A provocative title for sure when everyone thinks we just started the era of cloud computing. I recently listened to a talk by Peter Levine, general partner at Andreessen Horowitz on this topic which makes a ton of sense. The proliferation of intelligent devices and the rise of IoT (Internet of Things) lead us to a new world beyond what we see today in cloud computing (in terms of scale).
I have said many times that the onset of cloud computing was like back to the future of centralized computing. We had IBM mainframes, dominating the centralized computing era during the 1960s and 1970s. The introduction of PCs created the world of client-server computing (remember the wintel duopoly?) from 1980s till 2000. Then the popularity of the mobile devices started the cloud era in 2005, thus taking us back to centralized computing again. The text message I send you does not go from my device to your device directly, but gets to a server somewhere in the cloud first and then to your phone. The trillions of smart devices forecasted to appear as sensors in automobiles, home appliances, airplanes, drones, engines, and almost any thing you can imagine (like in your shoe) will drastically change the computing paradigm again. Each of these “edge intelligent devices” can not go back and forth to the cloud for every interaction. Rather they would want to process data at the edge to cut down latency. This brings us back to a new form of “distributed computing” model – kind of back to a vastly expanded version of the “PC era”.
Peter emphasized that the cloud will continue to exist, but its role will change from being the central hub to a “learning center” where curated data from the edge (only relevant data) resides in the cloud. The learning gets pushed back to the edge for getting better at its job. The edge of the cloud does three things – sense, infer, and act. The sense level handles massive amount of data like in a self-driving car (10GB per mile), thus making it like a “data center on wheels”. The sheer volume of data is too much to push back to the cloud. The infer piece is all machine learning and deep learning to detect patterns, improve accuracy and automation. Finally, the act phase is all about taking actions in real-time. Once again, the cloud plays the central role as a “learning center” and the custodian of important data for the enterprise.
Given the sheer volume of data created, peer-to-peer networks will be utilized to lessen load on core network and share data locally. The challenge is huge in terms of network management and security. Programming becomes more data-centric, meaning less code and more math. As the processing power of the edge devices increases, the cost will come down drastically. I like his last statement that the entire world becomes the domain of IT meaning we will have consumer-oriented applications with enterprise-scale manageability.
This is exciting and scary. But whoever could have imagined the internet in the 1980s or the smartphone during the 1990s, let alone self-driving cars?
I was invited to participate in a panel called “IoT Analytics” last Thursday, March 23rd. This was organized for the IoT Global Council by Erick Schonfeld of Traction Technology Partner (New York). Besides me there were two other speakers: Brandon Cannaday, cofounder and chief product officer of Losant and Patrick Stuart, head of products at SkyCatch. For those of you not familiar with IoT, it stands for Internet of Things. There is another term called IIoT for Industrial Internet of Things. IoT has been in the lexicon for last few years signifying the era of “pervasive computing” where devices with an IP address can be everywhere – the freeze, microwave, thermostats, door knobs, cars, airplanes, electric motors, various sensors,…..constantly sending data. The phrases “connected home” or “connected car” are an upshot of the IoT phenomenon. However Gartner group showed IoT to be at the peak of the “hype cycle” couple of years back.
I emphasized on the “pieces of the puzzle” or the components of IoT Analytics – data ingestion at scale, handling streaming data pipeline, data curation and unification, and storing the results in a highly scalable NoSQL data store, as the steps before analytics can happen. Just dumping everything into a Hadoop data lake only addresses 5% of the problem (data ingestion). Transforming the data and curating it to make sense is a non-trivial step. Then I spoke about analytics which has several components – descriptive (what happened and why?), predictive (what is probably going to happen?), and prescriptive (what should I do about it?). Streaming analytics must filter, aggregate, enrich, and analyze high throughput of data from disparate sources to identify patterns, detect urgent situations (like a temperature spike in an engine), and automate immediate action in real time.
Patrick of SkyCatch showed how they are serving the construction industry in taking images (via drones) and accurately creating “earth maps” for self-driving bulldozers, thus saving human labor cost. Another example was taking images of actual progress in large construction sites and contrasting it against plan, to show offsets, thus detecting delays and taking corrective actions in time.
Brandon of Losant showed example of a large utility company in Australia that supplies high powered (expensive) pumps with sensors. By collecting data from the sensors and monitoring it centrally, they can identify problems and notify the maintenance teams for taking corrective actions. Previously they had to fly people around for maintenance and this new IoT Analytics has saved the company lots of cost. Both are startup companies in the IoT Analytics space and are tackling immediate issues in real time.
It was a good panel and I learnt a lot from my co-panelists.
I happened to be in India last November when prime minister Modi announced the demonetization program, where 86% of the currency in the form of two paper bills (Rs. 500 and 1000 denomination) were made defunct. People were given time to deposit their existing currencies in the bank. Those who had unusually high volume of such currencies were supposed to declare the legal source or face stiff penalties such as 60-75% tax. The goal was to catch the money hoarders and black marketers who avoid paying taxes on such undeclared money.
Four months later, I happened to visit India last February. Everyone suggested I download an app. called Paytm. I could transfer money from a bank account instantly. What was convenient with Paytm was that I could use it at gas stations, small stores, and even at roadside vendor shops. Everyone seems to have installed the Paytm station where you point the smartphone with a Paytm barcode and the transaction happens instantly. You can check your balance any time. I noticed people are paying for phone, utilities, and other conveniences without having to carry loads of cash. To incentivize more usage, discounts are doled out by many vendors.
Paytm, based in Delhi, has raised $738 million from investors inside and outside India (Alibaba, SAIF Partners, Goldman Sachs, Singapore’s Tomasek, Taiwan’s Mediatek, etc) at a valuation over $5B. Paytm wallet users exceed 200m. Clearly the demonetization has come as a boon in dramatically increasing it’s usage. They have also started their international operation by making the digital wallet available in Canada earlier this year.
Why the digital wallets have not taken off big in the US? We have Apple Pay and Google Wallet for a while, but their usage has not been spectacular. One of the reasons may be the wide use of credit and debit cards that consumers are used to. But in a developing country like India where credit/debit card usage is quite low, a digital wallet like Paytm scores big. The company expects to reach profitability next year and may be one of the new unicorns (>$10B) soon.