Data latency is the length of time it takes for data to go from an event to a retrieval process or storage. It’s primarily measured in milliseconds with two methods:
- How long it takes for the data to go from an action, such as generation, to arrival at a database, server, application, platform, or other software.
- How long it takes after the data’s arrival to be available in that system
Companies choose the appropriate method based on the use case, the type of data, the applicable systems, the impact of data latency on a given application, and other factors.
Types of Data Latency
Real-time data provides near-instantaneous access to data and is also referred to as zero-data latency. It’s a resource-intensive configuration, as your organization needs systems that can collect and retrieve data at the same time. Use cases that require up-to-the-second data, such as fraud detection and healthcare applications, rely on having access to constantly updated data.
Real-time data is overkill for many applications, which is where near-time data latency comes into play. You configure the database to provide this data with a set interval that makes sense for your business requirements. Hourly, daily, and weekly schedules are commonplace for reporting tools. This option is commonly used when you have data that you need to pull in for a regularly scheduled report. For example, if your sales team creates a quarterly report, then it doesn't need to have data updated every minute. Instead, it's updated as needed.
If you have infrequently accessed and updated data sets, then some-time data latency works best for them. The typical update schedule for this data is quarterly or less.
Many organizations customize the data latency to the use case of the database or application and to best allocate technical resources. The proper management of data latency is a key factor in business agility. When organizations fail to account for how long it takes to access data or when it was last updated, they can lose their competitive advantage and struggle with maintaining a competitive edge. If it takes too long to make data-driven business decisions, then you can lose out on opportunities and market share.