This is the 3rd column of the 12-part series called Navigator MasterClass, wherein we will find our way through the myths and realities of one Bleeding Edge technology each month; in terms of where it truly stands at the time of writing, and its business applications- implemented, being attempted, or speculative. This month?s topic is Edge Computing. It is a technology that has hundreds of ?gurus?, each with their perspective. Let?s sift through that; we will however start with this columnist?s personal experience, which may seem unrelated at the start, but is interestingly similar.
In the early part of my career, I worked for a consulting company in the US. And their focus was offshoring to India, a concept that was resisted politically, socially, and technically. And there were two selling points: take the work to the people (and not the other way around), and continuous workday. Now the similarity is obvious. IT has travelled the path from big Data Centres to the cloud to data capture at the source. Computing at source is the inevitable next step.
We are now saying this after taking the computing remote to the cloud! Gartner says that edge computing complements cloud computing. And this is where the confusion starts. ?How can something remote be complemented by something so near??. The answer is in the question itself. Computing At Source was delayed by the cost of IoT, no longer an issue now. AND its feasibility was determined over by baby steps like cars, refrigerators, etc.
What Edge Computing is delivering is the ability for a business to collate and learn. In other words, combine it with Artificial Intelligence, Augmented Reality, Virtual Reality, Gaming, Natural Language Processing, etc. This is an outstanding division of work.
Gartner says four laws will take the majority of data outside data centers; McKinsey names them differently:
1. Law of Physics (speed Matters). McKinsey calls it the ?Need for Real-time Decision Making?.
2. Law of Economics (bandwidth issues). McKinsey calls it ?Varied Connectivity and Data Mobility?.
3. Law of the Land (regulatory). McKinsey calls ?New Storage and Security Needs?.
4. Murphy?s Law (connectivity goes down). McKinsey calls it ?Intermittent Power?.
McKinsey has listed over 100 potential use cases in Travel/ Transport/ Logistics, Retail, Healthcare, Public Sector/ Utilities, Energy, and Materials. And none of them are conceptual; 3000 companies are in the process of deploying these. They do go on to point out that the major risks are Privacy and Data Security. They finally go on list 3 ?habits? for Edge Computing to succeed:
1. Begin with what you already, make, or sell (not with new and unfamiliar).
2. Learn through multiple use cases (not a single one).
3. Embrace opportunities for Business Process changes.
McKinsey also says that the biggest advantage is quantitative decision-making, which is no longer a scary concept. For example, an agricultural farm manufacturer can analyze soil and water, and send it to farmers through satellites. Or autonomous plants that use Edge Computing, Augmented Reality, Artificial Intelligence, and Analytics.
MIT starts by saying that the cloud was ?bad enough? for cybersecurity, and now we have Edge Computing! But they go on to opine that with globalization and location being the challenges at the same time, ONLY Edge Computing can provide the solutions. The two changes that they foresee as being needed are:
- Requires new relationships with vendors, customers, employees, and even competitors.
- Strong Analytics is a requirement to realize full value.
We will close by listing some potential uses of Edge Computing:
- Information Gathering and Collation
- Entry Gateway
- Decision Making (for example, maintenance decision to halt a production line instantly)
- Real-Timeme Transactions
- Autonomous activity
- Immersive Experience
At some time or another, every business will be using Edge Computing. In various degrees and uses. This brings us to the very final point: Edge Computing IoT devices will be highly specialized (unlike generic cloud computing); from visual processing in autonomous vehicles to freezer temperatures in trucks carrying frozen food, and so on.