Data
Data Platforms
Data Quality.
Prakat designs frameworks and accelerators to enable enterprise-wide data quality parameter retention including Integrity, Completeness, Uniqueness, and Consistency.
Governance.
Defining and implementing a data governance framework, Prakat incorporates policies and processes to help enterprises discover, transform, and share internal and external data in conformance with compliance rules.
Data Quality.
Prakat designs frameworks and accelerators to enable enterprise-wide data quality parameter retention
including Integrity, Completeness, Uniqueness, and Consistency.
Governance.
Defining and implementing a data governance framework, Prakat incorporates policies and processes to
help enterprises discover, transform, and share internal and external data in conformance with
compliance rules.
Prakat’s Enterprise Master Data Management.
Enterprises are challenged by distributed and fragmented data across multiple systems and
departments. Causes can include onboarding new technologies and large-scale mergers and
acquisitions. Acquisitions have a deep impact on data and create data integration challenges that must be mitigated. Prakat’s Enterprise Master Data Management strategy adds value and minimizes risks due
to changing data dynamics.
GOVERNANCE
Logical framework manages the committees, policies, principles, and qualities to enable accurate and certified master data. Prakat’s governance process allows for the enterprise to advise and oversee a cross-functional team’s adoption of the MDM program blueprint.
ORGANIZATION
Getting the right stakeholder onto the MDM program, including master data owners, data stewards, and those participating in governance.
POLICY
The requirements, policies, and standards to which the MDM program adheres to.
PROCESS
Defined processes and rules across the data lifecycle to effectively manage master data.
TECHNOLOGY
Centralized data repository, enablers, and toolsets.
MEASURE
Enterprise and teams focus on MDM goals and targets with evaluation based on data quality and continuous improvement.
Prakat’s Enterprise Master Data Management.
Enterprises are challenged by distributed and fragmented data across multiple systems and departments. Causes can include onboarding new technologies and large-scale mergers and acquisitions. Acquisitions have a deep impact on data and create data integration challenges that must be mitigated. Prakat’s Enterprise Master Data Management strategy adds value and minimizes risks due to changing data dynamics.
GOVERNANCE
Logical framework manages the committees, policies, principles, and qualities to enable accurate and certified master data. Prakat’s governance process allows for the enterprise to advise and oversee a cross-functional team’s adoption of the MDM program blueprint.
ORGANIZATION
Getting the right stakeholder onto the MDM program, including master data owners, data stewards, and those participating in governance.
POLICY
The requirements, policies, and standards to which the MDM program adheres to.
PROCESS
Defined processes and rules across the data lifecycle to effectively manage master data.
TECHNOLOGY
Centralized data repository, enablers, and toolsets.
MEASURE
Enterprise and teams focus on MDM goals and targets with evaluation based on data quality and continuous improvement.
Test Data.
The Complexity, Volume, and Dynamism of data quality and reliability from a current enterprise system is critical to achieving goals via testing. Apart from following standard frameworks and methodologies, testing always begins with accurate test data. The data must mirror real-time production scenarios with functional testing or non-functional testing.
This critical component of testing is achieved with a robust test data management framework. Prakat developed an innovative Test Data Management Framework to ensure that these goals are met.
Test Data.
The Complexity, Volume, and Dynamism of data quality and reliability from a current enterprise system is critical to achieving goals via testing. Apart from following standard frameworks and methodologies, testing always begins with accurate test data. The data must mirror real-time production scenarios with functional testing or non-functional testing.
This critical component of testing is achieved with a robust test data management framework. Prakat developed an innovative Test Data Management Framework to ensure that these goals are met.
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