Data Management and Governance
Our team provides Data Management Development Processes, implementation, and enforcement of policies, procedures, and standards throughout the data lifecycle that establishes how data is defined, shared, stored, protected, retrieved, and purged. Strong data management enables an Enterprise to reduce its exposure to operational, financial, and reputational risks. Consistent data management methods can reduce the likelihood of operational errors, adverse business decisions, and financial loss.
Many governing bodies expect each Enterprise to have enterprise-wide data management policies, procedures, and standards. Data architecture should be integrated and provide scalable accessibility and effective utilization across the Enterprise as appropriate. Each Enterprise should establish data quality requirements so that data used for decision-making is relevant, accurate, complete, timely, and consistent. Data management practices should allow users to identify and access appropriate data for business, risk management, and compliance activities and functions. The regulatory expects the confidentiality, integrity, and availability of data to be consistent with sound business practices and regulatory requirements. Fundamental requirements in the following areas are detailed below:
We provide and help organizations to produce necessary Data Governance framework to control and support data used in decision making and risk management. Each Enterprise should establish a data strategy that supports organizational goals through data management, and effective policies, procedures, and standards to maintain the confidentiality, integrity, and availability of the Enterprise data throughout the data lifecycle. Policies, procedures, and standards should cover, at a minimum, data architecture, data quality, data security, and data usage. Policies and procedures should establish data requirements, controls for assessing and monitoring data,and assignment and coordination of individuals’ roles and responsibilities.
We provide and help organizations to produce necessary Data architecture which should define and support data requirements and formats, direct the integration of data, and align data investments with the data strategy. An Enterprise should establish data standardization requirements across the organization that are consistent with the data strategy and that reflect the needs of business and risk management functions. Adherence to those requirements, should be confirmed throughout the data lifecycle. We support our clients to maintain data or archive it pursuant to business, legal, and risk requirements to allow for recovery or evaluation of historical data outputs, whether stored in an Enterprise’s data center or in a hosted cloud environment.
An Enterprise should take steps designed to ensure that data is of an acceptable quality to meet business requirements and control function needs. Data should be sufficiently accurate, complete, timely, and consistent to enable the Enterprise to generate reliable results, such as reporting and risk modeling. An Enterprise should have comprehensive data quality management policies and procedures that include outlining roles and responsibilities regarding the collection, dissemination, and maintenance of data; both created and acquired, defining data quality requirements for created data, defining data quality checks for acquired data, and requiring a mechanism for assessing and verifying data quality, data quality metrics, and data conformance requirements.
Data must be protected against unauthorized and inappropriate use, modification, disclosure, and purging. Each Enterprise should have policies and procedures for monitoring and managing data security that are intended to ensure confidentiality, integrity, and appropriate availability of data. This includes the creation and maintenance of data classifications and controls consistent with the internal standards established in data governance, data architecture, and data quality management.
We as a service provider make sure that the implemented data management process enables relevant data to be used by an Enterprise to meet its Business needs, Manage business risks, Support risk management and compliance functions. Enterprise data, whether generated internally or acquired, should be available to business and risk functions to provide comprehensive, clear, and useful outputs.
Reporting or risk modeling processes should accurately aggregate data and be able to be reconciled and validated. Reliance on manual processes to manipulate data should be limited to reduce the possibility of human error. Each Enterprise should establish procedures intended to ensure that reports conveying the same data are consistent enterprise-wide. Sufficient controls should be implemented to appropriately protect the confidentiality of distributed information derived from data.