Without governance, data silos and inconsistencies make insights unreliable, decision-making risky, and AI training datasets vulnerable to sensitive information. Failing to establish robust governance can lead to data breaches, regulatory non-compliance, reputational damage, and financial loss. Now is the time to strengthen your data governance framework.
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At Arbisoft, we focus on improving data discoverability, strengthening access control, and streamlining lineage & auditing to establish a robust data governance mechanism.
Effective data governance and security begin with a thorough understanding of your data. We work with you to improve data discovery by identifying, locating, and cataloging data and AI assets, automatically generating documentation of data and AI assets, and managing metadata and relationships of data objects.
Data lineage and auditing provide the necessary transparency into the data's lifecycle. We provide tools that help your organization understand where data comes from, how it transforms, and where it goes. All this visibility is paramount for maintaining data quality, trust, and accountability.
To protect data against unauthorized access, use, disclosure, disruption, modification, or destruction, we make sure that users only access the data they are authorized to. Our hierarchical privilege model enables us to grant fine-grained access to data and AI assets. We also ensure that our data processing activities align with data protection regulations, such as GDPR, CCPA, HIPAA, and other industry-specific mandates.
As a Databricks consulting partner, we help organizations leverage Unity Catalog, a centralized data catalog that provides fine-grained access control for tabular and unstructured data in multiple formats on multiple platforms, along with governance for AI assets like machine learning models. It also includes built-in capabilities to discover data, track usage, capture lineage, and monitor data quality.
At Arbisoft, we ensure effective data governance and security through clear ownership, refined access policies, and appropriate classification of data. Our frameworks enforce data quality standards and implement security controls that allow the right stakeholders to access the right data with confidence. We also maintain comprehensive data lineage and auditing practices to help you track down any data security breaches.
Define clear roles, responsibilities, and policies for managing data and AI assets throughout their lifecycle.
Implement mechanisms to track the origin, transformations, and usage of data and AI assets to ensure transparency and accountability.
Enable comprehensive audit logging to track data access and platform events, facilitating compliance and security monitoring.
Establish and enforce standards for accuracy, consistency, completeness, and timeliness of data.
Use groups and service principals for granting permissions, avoiding direct grants to individual users for better manageability and security. Apply the principle of least privilege while granting access.
Use dynamic data masking and encryption principles and manage encryption keys on secret managers.
Review all data handling against compliance on regular intervals and use automated retention policies to archive or purge old data securely based on legal or regulatory needs.
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Arbisoft partnered with edX to revolutionize education by developing Open edX, an online MOOC platform accessible to everyone. With a team of 150 experts, Arbisoft contributed to the refinement of Studio features, extended the platform to integrate with third-party LMS, and provided timely support to edX learners. Today, over 20 million learners and 140 partners trust edX, a reliable and robust open-source platform.
Data governance improves data quality by enforcing consistency, accountability, validation, and transparency throughout the data lifecycle by ensuring data is accurate, complete, and reliable for BI and AI use cases.
Yes, defining standards, data ownership, access policies, and quality rules can take time initially. However, once governance is established, the benefits far outweigh the initial delays.
Mid-sized companies can build trust and confidence in data, often by implementing data governance. Such companies grow through multiple systems (CRMs, ERPs, spreadsheets, cloud apps). Implementing data governance ensures data accuracy, consistency, and integrity across those systems.
Yes, even if an organization is compliant with regulatory standards, it still needs a data governance framework. Compliance is the “destination,” whereas Data Governance is the “vehicle” that enables you to reach your destination.
Lack of data governance in AI poses many risks, such as loss of stakeholder trust in AI-driven decisions. Without strong governance, AI systems can produce biased, unreliable, or even non-compliant results. When training AI models on massive datasets, sensitive information can inadvertently become embedded in AI models, creating hidden vulnerabilities or inaccurate reasoning.
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