Data Integration

Get your data AI-ready. Harness evolving data sources by automating data cleaning and harmonization to feed machine learning models.

Data Harmonization

Data harmonization makes data compatible and comparable, even when it comes from a wide range of unrelated sources. Ensure your data is ready for unsupervised learning, supervised learning and advanced analytics.
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A few project examples...

  • Identify relevant data to build strategic, scalable AI algorithms

  • Label data for effective unsupervised learning

  • Combine data and context to tailor AI solutions

  • Automate data ingestion and cleaning for near real time data feeds

A few project examples...

  • Capture the requirements of relevant business processes

  • Extract structured and unstructured data from multiple sources

  • Clean data by applying advanced validating rules

  • Automate the data transformation process with business rules

Extract, Transform and Load (ETL)

Customized ETL/ELT services combine domain and technology expertise to build high quality data assets. Determine the best data extraction methods for your system, and identify data formats and structure that align with business goals.

Master Data Management

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.

A few project examples...

  • 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 deliver

A few project examples...

  • Define data quality rules for specific applications

  • Set up efficient data quality procedures

  • Continuously monitor, measure, and improve data quality over time

Common Data Model (CDM)

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

Data Quality

Before data can drive decisions, it must be complete, consistent, relevant, and accurate. Better data quality means better AI models, more accurate analytics and more reliable decisions.

A few project examples...

  • 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

Interested in Flipvista's Data Integration?