i3 is an independent data fabric. It is used to manage and provide data governance as data flows through the network. Data flows that connect a source and a destination are only created if the connection request is approved by the data owner. These data connections can be activated (or deactivated) on command or according to a user-specified schedule.
All data transfers are logged for future reference making it easy for an administrator to determine who had access to what data when.
i3 has made it easy to insert i3 technology into an existing systems architecture. The i3 libraries make it easy to connect existing data sources into the i3 data fabric with a couple of lines of code. By the same process, applications can be linked to the i3 data fabric in a couple of lines of code as well. 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 that is 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.
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 analytic algorithms uncover hidden relationships between data items that reveal new and potentially valuable insights. And, just like streams feed into larger lakes, data flows feed into data lakes thereby allowing these data structures more accurately forecast trends and identify potential operational issues. i3 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 may be easy to deploy but their structure inadvertently creates data structures that restrict an organization’s ability to collaborate internally and externally.
i3 helps break down 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 are only now beginning to become cost-effective. While first-generation IoT systems were deployed based on the benefit of a single IoT use case, these next-generation IoT systems are not being to create data that has value across a range of applications.
Digital Twins and the Metaverse
Digital twins are data constructs that model physical or virtual objects as they exist within a digital environment. For example, a physical automotive tire might be digitally modeled along with an environment to better understand how a tire might perform in the physical world. Alternatively, a building might be modeled in a digitized view of 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. These digital models may use sensors so the model is a physical reflection of a real-world object so the characteristics of the digital object change as the physical object changes. It is even possible to link a digital twin so that changes to the object made in the digital world can later be sent to the physical object through the use of actuator technology. In these environments, i3 plays an important role by managing the flow of data into and out of the digital twins contained within an metaverse.