Monday, April 9, 2018

Before and After

B-School changed me. But it changed me for good and my blog for bad. Or so as I like to believe. I no longer write about what's on my mind, FB has started taking a major chunk of it. I no longer write about travels, too busy Instagram'ming them. A person of few words, as I would like to call myself, with some rare exceptions who could disagree on, I became a person of fewer words, never mind the lots of hashtags though.

A few hours in a workshop today with lots of marketing and designing folks from the company and around, I had three realizations:

  • I was the diversity today and it didn't had to do anything with my gender, with less than 1% of Engineer-MBA in the room. A self achievement unlocked
  • Business Feasibility, is a concept I now understand way too well (not in a very usual modest mode today) and hence that spoiled my day with some really stupid marketing ideas around me! 
  • I just needed to scribble here 
Phew! :)

Wednesday, January 31, 2018

The other view

So after 10 years of regular blogging, 2017 clearly marked itself as the year when I officially stopped being a regular. However, I do wish to begin 2018 with a Happy New Year note to all :) and with it comes my 2 cents on the changing, or rather not changing trends on the technology driven offline vs online, in an emerging economy like India, which quite reflected my thoughts here as well. As a millennial Indian consumer i pen down what i personally observe.

       Offline shopping still gives a very good competition to offline and is here to stay for a while – Why I say so? Well, it definitely takes a bigger share of my wallet and the people around. Even the most important moment of truths as a consumer be it apparels, books, or electronics still happen around offline. Thanks to the not such a developed technology infrastructure even in tier 1 cities like Hyderabad/bangalore (where the quality of mobile internet has its own regular doubtful moments). Add to it the backend infrastructure and challenges in logistics, the reduced trust on the reverse logistics and so on! It might still take some time before the complete life cycle of E Commerce develops, for it to overtake the regular brick and mortar.

Having seen my husband try his hands on subscription model (in furniture), I personally think it still seems to be more of a fashionable thing to do rather than a financial decision. Thanks but no thanks Amazon, I do not wish to subscribe to the grocery items which you give an option to, I would rather like to keep my options open when it comes to choosing the purchase the next month and these also include the offline deals which I pass by along with the dozen of mobile notifications I get from the ever upcoming local online competitors which just can’t seem to stop mushrooming even after a perceived investment slowdown in online retail. So, though subscription is an interesting option when it comes to models like netflix but yet to take over the mainstream business!
     Now local transport is an interesting area where my wallet share has completely skewed (and wallet blown over quite literally). From what used to be a once in a while indulgence in ride hailing apps, it is now becoming a regular habit, thanks to the convenience offered. It comes with its own disadvantage, the discounts seem to have finished from the cab rental market, and the supply demand totally owns the quality as well as price of the service. But irrespective of that wait, did I hear Uber buying a fleet of cars from Volvo?

So when we think the technology is changing the business (which it definitely is), a check on when and how far can bring some interesting insights as well!

Thursday, April 13, 2017

Leveraging IoT for Smarter Buildings

Imagine if one of your EOD (End of the day) task at work is collecting the ingredients of a pre-decided dinner recipe just outside your work place. Or picture coffee machines at work which remember your preferences and tell users when they need to be refilled. Too smart, too soon? Not exactly! These are the features of, what has been claimed and awarded as, the world’s most sustainable and smart office building called The Edge, with an “outstanding” rating and highest ever BREEAM (short for Building Research Establishment Environmental Assessment Methodology) score of 98.36%.

