We offer an expert guidance and practical strategies to ensure your organisation's data is secure, compliant, and leveraged effectively to drive business success. We provide a holistic approach, ensuring that from the ground up, your data projects are set on a path of excellence. We will help you align your projects with best practices and to future-proof them against changing regulations and industry standards.
In the world of data engineering, the architecture and foundation of your data systems need to be both robust and flexible. Without proper data governance strategies in place, you risk building on a foundation that may not stand the test of time or regulatory scrutiny.
In data analysis, the results are only as reliable as the data that feeds them. Without governance, you might be making crucial business decisions based on faulty or non-compliant data.
As a leading data and technology consultancy, we recognise the paramount importance of data governance in ensuring both data quality and compliance. Here we provide a list of some tools and methods that can be used to ensure data access control, which is a fundamental security tool that enables you restrict access based on a set of policies.
These are the two main components of data access control that verify the user identity and determine the level of access and actions that each user has to the data. Authentication can be done through a multifactor authentication mechanism, such as passwords, tokens, biometrics, etc. Authorization can be based on specified policies, such as data classification, role assignments, or rules.
These are software tools that help you manage the access rights of users to data resources across multiple clouds and environments. They can help you define the roles, responsibilities, policies, and standards for data collection, processing, storage, and sharing. They can also help you monitor and audit the access activities and compliance of users.
These are models that apply different approaches to data access control based on the level of restriction and discretion. There are four main models for data access control: discretionary access control (DAC), mandatory access control (MAC), role-based access control (RBAC), and rule-based access control (RBAC or RB-RBAC). Each model has its own advantages and disadvantages depending on the security requirement, infrastructure, etc.
These are tools that help you manage the quality, security, and availability of data in an organization. They can help you implement data protection laws, such as the General Data Protection Regulation (GDPR), that aim to give data subjects more control over their data and to hold data controllers and processors accountable for their actions. They can also help you use artificial intelligence (AI) tools in data analysis in a responsible and ethical manner.