A unified view of your data is imperative. Without this, every link in the big data chain–from machine learning to artificial intelligence to information gathered from the Internet of Things–becomes less useful. Data warehouse and data mart technologies store, manage and transform your data. It is the fuel of the entire analytics process.

Adapt to evolving data sources by automating new data cleaning rules without slowing analytics efficiency. Continuous improvements in data warehouse technology give your systems agility and stability.

  • Integrated Data Mart for a Leading US Hospitality Client
  • Analytics Data Mart for a Middle East Telecom giant


ETL is a critical part of data warehousing. During this process, data is converted to a usable form, cleaned, and readied for analysis. Every business needs customized ETL that combines domain and technology expertise to precisely meet their needs.

  • Extract data from multiple sources, including the Internet of Things
  • Transform structured and unstructured data into useable forms
  • Clean data by applying advanced validating rules
  • Load transformed data into a target database
  • Choose the best data extraction method for your system
  • Select data formats and structures that align with your goals
  • Data Harmonization with Master Data Management for a Middle East CPG Client
  • Integrated Data Mart for a Leading US Hospitality Client


Provide a single point of reference for data within a company. Master Data Management (MDM) comprises the processes, governance, policies, standards, and tools that define and manage critical data.

  • Link all critical data to one master file
  • Facilitate computing in multiple system architectures, platforms, and applications
  • Create organization-wide data governance policies and procedures
  • Enhance information quality by complying with company data practices
  • Ensure cost-effective processes and timely project delivery
  • Data Quality Reporting Tool for a Russian Pharma Company


Each business must set its own standard for data quality. Before data can drive decisions, it must be complete, consistent, relevant, and accurate. Better data quality means more exact analytics and more reliable decisions.

  • Define data quality rules
  • Set up efficient data quality procedures
  • Continuously monitor, measure, and improve data quality
  • Data Harmonization with Master Data Management for a Middle East CPG Client
  • Sales Dashboard and Data Mart for a US Chemical Manufacturer


Data harmonization makes data compatible and comparable, even when it comes from a wide range of unrelated sources. Data that has been harmonized is easier to process and easier to use as a driver of actionable evaluations.

  • Capture the requirements of relevant business processes
  • Identify comparable data elements and attributes
  • Map core data elements to the data model
  • Minimize data differences and inconsistencies
  • Improve the data quality and compatibility
  • Make decisions from a single high-quality component
  • Integrated Data Mart for a Leading US Hospitality Client
  • Brand Equity Measurement Data Model for a US based Market Research Firm


Organize data into a standard structure and get a single source of truth for your entire company. Data contained within disparate databases is transformed into a common format and then systematically analyzed.

  • Define the structure of a robust CDM
  • Automate the CDM data ingestion process
  • Set up systematic analyses on the most current data
  • Ensure interoperability between platform components
  • Troubleshoot existing CDM challenges