Before shedding light on metadata management, it is crucial to understand what metadata is. Metadata refers to the information about your data. This data includes elements representing its context, content, and characteristics. It helps you discover, access, use, store, and retrieve your data, having a wide spread of variations. Metadata of an image. Image by Astera. Let’s look at some of the metadata types below.

Data aggregation is the process of combining and summarizing data from disparate sources into a cohesive dataset. It prepares data for analysis, making it easier to obtain insights into patterns and insights that aren’t observable in isolated data points. Once aggregated, data is generally stored in a data warehouse. Then, you can leverage it to gain a holistic perspective on your operations and market trends, design effective risk management practices, and make more informed decisions overall.

A solid understanding of internal technical and business terms is essential to manage and use data. Business glossaries are pivotal in this aspect, facilitating improved understanding and collaboration among teams. A business glossary breaks down complex terms into easy-to-understand definitions, ensuring that everyone in the organization, from the newest recruit to the CEO, is on the same page regarding business language.

OLTP is a transaction-centric data processing that follows a three-tier architecture. Every day, businesses worldwide perform millions of financial transactions. This fact brings to mind client-facing personnel such as bank tellers and supermarket cashiers tapping away on keyboards and at cash registers, and with good reason. According to ACI Worldwide, a payment systems company, there was a 42.2% growth in global real-time transaction volumes in 2023, amounting to 266.2 billion transactions.

Data mining, also known as Knowledge Discovery in Data (KDD), is a powerful technique that analyzes and unlocks hidden insights from vast amounts of information and datasets. Data mining goes beyond simple analysis—leveraging extensive data processing and complex mathematical algorithms to detect underlying trends or calculate the probability of future events.

Data filtering plays an instrumental role in reducing computational time and enhancing the accuracy of AI models. Given the increasing need for organizations to manage large volumes of data, leveraging data filtering has become indispensable.