Data latency is the time that passes between a satellite observation and the time it takes for the data to be available for use. Fire information from NASA’s Fire Information for Resource Management System (FIRMS) is available to the public in a few hours after it has been observed from a spacecraft. The VIIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, mounted on the Terra and Aqua satellites and the Suomi National Polar-orbiting Partnership (NPP) satellite, can provide data in real-time. The amount of time between the observation and the distribution of the data will depend on several factors, including the amount of time the users have to wait for the data to be processed.
Data latency has a variety of uses and is crucial for achieving real-time results. The real commercial value is in the ability to act on the data quickly. In many cases, data processing speed will be directly proportional to how quickly data can be acted upon. In the context of online marketplaces, the availability of data is essential for enabling a more seamless experience for buyers and suppliers. Similarly, news websites can increase their readership by providing relevant content. In addition, banks can minimize the cost of fraud by detecting transactions as soon as possible.
Data latency is an important factor that should be considered when developing a data warehouse or database. It is an important factor in business agility. Businesses that fail to account for data latency may lose a competitive edge. They may also lose opportunities and market share. If you want to maximize your business agility, you need to ensure that you have proper and appropriate systems. So what is the best approach for reducing your organization’s data latency?
While data availability is important, the real commercial value of data is in the ability to act on it quickly. Marketplaces can improve their user experience by displaying relevant content. News sites can improve reader engagement by presenting relevant content. And banks can reduce the costs of fraud by detecting transactions quickly. These are only a few examples of how data latency affects the business environment. To increase your business agility, you should consider the following scenarios and how to manage it.
When it comes to data quality, it is vital to understand the amount of time it takes to process data. In business intelligence, latency is one of the most important metrics for a data team to measure. The longer the data is delayed, the more likely it will be to be affected by a failure to act on it. But how to reduce it? How can you measure it? Here are some examples. Once you understand the concept of latency, you’ll see the benefits.
Data latency refers to the time that it takes for data to be retrieved from a server. For example, if you want to access a web page that requires a login, you should consider the latency before you log in. The more responsive a network is, the more it will benefit the user. If a website is slow to respond, it could be an indicator of a network failure.
Near-time data latency is overkill for some applications. It’s the most common way to provide data to a system that is constantly changing. If a company creates a report on a weekly or daily basis, near-time latency is the best option. However, a quarterly report doesn’t need to be updated every minute. Some-time latency is the best choice in these cases.
Data latency affects throughput, which is the amount of data written in a second. In contrast, throughput measures how much data is transmitted in a period of time. Moreover, a network’s path is curvy and the delay is a factor in that. The longer the latency, the slower the data is. Hence, it’s better to optimize the path. It is important to choose a solution with a low latency.
Some-time data latency is overkill for most applications. It’s the type of latency that occurs between the time a data source is created and when it is made available to end users. For example, if the data is created on a weekly or daily basis, it will appear immediately after the data is written. In such cases, it’s not recommended to use near-time data latency. When considering network performance, some-time latency is the best choice.
