Big Data, Analytics &
Artificial Intelligence

Organize multiple sources of truth with data democratization.


What Happened Yesterday
What is Happening Right Now
What Will Happen Tomorrow

The purpose of our Big Data, Analytics and ML services is to plan, build, and implement Business Intelligence and Machine Learning infrastructure  on premises or in the AWS Cloud so you can have the answers to the above questions in real time and when you need it.  Architecting and deploying scripts and services to activate  data pipelines, implementing data engineering stack necessary to land reliable data in the Data Lake for Analytics, Insights and ML, all enable clients to make decisions that improve business outcomes.

Big Data Pipeline and Infrastructure

Sourcing data via AWS Kinesis, Apache Kafka or batch ingest from on-premises transaction systems or data warehouses like Oracle, Hadoop or AWS S3 buckets,  we cleanse and enrich the data, and then load the data into a Data Lake, which provides the foundation to build higher level intelligence. Data is the new oil, but before it can be useful, it has to be refined. Our Data Pipeline services include:

  • Data Import from data warehouses and/or transaction systems
  • Data Cleansing, Validity and Semantic Checks
  • Data Enrichment
  • Data Lake Infrastructure and APIs
  • Data Load Job Scheduling Scripts
  • APIs Gateways
  • SLA Reporting (Accuracy, Completeness, Timeliness)

Analytics and Insights

Get a clear understanding of the business operations from out of the box and custom reports using various visualization notebooks (Tableau, Jupyter, Quicksight..) with easy to understand visuals that provide valuable insights tied to business outcomes.

  • Visualizations Design and Presentation
  • Rollup and Drill Downs
  • Powerful and Intuitive Associations and Correlations
  • Ad-Hoc and On-the-Fly Reports and Dashboards
  • Report Publishing

Machine Learning and Artificial Intelligence

Sourcing data from reliable Data Lakes or Data Warehouses, we build custom code or use the power of AWS Sagemaker to test and train ML models that provide probabilistic predictions and can provide end points that can be consumed to enable human or machine decision making. Our approach is ROI Based to ensure that we are going after the right use cases and that we also consider the ability with which it can be operationalized-- either with human supervision or automatically.

  • Use Dimension Reduction, Clustering, Classification or Regression to Determine Appropriate Model
  • Identify/reduce Variables that cause noise in the data
  • Test and Train the models to get to an appropriate level on Precision and Recall and F1 score
  • Assign probabilities thresholds for inference accuracy
  • ‍ML Operationalization: Provide end-point for inference/prediction that can be consumed by other decsison making systems

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