Data Strategy and Preparation

Data Audit

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

Data Sourcing

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.

Data Pipelines

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

Data Cleansing

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.

Data Labelling

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.

Data Governance

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

Data Platforms

We will Guide the assembling of data platforms like data lakes and warehouses for organizing, storing and sharing data at scale. This informs choices of tech stack and architecture

Feature Engineering

Identifying the optimal features to extract and transform raw data into formats consumable for different AI algorithms. This increases model accuracy.