Data Quality

We help the mining industry improve data quality, ensuring accurate, reliable, and actionable information that builds trust in data-driven decisions and drives operational excellence.

Why Data Quality Matters

The mining industry faces significant data quality challenges due to proliferating systems, historical lack of data quality control mechanisms during development, increasing system complexity, and fragmented or discontinued data knowledge across systems.

Analytics solutions are only successful if they deliver real value and generate a measurable return on investment.
Inconsistency
Incomplete Inaccurate
Loss of Trust
Costly Inefficiency
Missed Opportunities
High Operational Risks

How StratDA Helps

How StratDA Works

Automated Data Cleansing Engine

Data Quality Lab automatically conducts data profiling identifying outliers, inconsistencies, as well as data types. Data cleansing models are automatically tailored to target the specific issues while satisfying the requirements and standards.

Business-driven Quality Framework

Data Quality Lan provides pre-configured scenarios and requirement framework to accelerate the development of custom use cases.

Trusted Data Foundation for Analytics

Data Quality Lab provides end-to-end visibility and traceability of quality operations and creates data lineage for transparent data governance. The datasets are curated and validated with quality guarantees for analytics consumption.
Geology Datasets
Machine Data
Fleet Management
Safety Data
Environment Data

Analytics and AI Use Cases

A structured approach to identifying, evaluating, prioritising, and managing analytics and AI use cases—ensuring alignment with business goals and the consistent delivery of measurable value across mining operations.

Report Design for Small Miners

Define the vision for business improvement and target data capability maturity aligned with mining goals—tailored to the business and operational context, and providing a benchmark to guide future planning and investment.

Proof of Concept

A lightweight, low-risk approach to rapidly test and validate analytics or AI ideas—demonstrating value, providing base scripts to accelerate development, and informing go/no-go decisions before full-scale implementation.

Data Contracts

A clear, agreed-upon specification between data producers and consumers—defining the structure, quality, ownership, and delivery expectations to ensure reliable, consistent, and trusted data for downstream use.

A clear, prioritised pipeline of high-impact analytics and AI initiatives

Strong alignment between technical solutions and business needs

Faster validation and value realisation from targeted use cases

Faster, more confident decisions at all levels

Improved operational focus through role-relevant insights

Greater trust in data through consistency and clarity

Reduced time spent searching for critical information

Clear evidence of potential value and feasibility

Refined requirements for smoother implementation

Reduced risk and cost before full-scale rollout

Faster stakeholder buy-in through tangible results

Improved data quality, reliability and consistency

Clear accountability between producers and consumers

Reduced errors and rework in downstream processes

Faster troubleshooting and issue resolution

Ready to Transform Your Business?