Data observability is a process that actively monitors an organization’s data for accuracy, health, and usefulness. It is the ability of an organization to have comprehensive visibility over its entire data landscape, including data pipelines, infrastructure, and applications. Data observability allows the organization to quickly identify, control, prevent, remediate, and fix data outages, all within agreed service level agreements (SLAs).

3. Elevate risk governance to the next level. NatWest is the UK's leading business bank and second-largest retail mortgage provider. Lengthy change implementation processes hindered risk governance at the bank. Initiating change projects or launching products and applications meant navigating layers of policy checks and approvals, which could take up to 73 days.

Customer experiences matter. And many businesses struggle to improve them. More often than not, a bad process is to blame: manual tasks, data spread across too many systems, or even just old ad hoc workflows that linger on because that’s how it’s always been done. But the cost of ignoring these issues is high.

The regulatory and business demands of financial services require teams to collaborate with consistency and accuracy—a difficult task when it comes to enterprise risk management (ERM). Business units often complete their risk management strategic objectives independently, and since each may have its own taxonomies and processes, human errors may go unnoticed, increasing potential risk.

Metadata refers to the information about data that gives it more context and relevance. It records essential aspects of the data (e.g., date, size, ownership, data type, or other data sources) to help users discover, identify, understand, organize, retrieve, and use it—transforming information into business-critical assets. Think of it as labels on a box that describe what’s inside. Metadata makes it easier to find and utilize the data that you need. Typical metadata elements include.

Data provenance is a method of creating a documented trail that accounts for data’s origin, creation, movement, and dissemination. It involves storing the ownership and process history of data objects to answer questions like, “When was data created?”, “Who created the data?” and “Why was it created? Data Provenance is vital in establishing data lineage, which is essential for validating, debugging, auditing, and evaluating data quality and determining data reliability.

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.