A data fabric is a design framework for creating flexible and reusable data pipelines, services, and semantics. It uses data integration, active metadata, knowledge graphs, profiling, machine learning, and data cataloging. Data Fabric changes the main approach to data management, which is “build to suit” for data and use cases and replaces it with “observe and leverage”.

Data governance is a framework of rules and guidelines for how everyone should handle and use information in a company to keep it accurate, secure, and useful.

Why it matters: Strong governance ensures data is secure, compliant, and used responsibly, reducing risk while enabling business value.

Data integration involves practices, techniques, and tools to ensure consistent access and delivery of data across different areas and structures within a company. It aims to meet the data needs of all applications and business processes efficiently.

Why it matters: Seamless data integration ensures consistency, reduces duplication, and supports accurate, organization-wide insights.

Data lineage refers to the lifecycle and journey of data from origin to destination. From creation to how it’s been edited, transformed and used. Data lineage is critical to know how data can and should be used (compliance), how it was generated and how trustworthy it is. This becomes particularly important when considering data for insights or for machine learning and large language models.

Data management refers to the collection, organization, protection and utilization of an organization’s data and is a core enabler of modern businesses. Data is considered a company’s most critical and valuable asset, however without tooling to effectively manage and make use of the data, it is worthless. Data management technologies include Master Data Management, Customer Data Platforms, Data Unification platforms and Data integration platforms. Every system in use at an enterprise collects data, so a clear data management strategy is crucial to manage, govern and make use of all the data in a safe, secure and compliant way.

Data modelling is the practice of designing entities, attributes, and the relationships between them to represent real-world concepts within a system. It determines how information is organized and how different pieces of data connect.

Why it matters:
Clear data models reduce ambiguity in how data is interpreted by both humans and AI. By explicitly defining relationships, data modelling helps AI systems reason more accurately and consistently across complex domains.

Data observability is the ability to see and understand what data systems are doing in real time, including how data changes, moves, and behaves across pipelines. It focuses on detecting issues such as anomalies, drift, delays, or unexpected changes as early as possible.

Why it matters:
Strong data observability helps organizations catch data drift and abnormal behavior before it causes downstream impact. For AI systems, this early visibility is critical to maintaining reliability, accuracy, and trust.

Data poisoning, also known as AI poisoning, involves a deliberate and malicious contamination of data to compromise the performance of AI and ML systems. Attackers may inject false, misleading, or manipulated data into the training process to degrade model accuracy, introduce biases, or cause targeted misbehavior in specific scenarios.

Learn how to protect agaist data poisioning here.

Data profiling involves statistical analysis of various datasets (both structured and unstructured, external and internal) acting as an enabler to provide business users with insights into data quality and identify data quality issues. Profiling also checks data against established rules from rules management.

Data provenance is a historical record of source data, a way to understand the journey of data throughout the organization. Data provenance plays a crucial role in understanding the quality of your data and ensuring its veracity. It’s like a detailed travel log for data, showing where it came from, where it has been, and how it has changed over time.‍

Why it matters: Knowing a data’s origin and transformations ensures accuracy, trust, and compliance across systems and applications.

Data risk scoring is a method of rating potential risk on different kinds of information based on how sensitive it is and how likely it could be accessed by someone who shouldn't have it.

Why it matters: Understanding potential data risks allows organizations to prioritize mitigation and protect sensitive information.

Data transformation refers to the process of converting data from one format or structure into another, often done to facilitate analysis, integration or storage. Similarly we could say it’s like changing a piece of Lego so they fit better together in your creation, or in order to build something new and useful.

Data trust scoring assesses the reliability of any data with standards that provide instant insight into how much you can trust your data.

Data veracity refers to the accuracy, quality, and reliability of data, in order to make it suitable for decision-making and analysis. The better data veracity, the more trustworthy and better performing your AI can be, for instance.

Why it matters: Reliable, high-quality data is critical for accurate insights, trustworthy AI, and informed decision-making.

Data visibility refers to the ability to view and understand data across systems or platforms. It ensures users can easily locate, access and interpret data, providing transparency and supporting informed decision-making.

