Take advantage of new generation data architecture to keep data distributed, secure and actionable: Iotics

Data driven decision making (DDDM) is a process that involves collecting data based on measurable goals or KPIs, analyzing patterns and facts from these insights, and utilizing them to develop strategies and activities that benefit the business in a number of areas. Fundamentally, data driven decision making means working towards key business goals by leveraging verified, analyzed data rather than merely shooting in the dark. However, to extract genuine value from your data, it must be accurate as well as relevant to your aims. Collecting, extracting, formatting, and analyzing insights for enhanced data driven decision making in business was once an all-encompassing task, which naturally delayed the entire data decision making process. Iotics liberates data every day to enable the world to make the most informed decisions. A world where anything can interact with anything. Iotics delivers a platform that enables data and insights to flow freely everywhere. Now more than ever, businesses depend on data-driven insights. In order to find the key insight for the business decision, the world has grown accustom to the pains that come with the current data solutions. Centralizing, duplicating and rearchitecting data is crippling on time, resources, budgets and security. Decentralized data architecture keeps data distributed, secure and actionable. A lightweight overlay overcomes vast, complex data silos with decentralized access to all your data. Blending signals from real-time, static and enterprise data source reduces complexity and surfaces the meaningful insight for the business to immediately action. Enterprises know digitizing, connecting and actioning data is critical for survival, now more than ever.

Distributed Data Architecture

Distributed data architecture allows enterprises to leave data at rest and extract the meaningful event data to your business with context. Unfortunately, many enterprises make choices that require centralizing, duplicating and rearchitecting which are often crippling on time, resources, budgets and security. However, accessing the data through these approaches does not give meaningful, relevant or real-time information or get the key insights into the right hands. By 2020, more than eight billion people and businesses, and at least 30 billion devices, will be connected. Connectivity is not meaningful communication. Data is not information. The more complex the data problem, the more important the semantic context becomes so you can share your data with confidence that it will be understood.

Semantic Data

Data needs context to become information. The recipients of information are normally humans and we humans are good at context. Unfortunately, machines are not. For a machine to “understand” data, the context has to be provided in an unambiguous way. This is where semantics comes in. Overlaying your data with descriptive metadata gives it meaning that other machines can understand. The semantic web technologies, such as Resource Description Framework, allow intelligence to be embedded within data making self-consistent blocks of data that need no interpretation. Using semantic data means that sharing data with others is easier and less prone to misunderstandings and apps can be intelligent about how they consume data, reducing application development time. Semantics embeds the intelligence in with the data and avoids mistakes as simple as this where a spacecraft crashed because one team used metric units and another used imperial, but they shared only the raw numbers. Data in context allows more seamless communication up and down supply chains where it can be clear that this data means exactly this about this particular thing, leaving less to interpretation and therefore mistakes. Any interaction with the source must have some arbitration. This negotiated access requires a mechanism of brokered interactions. Once the data is found and the follow request granted, the data flows from the producer to the consumer. The final step is that the semantic overlay on the data allows the consumer of the data to know what they have received. For example: this is a pressure in Pascals from a hydraulic coupling – not an atmospheric pressure in mmHg. The semantic context allows data to be understood and actioned.

Mature Event Analytics

A complex example is whether a train has deviated from its planned schedule. This has knock-on effects not only for the passengers, but also for the maintenance crews waiting for it at the end of day. The analytics in this case are more intricate, needing the position of the train, its route, its priority, etc. The result of the processing is that multiple significant events are created and sent, one to the train twin (immediately it is known that it has deviated) and one each to the planned and actual depots.

Hans Kuropatwa, Interim CEO

“Event Analytics within the Iotics operating environment is defined as the processing of real-time, event-based data to create additional meaningful events.”