Data Governance Program

Data strategy assessment evaluates your current data practices, infrastructure, and goals to identify opportunities for improvement and alignment with your business objectives.

Data strategy refinement & execution focuses on fine-tuning your current data approach to better suit your shifting business priorities, followed by its effective deployment.

Generative AI readiness and Strategy prepares your organization to effectively adopt and leverage generative AI technologies.

Process for evaluating and selecting the optimal hardware, software, and structural frameworks that align with an organization’s data goals.

Data strategy consulting offers expert guidance to help organizations develop and implement a comprehensive data strategy aligned with their business objectives.

These services include strategic planning, risk assessment, and the integration of data-driven insights into organizational processes, aimed at building a data-centric culture at the executive level.

Inconsistent, inaccurate, or incomplete data can lead to poor decision-making. A governance program establishes standards and processes to improve data accuracy, consistency, and reliability.

Organizations are required to meet various industry regulations (e.g., GDPR, HIPAA). A governance program helps ensure that data practices comply with legal and regulatory requirements, minimizing the risk of penalties and audits.

Data often gets stored in isolated systems, limiting its accessibility. A data governance program promotes data integration and standardization, ensuring seamless access and collaboration across departments.

Poor data security can lead to breaches and data loss. Governance programs implement strict data handling, access controls, and monitoring, reducing the risk of unauthorized access and data breaches.

Without clear ownership, data management responsibilities can become blurred. Data governance assigns roles and accountability, clarifying who is responsible for the quality, security, and lifecycle of data.

A lack of reliable and governed data can slow down decision-making processes. A data governance program ensures that decision-makers have access to trusted and actionable data to make informed business decisions.

Without governance, data may not be fully utilized for insights. Data governance ensures that data is consistently and optimally used to derive maximum value from organizational assets.

Combining data from various sources and ensuring it can be used seamlessly across different systems is challenging. Data governance promotes standards and practices that facilitate data integration and system interoperability.

Managing data throughout its entire lifecycle—from creation and active use to archival and eventual disposal—can be complex. A data governance program provides guidelines for data retention policies, archival procedures, and secure disposal methods.

“Orases successfully built efficiencies into our prototype and delivered a high-quality platform.”

“They’re honorable, reputable, and easy to work with. They genuinely care about the outcome and want to do a good job.”

“Orases built a platform that’s boosted productivity by about 30%…”

“Not only do they want to succeed, they strive to produce functionally and visually unique software.”

Data governance is important because it enhances data quality, ensures regulatory compliance, reduces risks related to data breaches, and promotes efficient data management. It enables organizations to make informed decisions based on reliable data, ultimately leading to better business outcomes.

Data governance provides the policies and framework within which data management operates. While data management involves the technical execution of data-related tasks, data governance defines the rules and standards that guide those tasks.

By establishing clear standards, processes, and accountability for data management, data governance ensures that data is accurate, complete, and consistent. Regular data quality assessments and remediation procedures help maintain high-quality data.

Policies should be reviewed at least annually or whenever there are significant changes in regulations, business objectives, or technological advancements to ensure they remain relevant and effective.

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