i3 is an independent data fabric. It is independent of the applications that consume data and the applications/devices that produce data. It is used to manage and provide data governance as data flows through the data infrastructure.
Data flows connect authorized data sources and destinations and provide record-keeping services so it is clear which parties accessed which data and the purpose for such access. Data generated by one application (or many IoT devices) can be reused/leveraged for optimal efficiency AND applications have easy access to a vast library of authorized data sources. Throughout the process, the data owner is kept informed of data usage and remains in control of how their data is distributed throughout the organization or a multi-party ecosystem.
i3 has made it easy to insert a data management/governance system into an existing systems architecture and made it easy for that architecture to evolve as new data sources are incorporated into the data infrastructure.
Easy-to-use i3 software libraries connect existing data sources into the i3 data fabric with a couple of lines of code. By the same process, applications can easily be linked to the i3 data fabric in order to gain easy access to a treasure of data. Once an i3 system has been integrated into the data infrastructure, data flows can be activated or terminated with a few clicks of a mouse. Gone are the days when every data integration effort become an arduous ordeal to deploy and support.
Artificial Intelligence (AI)
Artificial Intelligence systems are based on rules. These systems accept data from a variety of sources and use their rules to determine if an event of interest has occurred. When such an event is found, the rule engines also specify how the AI system should respond to alert operations, generate reports, and react to changing conditions.
The value of any AI system is limited or enhanced by the data available to these sophisticated software tools. More data implies better insights and more value from the AI engine. When an AI engine receives its data from an i3 data fabric, it is easy to add more data to the AI engine data library. At the same time, it is easy to remove problematic data sources from the analysis. Enhancing an AI-driven system with i3 technology, allows the AI system to gracefully evolve as rules are changed and features are added which require additional data.
AI engineers tell us that they spend upwards of 80% of their time trying to identify and build the relationships necessary to make sure their AI engines can operate properly. That means that an average AI engineer only spends 20% of their time creating value from these complex systems. i3’s ability to manage the data flows that feed these AI systems ensures the AI systems and the engineers that support them are operating at peak efficiency.
Big Data is all about running data analytics processes across a database or numerous databases that form a data lake. Value is created when these data algorithms uncover hidden relationships between data items that reveal new and potentially valuable insights. Data lakes are typically used to manage fixed data sets that do not evolve. As the data changes, the changes are incorporated into these data sets toc create a new data set. This process often results in confusion related to a growing number of similar but different data files.
Rather than looking at data structures as fixed environments, the i3 technology manages data as a continually evolving series of data streams. In doing so, data analytics are no longer left to study historic data but can forecast trends and identify potential operational issues based on active situational data. i3 technology serves as a data fabric to coordinate the many tributaries that feed these massive data lakes and it does so in a way that respects the rights of the data owners by giving them a voice in the conversations about how their data will be used.
Applications consume data and present it to the application users in a way that allows them to use technology for the operational advantage of an organization. Applications are thirsty for data and many applications maintain internal data structures for the exclusive benefit of that application. Applications that have been vertically integrated to manage data sources from ingestion through the presentation inadvertently create closed or siloed data structures that restrict an organization’s ability to collaborate internally and externally.
The expansive growth of the internet was fueled by the advent of browser-based technologies that allowed independent and remote clients to access remote applications that supported a myriad of distributed users. i3 helps break down these data silos by treating data as an organization’s asset and allows these data assets to be managed as any other organizational resource.
Internet of Things (IoT)
Great advancements have been made in the world of IoT. Sensors and actuators are becoming so cost-effective that literally anything in the physical world can be monitored by attaching a sensor to it and controlled through actuators. Many IoT systems have been deployed to meet specific requirements however there are many more use cases that while desirable, cannot justify the costs associated with the deployment and support of a large dedicated network of sensors.
The key to the large-scale use of IoT technology is to deploy IoT devices that generate data for many applications. When the development, deployment, and support costs are amortized over a number of different applications the return on investment for these networks is dramatically increased. But, for this to happen the industry has to move away from the deployment of single-use application-specific IoT systems and applications. i3 technology has been developed to allow these next-generation IoT to become practical and manageable.
Digital Twins and the Metaverse
Digital twins are data constructs that model physical objects as they exist within a digital environment and often augment these virtualized objects with digitally enhanced capabilities. For example, a physical automotive tire might be digitally modeled to better understand how a tire might perform in a variety of conditions and physical environments that could not be physically tested. Alternatively, a building might be modeled as a digitized image that can be virtually placed in a larger city to better understand how people might interact with the building. These digitized models are referred to as digital twins and a collection of digital twins forms a metaverse.
Many metaverse conversations focus on the creation of a virtual digital world that is operationally autonomous from the physical world. A virtual object’s ability to generate value stems from its ability to shape our interactions with the physical world. For example, a virtualized model of a building has little value in itself, but its ability to allow us to consider different renovation options or to predict potential earthquake damage is extremely valuable, i3 technology has been developed so that sensors placed within a building and around buildings allow the physical world to actively shape the nature of the virtual model and thus increases the value of the larger metaverse.