The way, ride sharing apps have connected our smartphones with a more convenient transportation (while simultaneously occupying a bigger wallet share!) similarly, buildings are one of the basic components which are swarming with opportunities. A staggering 40% of the world’s electricity is consumed by the 1-billion buildings on the earth’s surface, and they are responsible for 33% of the globe’s greenhouse gas emissions. This leads to the need for greener, smarter, and more intelligent solutions — The Smart Buildings.
Smart Buildings are said to be among the fastest growing commercial market under Internet of Things, along with Smart Home and Transport. According to Gartner estimates, by 2017, use cases in smart commercial buildings and transportation will be the main contributors, representing 58% of all IoT installed base in a Smart City. This article covers the business use cases and areas which show the impact of IoT technology in the Real Estate sector, or the Smart Buildings.
Buildings have evolved from simple to automated and now pave a way to smart connected buildings along with technology progression. IoT, by its basic definition, implies connected physical objects communicating data about their condition, position, or any other attributes over internet. For any business to derive value while leveraging IoT, it needs to focus on reducing the major cost forms to generate viable returns. Improvisation and reduction of the operational and maintenance costs, which form a huge portion of the Life-cycle cost of a building, are key attributes impacting the smart index of a building. Following details the important characteristics of a Smart Building:
  1. Optimization of the Building resources — As sensors track different building functions like motion, pressure, light, temperature, etc. an integrated platform can intelligently monitor and control them accordingly. This has led to a growing number of technology providers in the energy efficiency space like lighting, energy usage monitoring, smart plugs, occupant detection, HVAC monitoring and control, access control, water management, and others. According to the United States Environmental Protection Agency (EPA) about 35% of the total building operation costs is spent on energy, while a little over a third, is spent on lighting. An interesting example in this area being View Dynamic Glass, a Smart Glass player, whose tint can be modified automatically based on weather outside (solar heat coefficient) or through smartphones based on a user preference. The company has seen over 300 completed commercial installations and another 150 in progress of an extensive client portfolio since last 12 months and boasts of investors like GE, Khosla Ventures, DBL, etc.
  2. Automation — Reduced manual human intervention across various dimensions like building security, environment risk detection, structural health sensors, etc. not only aims to improve the user experience but can also identify grey spaces in savings through features like fault detection, proactive maintenance, energy savings, or interfacing with various applications in general. For instance PointGrab claims to use embedded edge analytics on a low-cost ARM-based processor for image sensors to identify and process occupant details while protecting privacy. Such technology can lay foundation to numerous upcoming use cases like staff planning, space utilization, etc. in a Smart Office or advanced analytics in retail chains.
  3. Personalization — A normal automated building might have a centralized feedback, however an interactive control flow makes a building smarter. Dynamic and self-learning control systems, preference-based operability providing occupants with smartphone based control over the end-user energy flow in workplaces, or personalized retail experiences like automatic check-in, marketing, payment, etc. are some opportunities which can be harnesses through Smart Buildings.
Hence, the real-time visibility of building operations and energy responsiveness are clearly the game changing way forward towards a smarter home, office, hospitals, and an overall smarter infrastructure in a Smart City ecosystem. However, there are few challenges which still need to be addressed before the wide-spread implementation of the technology to provide the desired commercial returns. These include:
  • Smart Building capabilities should match with the needs of existing facilities like existing legacy system controls or proprietary technology
  • Upfront cost needs to match with the life-cycle savings of the simple sub-systems like efficient light control. While implementing the smart solutions at The Edge in Amsterdam, Deloitte considered implementing the smart solutions with a return on ­investment of less than 10 years. Moreover, the recurring costs of a traditional and smart building might vary a lot for an end-user
  • The security issues associated with Smart Buildings — A simple break-in to the Building Automation System (BAS) can lead to not only software but physical vulnerabilities as well, as exposed by the IBM X-Force Ethical Hacking Team
  • Privacy concerns of an end user — The data being collected by continuous monitoring of a building brings out certain privacy concerns by the users. Hence smarter buildings need to come up with smarter regulations to address the same.
Real-time monitoring, sustainability analysis, energy consumption, and innumerable similar scenarios can further create multitude of business opportunities bringing forth a new disruptive frontier in both B2B and B2C space through real estate sector. Established players like Cisco, Intel, Honeywell, Huawei, Philips Electronics, and others have already realized the commercial importance this segment can bring with their own Smart Buildings solutions. Future lies in implementing the same to address the right problems, keep evolving the solution with enhanced underlying technology, and get back the expected overall returns in a long term.
Views expressed here are my own and do not represent that of my employer (Current or Previous)

Sunday, March 19, 2017

Deriving business value from the intersection of Edge IoT with Artificial Intelligence

According to The Economist Intelligence Unit IoT Business Index 2017 (Link), though 90% of business owners accept the evolving convergence of IoT in business, there are 2 major deterrents of wide-spread business implementation of IoT Technology:

1. High cost of required investment in IoT Infrastructure,
2. Security and privacy challenges

In order for a business to reap value from connected devices over internet, the crucial missing piece which needs to be harnessed is the right utilization of the humongous data from the ever increasing number of connected devices in terms of time sensitivity as well as bandwidth of the data being transferred. This is most likely to be possible when the following basic conditions, which reduces the cost and increases the return from an IoT implementation, are met:

  > Limited human intervention
  > Limited hardware

This is when the concept of Edge processing comes into picture. Edge processing (or Fog Computing, as Cisco originally coined the term) is the concept which in simple terms uses data from unlimited sources before it looses significance as it brings processing at the edge of the network or ‘on-the-device’. It is not a new idea as reducing the dependency over the cloud and internet has been understood since the importance of cloud infrastructure was understood in real scenarios and would be an important piece in commercializing the connected devices in the future. Example: India’s leading ride sharing operator Ola launched its Ola Offline App in the late 2016. With consistently increasing number of connected devices, one can only think of the ease edge processing can bring to an IoT solution both it terms of the RoI of an IoT investment as well as Security. This means devices to turn and act ‘intelligent’ on their own and hence the intersection of artificial intelligence.

USE CASES which see a huge amount of data generated by IoT / mobile devices can definitely use the high precision machine learning / deep learning (ML/DL) platform as it can tackle some of the major issues related to data like: Latency, Bandwidth, Security of the data, as well as the Cost incurred in transmitting it, apart from several others. Some of the use cases which can be thought of are:

1. Time sensitive decision making in connected devices — Decision making in autonomous vehicles where information processing needs to be done in micro/milli seconds and it can’t afford the transmission lag and latency issues

2. When enormous data should be processed before being transmitted like monitoring plants in a vast farmland, where only the information about withering or infected plants needs to be transmitted based on certain sensor parameters

3. Security is of concern in automated industrial plants or surveillance cameras where local processing and intelligence of huge amount of recorded data is of high priority

4. Smart Home applications which need reduced human intervention like (taking inspiration of Amazon Echo here) replenishing the empty food bottles and cans, or switching between devices might also use edge analytics use case

A CHALLENGE seen in the intersection IoT with AI can be the fact that high precision ML/DL platform usually require higher processing and costlier hardware. The use cases listed above and similar upcoming use cases might depend more on how the technology solves the the size and cost of processing in the edge devices.

RECENT EXAMPLES: NVIDIA’s Jetson TX2 launched in March 2017 claims to be the 1st supercomputer on a module, a processor to bring AI to the Edge. Another example being that of Qualcomm’s deep learning SDK for devices called Zeroth Machine Intelligence Engine SDK which enables running of independent OEM’s neural network models on its Snapdragon 820 devices such as smart phones, security cameras, automobiles and drones, without any dependency to the cloud.

This post is inspired from my quora answer which first appeared here