, we need to understand the quality of the data. I prefer to call it as information, to emphasize the factor of subjectiveness/dependence. First, we need to understand the completeness, includes context, metadata and inherit information. Next, all the information need to be splitted into units, i.e. by hole or by mapping program/study. Then each unit should be given a rank based on it quailty. We need to understand the criterias to classify the rank. By having this, then we understand the quailty information that we dealing with. The worst case, may need to redo everything. Ensure everthing done correctly from the start is the best.

I have been responsible for implementing data systems at a couple of organizations. Convincing the organizations to spend the money on up-front data clean up was very difficult. Everyone wanted to deploy the latest AI tools but these did not gain any efficiencies if the primary data did not undergo comprehensive quality control

We cannot move from the basics. Conduct basic data cleaning, remove obvious outliers, typos using basic software. Thereafter you can use more sophisticated software and algorithms.Software will give you what you input into it. Having a good understanding of the type of data being analysed is key. Software cannot replace subject knowledge and understanding.The teo compliment each other.

La geología no es compleja, la interpretación de algunos geólogos es lo que la hace compleja. Si el yacimiento no funciona con sentido lógico, el yacimiento interpretado no existe, hay que interpreta con sentido común. No hay que olvidar que no se puede describir la geología que no se conoce

Une analyse des données s'impose, un filtrage suivi d'un tri et d'une classification. Faire la part entre les données des forages, des données de cartographie, de géochimie et de la géophysique. Attribuer les tâches d'analyse par sections dans l'équipe des Géologues, qui après devraient faire un rapport de l'activité. Les données ainsi analysées peuvent êtres remis au gestionnaire de base des données pour incorporation dans la base principale.

For Complex geological data first step is to analyse the data manually before fitting it into any of the automated high-class software. Software cannot replace the need of Basic analysis based on geological data/model available. Once the geological input are correct precision can be arrived with the help of software. Please remember precise input is important for good output from any software.

Streamlining the analysis of complex geological data while maintaining precision and quality can be achieved through advanced software tools, automation of data transformation, centralized data storage, enhanced visualization techniques, and the integration of AI and machine learning. These strategies not only improve efficiency but also ensure accurate data analysis, enabling better decision-making in geological studies. Regular training and updates for the team further enhance proficiency with the latest technologies, ensuring that data analysis remains both streamlined and precise.

To streamline the analysis of complex geological data without compromising precision, it's essential to use advanced software like ArcGIS and other Software for efficient 3D modeling and data visualization. Integrating various datasets and applying automated workflows can speed up interpretation. Employing machine learning for pattern recognition and data filtering enhances analysis while maintaining data quality, allowing for accurate and efficient decision-making.

The first step to handle any intricate and convoluted natural data is simplification of the complex database, and geological data is no exception. No matter how complex is the dataset, if the data is accurate, meaningful interpretations can be made. A broad general classification of the data is a good starting point. For example, it's not uncommon that field geologists differentiate lithologies based on minute variations, and ultimately, they end up with unmanageable number of lithounits for modelling. Before diving directly into modelling/interpretation with the raw data, grouping of lithounits based on commonality is a good option in such cases. Likewise, for structural data knowing the general tendency is perhaps the most important part.

To streamline complex geological data analysis without compromising precision, I’d use automated data processing tools, GIS for visualization, and geostatistical methods like kriging. By filtering data, prioritizing key variables, and using modular workflows, I ensure efficient analysis while maintaining data accuracy.