Data is at the core of every IT process, and the amount of data and applications that organizations must manage is only likely to increase over time. Not only do you need the infrastructure to store and manage all this data, you also need to make sure that data from different sources is actually compatible before you can do anything with it.
Learn how beneficial data compatibility is and when it can make a big difference for you.
Key Takeaways:
Data compatibility arises from consistency across data schemas, structures, and definitions across multiple different data sources. This consistency makes it possible for engineering teams to collaborate and communicate effectively and seamlessly.
Compatibility also leads to interoperability, enabling different data systems and platforms to exchange data without a middleman. Without interoperability, data must be transformed every time there is an attempt to use data from one source with that of another. This results in an unnecessary loss of time and resources and opens up the possibility for data to be lost in translation.
According to expert insights from data architecture professionals in the LinkedIn community, ensuring data compatibility requires:
Compatibility is obviously a good thing. However, if you are already operating in an environment where different data sources are incompatible, it can be easier said than done to shift to an interoperable methodology. When exactly should it matter to you to do so?
Disparate data sources are those that maintain data in separate systems, databases, or file formats that are not compatible with one another. This might not present a noticeable issue at a small scale, but the difficulties stemming from a lack of data compatibility often become more apparent as your operations grow and expand.
When teams or individuals wish to collaborate or communicate regarding a data-driven process, disparate data becomes a roadblock. It is practically impossible to be on the same page when using two different types of datasets with different primary keys.
The biggest telltale sign that incompatibility is becoming a roadblock is when data quality issues begin to arise. Disparate data sources have differing levels of accuracy and consistency and may operate on different timelines, making it difficult to determine which data is of higher quality.
Frequent data cleaning can be an effective solution to bring all data sources up to snuff, but it is temporary. A full-scale data transformation is the ideal long-term solution to ensure future data compatibility.
Efficiency is always important for your organization, but it becomes paramount when you are dealing with strict budgets and tight timelines, all while trying to provide the best user experience. Incompatibility can hinder your efficiency at every step due to the need for cumbersome data transformation.
Datasets that are compatible and interoperable can be blended directly without the need for any data transformation processes. This allows for an environment of “plug-and-play” simplicity when collaborating on data-driven projects and allows the dynamic generation of new analytics-ready datasets.
Not only is the need for data transformation a hurdle that impedes efficiency but so too is data integration. To effectively use data from incompatible sources, you must transform it into a consistent format and then integrate it all into a unified system. This integration phase requires meticulous mapping of data to ensure that there are no inconsistencies or redundant instances of data.
Suffice to say, data transformation and integration can be anything but efficient processes. This is increasingly true for organizations processing larger amounts of data, meaning that the problem stands to become more and more of a hindrance as your business grows.
Consistency of data and consistency of technology drive efficiency. Data compatibility becomes even more of an asset when there is technical compatibility as well. Add to that the ease of management that comes with building security and compliance for data governance, and you can have a much more efficient process for collecting data and using it to form data-driven decisions.
Incompatibility of data leads to roadblocks and slows workflows. In a time when consumers demand more from your applications and are generating more data than ever before, you need a solution for overcoming a lack of interoperability. You can solve these problems and achieve compatibility and interoperability that is also relevant to a cloud-native future by adopting the right cloud services.
Nutanix AHV is a virtualization platform that comes equipped with a full suite of enterprise features that simplify the management of workflows and associated data. It can also function as a database-as-a-service, automating management across multiple data sources and across any variety of on-premises or public cloud locations.
Through streamlined management powered by an easy-to-use hypervisor, AHV not only helps you achieve data compatibility but also solves a number of other modern cloud challenges with distributed systems all manageable through a single control plane. With this in mind, operating in the Nutanix environment can give you the confidence to place data and applications anywhere without worrying about incompatibility as an obstacle.
Learn more about database automation and other ways to simplify data management in the cloud.
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