Data
Data Platforms
Data Quality.
Frameworks and accelerators enabling enterprise-wide data to adhere to quality parameters like Integrity, Completeness, Uniqueness, and Consistency.
Governance.
Define and implement a data governance framework incorporating policies and processes to help enterprise discover, transform, and share internal and external data in conformance with compliance rules.
Data Quality.
Frameworks and accelerators enabling enterprise-wide data to adhere to quality parameters like Integrity, Completeness, Uniqueness, and Consistency.
Governance.
Define and implement a data governance framework incorporating policies and processes to help enterprise discover, transform, and share internal and external data in conformance with compliance rules.
Prakat’s Enterprise Master Data Management.
Enterprises today face challenges due to distributed and fragmented data across multiple systems and departments. Some of the main causes include onboarding new technology or systems as well as frequent large-scale mergers and acquisitions. Acquisitions have a deep impact on data and create data integration challenges that need to be mitigated. A Master Data Management strategy can add value and minimize risks due to changing data dynamics.
GOVERNANCE
Logical framework to manage the committees, policies, principles, and qualities to enable accurate and certified master data. This 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 should adhere to.
PROCESS
Defined processes and rules across the data lifecycle to effectively manage master data.
TECHNOLOGY
Centralized data repository, enablers and tool sets.
MEASURE
Enterprise and teams on your MDM goals and targets means evaluation based on data quality and continuous improvement.
Prakat’s Enterprise Master Data Management.
Enterprises today face challenges due to distributed and fragmented data across multiple systems and departments. Some of the main causes include onboarding new technology or systems as well as frequent large-scale mergers and acquisitions. Acquisitions have a deep impact on data and create data integration challenges that need to be mitigated. A Master Data Management strategy can add value and minimize risks due to changing data dynamics.
GOVERNANCE
Logical framework to manage the committees, policies, principles, and qualities to enable accurate and certified master data. This 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 should adhere to.
PROCESS
Defined processes and rules across the data lifecycle to effectively manage master data.
TECHNOLOGY
Centralized data repository, enablers and tool sets.
MEASURE
Enterprise and teams on your MDM goals and targets means evaluation based on data quality and continuous improvement.
Test Data.
The Complexity, Volume, and Dynamism of the data of current enterprise systems, quality and reliability are critical. Achieving these goals via testing is also a challenge. Testing, apart from following standard frameworks and methodologies, always begins with accurate test data. It needs to mirror real-time production scenarios, be it functional testing or non-functional testing.
This critical component of testing can be realized by having a robust test data management framework. Prakat Technologies has developed an innovative Test Data Management Framework.
Test Data.
The Complexity, Volume, and Dynamism of the data of current enterprise systems, quality and reliability are critical. Achieving these goals via testing is also a challenge. Testing, apart from following standard frameworks and methodologies, always begins with accurate test data. It needs to mirror real-time production scenarios, be it functional testing or non-functional testing.
This critical component of testing can be realized by having a robust test data management framework. Prakat Technologies has developed an innovative Test Data Management Framework.
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