What is Data Observability and Why Should You Care?
You can see the condition of data across the complete life cycle, which allows you to discover problems and act rapidly. Data observability is crucial for organisations so they can make smarter decisions, adhere to regulations, and decrease the total cost of ownership. You can achieve data observability by implementing data governance, discovery, modelling, security, and data usage controls.
Data observability is a management principle that allows anyone to view the state of an organization's data. It’s not just for data scientists and analysts—everyone should be able to see what’s going on with their company’s data. In this post, we'll dive into why data observability is so important and how it can impact your business. Let’s get started.
What is Data Observability?
Data observability is the ability to see the state of your data at all times. This means that you have the ability to see where all your data is, who's using it, who's responsible for it, and how it's being used. When an organization has high data observability, the business can make smarter decisions, comply with regulations more easily, and significantly cut down on the total cost of ownership. With data observability, you can track data from the moment it’s created all the way through its life cycle. This is critical for spotting issues, errors, and anomalies as soon as possible.
Why is Data Observability Important?
If you’ve ever worked in IT, you know that data and technology can be messy. When it comes to data, users and stakeholders often don’t have the right tools or visibility to understand what’s happening with their data. This can cause communication breakdowns, compliance issues, and unnecessary costs. You need to be able to see all your data and who’s using it in real time. This allows you to spot issues and take action immediately. This is data observability in action.
5 Ways to Achieve Data Observability
When it comes to achieving data observability, there are several ways you can do it. Let’s take a look at five of them: - Data Governance - Data governance is a set of policies and procedures for managing your data across the entire lifecycle. It includes things like data discovery, metadata management, data modelling, data lineage, data security, and more. - Data Discovery - Data discovery is the process of finding data across your organization. It allows you to see which applications and systems are being used to collect data, where that data is stored, and who’s using it. Data discovery tools can make data governance significantly easier. - Data Modeling - Data modelling is the process of creating a model that describes how your data is structured. It allows you to understand the relationships between data entities and be able to trace data across its lifecycle. - Data Security - Data security refers to the policies, procedures, and technologies that protect data across its lifecycle. This includes things like encryption, access control, data retention, data auditing, and more. - Data Governance Tools - Data governance tools help you to enforce data governance throughout your organization. They allow you to track, manage, and monitor your data across the lifecycle.
Data observability is the ability to see the state of data at all times, across the entire lifecycle. This allows you to spot issues and take action immediately. It’s important for organizations to achieve data observability so that they can make smarter decisions, comply with regulations, and significantly cut down on the total cost of ownership. To achieve data observability, you need to implement data governance, discovery, modelling, security, and use data governance tools.