‍Why it matters: Greater visibility supports informed decision-making, improves accountability, and helps detect gaps or risks in how data is used.

Data visibility in AI involves having a clear and complete understanding of the data used by AI models - where it originates, how it’s processed, and how it influences the AI’s decisions. This helps organizations ensure data quality, maintain accountability, and make better, more transparent AI-driven decisions.

‍Why it matters: Understanding how data shapes AI behavior enables transparency, fairness, and responsible governance across AI systems.

Dynamic authorization is a context-based decision model that grants or denies access in real-time, rather than relying solely on static, predefined permissions. It works by first identifying the nature of the request, before deciding whether to collect any additional data to make the authorization decision. The process is done dynamically in real-time, and after collecting all context needed the right decision is made, as defined by the application’s access policies. Access is either granted, denied, or more information might be requested. For customers it can enable minimal friction.

Learn more here.

Dynamic enforcement is the practice of applying policies, permissions, or controls in real time, based on current conditions, context, or risk factors. Unlike static rules, it adapts continuously to changing circumstances to maintain security, compliance, and operational efficiency.

Why it matters: Dynamic enforcement reduces the risk of unauthorized actions, ensures policies remain effective in complex environments, and supports responsive decision-making. In AI systems, it is especially critical for autonomous agents, where actions occur at machine speed and must be continuously evaluated against evolving conditions.

Enterprise knowledge search builds on enterprise search by enabling stakeholders not only to find information but also to understand and apply it. It connects and interprets data across all internal sources - such as documents, emails, databases, and collaboration tools - to surface meaningful insights and synthesized answers. Modern versions use AI to understand context, relationships, and intent, turning raw data into accessible knowledge.

‍Why it matters: Enterprise knowledge search helps teams go beyond locating files to discovering the knowledge within them, improving decision-making, collaboration, and productivity across the organization.

Enterprise search is technology that enables organizations to find information stored across internal systems, such as file shares, intranets, document repositories, and databases. It indexes and retrieves content based on keywords and metadata, providing users with lists of relevant documents or data sources from across the enterprise.

‍Why it matters: Enterprise search helps employees locate information efficiently, reduces time spent navigating siloed systems, and creates a unified way to access organizational data.

Externalized authorization is access control decisions centralized and separated from application logic. In other words, it centralizes access control decisions for applications and systems across the organization, rather than within individual programs. This means the access logic and policies are consistent, regardless of the application.  It’s like having a central security office that decides who can enter which rooms in all buildings of a company, instead of each building managing its own security. Such centralized management allows security and IAM professionals to efficiently add, update and deploy policies across a portfolio of applications, alongside fine-grained access control which ensures users access the right data and actions. When combined with a dynamic data model, it allows businesses to leverage other data, make faster decisions based on dynamic data points and orchestrate a consistent experience across services (with all systems using the same externalized authorization). 

Learn more here.

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Fine-grained access control allows for more precise management of access permissions, and grants or denies access based on multiple factors. This method provides precise control over who can access what data or functionalities, and becomes particularly important when dealing with access to specific data or in complex circumstances - where you might have more than one account. Imagine a library where access to each section and book is individually controlled. Some books may only be available to specific membership types, and users may need different permissions to borrow books or access special collections.

Learn more here.

First party data refers to information directly collected by a company from its customers or users. It is typically obtained through interactions, transactions, or engagement with the company's own platforms, products, or services. First-party data includes information from sources like your customer relationship management (CRM) system.

Fragmented data refers to data that is scattered across multiple sources or systems in a disorganized manner, making it difficult to access, analyze and use. A company may use different systems for sales, inventory, CRM, marketing, etc. Without this data unified, it can be difficult to get a complete picture, leading to poor decision making and unsatisfied customers.

Graph-based access control is a security model that uses graph technology - where nodes represent entities (like users, roles, or resources) while edges represent relationships - to manage and enforce access decisions. By analyzing these relationships, the system can determine whether a user should be granted access based on context, connections, and permissions.

‍Why it matters: Graph-based access enables more accurate, context-rich decisions, improving both security and flexibility in complex environments.