Data Strategy and Preparation

Assessing the client's existing data landscape, including sources, types, quality, governance, warehousing, and analytic capabilities. Identifying gaps that need to be addressed. 

Helping define new data sources (internal, external, Open Data, etc) that need to be tapped to support target AI use cases. Guidance on licensing, procurement, scraping etc. 

Designing and implementing robust pipelines and workflows to move data from sources into an integrated analytics infrastructure. This covers connectivity, ETL, and data schemas 

Developing data quality rules, metrics, and processes to cleanse data and address issues like missing values, outliers, duplicates etc. This preprocessing prepares data for AI modeling. 

For supervised machine learning, we can organize high-quality labelling of data to generate ground truth for model training. We handle labelling, methodology, tools, and human annotation.

We recommend data governance models covering privacy, ethics, security, access control, and regulatory compliance. This supports trustworthy and responsible AI.