With all the attention on analytics and flashy data visualization and dashboards, one area is easily forgotten is that which relates to Data Quality Assurance and Source Systems Integration Efficiencies which is the less sexy engine underneath BI, Analytics and glitzy Dashboards.

We refer to this area as Analytics and BI Enablement which consists of two (2) major areas, Data Quality Assessement & Assurance.

  • Data Virtualization: enable your company to connect, combine and publish data in all its formats from all data sources within the enterprise and beyond. Northbound consumers consisting of all BI, Analytics, Visualization, Archiving, Reporting, etc are able to take advantage of these virtual data sets through a robust Data Virtualization/Abstraction layer. Via this technology and approach one is able to create a unified and connected view of all underlying data sources. Systems/applications that need to consume this data only have to interact with the abstraction/data virtualization layer which is capable of accessing the entire enterprise data sources.
    Our Cloud based data abstraction layer a component of CloudFectiv's Converged Data Architecture enable enterprise data architects to easily create environments for all data consumers to access the data they need and trust in near real-time, in a format they understand, utilizing any data socialization tool/device they desire - Enterprise data availability anytime, anywhere. -- (Contact Us for a diagramatic representation of same). 


  • Data Quality Assurance: Historically this has consisted of data profiling tools which are used to determine data lineage and sample patterns which utilizes a tremendous amount of manual professional services effort to comb through myriads of data objects to look for non conformant patterns and anomalies.
    Our Cloud based continuous and automated data quality assessment solution that defines monitoring and transformation rules, enable data driven organizations to continuously monitor data comprehensively at the value level to identify and flag source systems data when certain quality attributes violate a threshold. This disruptive approach eliminates the time consuming and costly manual process of data quality assessment and offers close to real time detection of anomalies and security threats.