The field of data governance is an emerging discipline with an evolving definition. It's primary focus is on the processes of formally managing data throughout the enterprise. It embodies a convergence of business process, data quality and risk management policies as they pertain to data handling. Formal data governance allows organizations to exercise control over data management processes used by data stewards and custodians.
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Data governance is a collaborative process that ensures important data assets are properly managed throughout the enterprise. It makes certain that data can be trusted and people can be held accountable for any adverse events that may happen as a result of low data quality. The primary goal of data governance is to improve efficiency of the enterprise by preventing so called localized thinking, wherein developers focus on resolving immediate issues faced by a business unit and do not take into account the global impact of technical decisions. A data governance process is often evolutionary for a company, altering the IT organization's way of thinking and changing the way information is processed and used by a line of business. |
Data governance encompasses people, processes, and the technology required to create a consistent approach for handling an organization's data. It is a collaborative process that naturally fits within the Collaborative Computing paradigm. If enterprise data assets are considered a product, then governance is way to optimize an organization's data factory operations to ensure product quality.
Data Governance Goals
To manage financial and operational risk, organizations must control data usage and ensure effective governance by putting in place consistent methods, best practices and technologies that support the human decision-making process. Safeguarding corporate information allows organizations to satisfy auditors and address regulatory compliance as well as retain customers and drive new business opportunities. Data governance initiatives intend to acheive several goals:
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Formalizing a data governance process often requires the use of supporting technologies that facilitate Structured Data Management, Profiling, Data Mapping and Transformation, Change Management and Monitoring. Data security and the ability to establish classification of information are also important capabilities that help identify data retention and destruction policies.
Key Platform Features
StreamScape Technologies offers a comprehensive solution and services to support data governance initiatives. The Service Application Engine™ provides a unified platform for data management. Our data governance framework offers a rich set of features that allow an infrastructure to evolve in step with your company's data governance strategy. No complex, monolithic product stack. The platform provides a light-weight, easy to use infrastructure that can be used to populate gold copy databases, affect data virtualization and abstraction, facilitate data distribution, synchronization and semantic mapping.
Key features include:
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