Turn Data to Decisions
One Question at a Time

We connect to enterprise transactional systems using native connectors and perform comprehensive system topology discovery for accurate data source mapping. Then we extract source metadata to profile schema structures and relationship cardinality. After that we initialize data lineage to enable origin tracking for compliance, and identify API surfaces to map external data interfaces and authentication methods.

We begin by acquiring and ingesting data through an orchestrated ETL/ELT pipeline with dependency management. Our data connectors framework deploys adapters for diverse source integration, and we establish change data capture for incremental loads. We define batch, micro-batch, and streaming methodologies for data ingestion and implement a polyglot persistence strategy with a multi-modal storage architecture.

We execute systematized data sanitization processes through data cleansing operations. Our data wrangling pipelines transform raw data into analysis-ready formats, and we standardize values and scales through data normalization. We generate statistical distributions and visualizations via exploratory data analysis and deploy pattern recognition for automated data profiling and characterization.

We apply encoding, scaling, and normalization techniques through a feature transformation pipeline. We augment core data with derived and external attributes using data enrichment services. A centralized feature store is implemented for feature versioning, and we apply dimension reduction techniques like PCA/t-SNE for high-dimensional data. Our feature selection framework utilizes statistical and model-based feature importance.

We utilize an algorithm selection framework to match modeling techniques to business problems. We implement Bayesian search for hyperparameter optimization and design k-fold validation with stratification for our cross-validation strategy. Our ensemble methodology combines base models using stacking/boosting approaches, and we adapt pre-trained models to domain-specific tasks through a transfer learning pipeline.