Commonly Used Words You Might Come Across

Foundational Data Platform

A foundational data platform is a set of technologies, tools, and infrastructure that provide a foundation for collecting, storing, processing, and analyzing data in an organization. It typically includes data storage, data management, data integration, and data processing tools, as well as data governance and security features. It serves as the foundation for building more advanced data-driven applications and services.

Data Exchange

Data exchange refers to the process of sharing or transferring data between different systems, applications, or organizations. This can be accomplished through various methods such as data integration, data migration, or the use of common data formats and protocols. Data exchange enables different systems to share and access information, allowing for greater data interoperability and improved decision-making.

Data Governance

Data governance is the overall management of the availability, usability, integrity, and security of an organization’s data. It involves establishing policies, procedures, and standards for data management, as well as assigning roles and responsibilities for data-related activities. Data governance aims to ensure that data is accurate, reliable, and compliant with legal, regulatory, and organizational requirements, while also protecting the privacy and security of sensitive information.

Data Quality

Data quality refers to the overall level of accuracy, completeness, consistency, and reliability of an organization’s data. It is a measure of how well data meets the requirements and expectations of its intended use. Data quality is critical for effective decision-making, as poor quality data can lead to incorrect or misleading conclusions. Ensuring data quality involves processes and practices for data validation, data cleaning, data standardization, and data monitoring.

Data Transformation

Data transformation is the process of converting data from one format or structure to another, in order to make it more suitable for a specific purpose or use case. This can involve tasks such as data mapping, data cleansing, data normalization, data aggregation, and data validation. Data transformation is a key step in the data integration process, and it allows organizations to make their data more usable, consistent, and valuable for analysis and decision-making.

ML Modeling

ML modeling refers to the process of developing and training a machine learning model. This process involves selecting and preparing data, choosing a model architecture and algorithm, training the model on the data, and evaluating its performance. The goal of ML modeling is to create a model that can accurately make predictions or identify patterns in new, unseen data. The model can be used for a variety of tasks such as classification, regression, clustering or anomaly detection.

Data Lineage

Data lineage is the process of tracking the origin, movement and evolution of data through different systems and stages. It provides a detailed and complete view of how data is collected, transformed, stored and used, providing transparency and traceability of the data. Data lineage is important for data governance, compliance, and data quality. It helps organizations to identify errors and issues in data, and track how data is used and by whom. Data lineage can be represented as data flow diagrams, tables or metadata.

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