In today’s data driven world, organizations and individuals rely heavily on the effective handling and use of data. When done correctly we can assess and gain valuable insights from data which allows for informed decision-making, technological progress and further promoting innovation. Two components that play a crucial role in this process are data processing and data management. While some use these terms interchangeably –– as aspects of the two overlap –– each component plays its own distinct role and is a distinct aspect of the data lifecycle.
We are data enthusiasts at GNO-SYS and enjoy diving into and discovering the best and most effective ways of handling data, whether that be for our clients or our own fun side projects.
Data processing refers to collection and manipulation of raw data, transforming its structure to produce insightful and actionable information. The primary objective is to extract valuable insights and uncover patterns hidden within the data. Various techniques and methods can be used to achieve this, including data filtering, analysis and so on.
Filtering identifies parts of the data which may contain errors and removes them to prevent any incorrect conclusions or derived products. Through analysis, useful information can be extracted from the data. For example the frequencies contained in complex signals by applying a fourier transform.
Data processing also generates derived products. In some cases, data is combined from multiple sources to create something new such as combining a SAR image with a multispectral image to improve identification of ground features. In some cases, derived products are relatively simple such as a location being computed by a distance and direction. But there are also more complex situations. These involve a series of equations relating camera focal lengths, lens distortions, camera positions, and orientations from many observations. These intersect to yield a 3D model of the target being imaged.
Data management is the practice of governing, organizing and controlling data. Although it may not enlist the buzzwords of data processing, it is crucial to effectively handling and transforming any type of data.
Data management provides a framework and procedures for organizing and managing different data assets. Its architecture gives structure to the storage of data including databases, data warehouses and so on. These systems provide a centralized and structured environment for one’s data. For large data systems, especially in cloud environments, the data can be less centralized. It can be spread across multiple data centers, typically for redundancy/backup or disaster recovery. This highlights the importance of good and effective data management because without a well-built process it can be harder to find and manage data. Additionally, having a well-established data management system provides a sense of ease. It also allows for a user to better focus their time on analyzing and processing the data.
The value that lives within data is priceless but requires a thoughtful approach to effectively assessed and processed. Certain forms of media, like an archive, are inexpensive but less readily available, whereas other forms are easy to access but more costly. Additionally, it is important for users to have a way to find and access their data which is where searchability functions come in. Frequently data will be organized according into a scheme, such as a tile system with the tile easily identified by the file name or directory structure. In other cases, data will be described by a catalog, such as Spatio-Temporal Asset Catalog (STAC). This catalog contains metadata and allows a user to find data useful for their application.
Storage is another important element of data management and though storage costs have dropped over the years, data volumes have skyrocketed. Additionally, with the prevalence of cloud services, certain formats are more easily used in the cloud environments. They achieve this by optimizing bandwidth use to only transfer necessary portions of files rather than entire files. Effective data management also ensures the privacy of your data through robust measures. Having these measures in place like access controls allows you to close the door and lock up your data helping maintain its integrity and value.
Though different, the two still work in unison. Effective data processing wouldn’t be possible without effective data management – to an extent the two are inseparable. Data management ensures data is accurate and reliable, providing a foundation for data processing to thrive. Without effective data management, any processing done would be impacted by inconsistencies and inaccuracies. This can result in unreliable and even misleading outcomes. In addition to this, data processing further enhances data management by extracting valuable insights and actionable information from raw data. With data management establishing the groundwork, data processing can pull the value from data.
However, while data processing and data management are closely related, there are some distinct differences. Data processing allows a user to analyze their data, in which they can draw insights and further use it to generate tools and products. Unlike data processing, data management does not sift through data cleaning it and analyzing it. Data management focuses on the environment in which your data lives in and is accessed through.
Despite their differences at the end of the day these two functions work in parallel to help you find and maximize the value in your data.
Being able to effectively manage and process data is vital to any operation. With an increase in our global reliance on data these functions and processes are more important than ever. As system engineers who help companies and individuals create software capable of unlimited extensibility, our team understands the need for robust systems. These systems address any data need our clients may have and give users flexibility and options. Our GNOde software is specifically designed to do exactly that. GNOde delivers users unlimited extensibility at their fingertips, equipping them with capabilities. These capabilities allow them to operationalize, automate, and scale their projects, procedures, and ultimately the value of their data.
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