The Digital Ecosystem Blueprint
|A blueprint for Digital Ecosystem Graphic|
A Digital Ecosystem is a conglomeration of Internet of Things (IoT), Machine-to-Machine (M2M), Big Data Analytics, Hybrid compute platform that enable new business models banking on the currency of data that is mostly real-time in nature hence more valuable to the users.
This is an opportunity for businesses to create value out of through investment in digital technologies. The Digital Ecosystem value-chain should cover sourcing, analyzing and consuming digitized engineering data and information.
- Digitize: source engineering data into IT systems
- Analyze and Automate: process data and control devices
- Consume: share and use information that is of value to business
Each element in the value chain is currently at a different stage of maturity.
Digitize. For years, the field of engineering has advanced and adopted automation yet largely in silos partly due to proprietary technologies. Now is a ripe time to tap into this vast pools of fossilized data, the new fuel for growth, to the enterprises of the future. Industry efforts towards standardization and consolidation are at various stages of maturity.
Analyze. IT Automation at the turn of the century caused a shift in the way we looked at work. Take a retail example of what has happened to coffee shops. There are now lesser number of people and more machines strewn across on a podium overlooked by a long queue of customers waiting to order and another long one waiting to collect their drink. Between the humans on the podium there are a few hundred unspoken communication happening for every order-to-delivery. Humans execute commands on different machines. Just contrast this with pots and pans with a few tens of humans running around fetching coffee and snacks to sitting customers - that sounds so prehistoric. The maturity of the process of IT automation is pretty high and leads to tectonic changes. In no time, actuation and control technologies will by themselves convert the commands into actions. To achieve this, businesses need ability to process the data and convert them into control signals for actuation devices and utility applications.
Consume. Consumption of information of value today is determined by the multi-modal access using applications and technologies by both humans and machines. There is an inclination to call these two together as Applications (and rightfully so). Sharing of data across Applications is important here crossing silos to enrich the data from various sources and to bring awareness into the context of data analysis. This is the high point for the ongoing convergence of IT and related technologies.
Some of the architectural considerations for the digital ecosystem are:
- It is an ecosystem and not just a system
- Diverse and widespread devices need ubiquitous connectivity
- Value of data is higher nearer to real-time putting a strain on the network and edge compute
- Standardization of interfaces and gateways could take time so focus on agility of solutions
- Higher volumes of data require even more storage and compute
- Analytic and algorithm technologies are needed to understand and amalgamate data from disparate sources
- Securing the ecosystem could bring more barrier to processing at the edge giving rise to new brokering technologies
- Multi-modal access channels will continue to spread. More network and newer applications both analog and digital
- Systems in the digital ecosystem are bipolar. They provide and consume data at the same time.
- Utility of digitized data may not be closer to the source at all times.
It's a cusp of abundance meeting plenty, the new normal.
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