Modern data collection techniques allow massive amounts of information to be created, stored, and accessed within businesses. In addition to the sheer volume of available information, the expanding selection of connectible devices in the Internet of Things (IoT) compounds the difficulty. Any system attempting to access and normalize data could be dealing with endpoints numbering in the hundreds, if not more, for large systems and organizations.
The complexity of finding, storing, condensing, analyzing, and successfully interpreting giant data sets spread between multiple entities exceeds what most data analysis software solutions can do. Even businesses that recognize the value of using nuanced, deep dives into data as a way to make informed decisions quickly may have trouble putting that plan into action without the correct tools.
Data fabric is the missing link for many organizations.
Think of data fabric as a woven tapestry of threads that connects each device within an IoT network. The fabric collects and distills all of the data flowing between these endpoints automatically, refining the information into a result that makes sense and provides all the depth necessary for proper data analysis.
AI and machine learning models would allow the data fabric to gradually evolve and become more efficient at recognizing data patterns and valuable insights that would benefit from human attention. A mature data fabric, hosted in the cloud and connected to each device within an organization’s IoT network, could eventually learn to monitor and repair disruptions, manage complex business processes, and help executives make fast decisions backed by information collected from every relevant data endpoint within their company.
Data fabric, IoT, and edge computing are technologies poised to grow together.
Edge computing is gaining traction in the IoT space. Users can execute processes locally on devices without having to communicate with a remote, cloud-based server. This development has dramatically improved speed, reliability, and security for users. But it has complicated data collection and interpretation even more. If devices accept user input and run processes based on that input locally, that makes them the source for harvesting, storing, and transmitting that information to a central hub to be analyzed.
If a data fabric were implemented within a local IoT network that uses edge computing, that fabric could communicate with the central, remote hub and send all of the relevant analytics automatically while the local devices execute their operations for the end user.
Powerful data analysis was once only available to companies that could afford top-of-the-line software, but now smaller businesses are getting in on the action. As data collection becomes increasingly important to all businesses and IoT networks become more complex, data fabric technology and usage will likely grow. The forecast is bright for better and smarter data fabrics to emerge.
