A Lean Greentech Approach

I am a greentech enthusiast and I have been closely following the greentech VC investment landscape. The VCs like Kleiner Perkins who have had a large greentech portfolio including companies such as Bloom Energy are scaling down on greentech investment. Their current investment is not likely to get any returns close to what a VC would expect. The fundamental challenge with such greentech (excluding software) investment is that they are open ended capital-intensive; you just don't know home much time it would take to build the technology/product, how much it would cost, and how much you would be able to sell it for. The market fluctuations make things even worse. This is not only true in the case of start-ups but also true for the large companies; Applied Materials' grand plan to revolutionize thin-film solar business ended up in a bust.  

There's a different way to approach this monumental challenge.

Just look at how open source has evolved. It started out as non-commercial academia projects where a few individuals challenged the way the existing systems behaved and created new systems. These open source projects found corporate sponsors who embraced them and helped them find a permanent home. This also resulted in a vibrant ecosystem around it to extend those projects. A few entrepreneurs looked at these open source projects and built companies to commercialize them with the help of VC funding. Time after time, this business model has worked. Technologists are great at building technology, companies are great at throwing money at people, entrepreneurs are great at extending and combining existing technology to create new products, and VCs are great at funding those companies to help entrepreneurs build businesses. What VCs are not good at is doling out very large sum of money to bet on technology that doesn't yet exist.

If we need to make it work, we need a three-way relationship. People in academia should work on capital-intensive greentech technology projects that are funded by corporations through traditional grants. These projects should become available in public domain with an open source like license or even a commercial license. The entrepreneurs can license these technology, open source or not, and raise venture money to build a profitable business. The companies that are constantly contributing their greentech initiatives to public domain should continue to do so. Facebook's Open Compute project is gaining traction in its second year and Google continues to share their green data center design.

The important aspect is to differentiate technology from a product. The VCs are not that good at investing into (non-software) technology but are certainly good at investing into products. For many greentech companies, technology is a key piece such as a battery, a specific kind of a solar film, a fuel cell etc. Commercializing this technology is a completely different story. This requires setting up key partnerships such as eBay's new data center using Bloombox and Israeli government committing to a nationwide all-electric car infrastructure with Better Place.

Many large companies have set up their incubators or "labs" to find something that is fundamentally disruptive that could help their business. Later, there have been a very few success stories of these incubators or labs because the start-up world is way more efficient to do what big companies want to do. These labs are also torn between technology and products. My suggestion to them would be to go back to what they were good at - hiring great scientists from academia and working with academia on the next-generation technology to create a business model by either using that technology in your products or to license it to others who want to build business. This shifts the investment from a few VCs to a relatively large number of corporations.

What we really need is a lean greentech approach.

Photo Courtesy: Kah Wai Lin

Role Of Analytics In Creating New Consumer Behaviors

I am in India visiting a large customer who has heavily invested into organized retail stores, a relatively new category for the Indian market. Their head of analytics shared some details of their last promotion with me. They ran an email promotion to send out coupons that were valid on one and only one day -15th August, the Independence Day of India, which is a holiday in the country. They were really bold to take out a page-long ad in all large newspapers on the 15th August highlighting this promotion.

Their sales, in all regions, soared on that day. It not only soared but broke all their previous records. They registered the highest sale in that year which was more than the Diwali sale. In the American terms, they managed to sell more on 4th July than on the Black Friday. This shocked me. I analyzed their efforts further to better understand this behavior.

Indians in India don't drink beer, barbecue, or watch fireworks on the Independence Day. In fact they don't do anything. It's just another day except that you don't go to work and kids don't go to school. That was the key. Since they didn't have anything else to do they went to the store and shopped. They bought things they were contemplating to buy for some time. This is where coupons helped and they also ended up buying things they didn't need. Yes, they are quickly learning from Americans.

What amazed me the most that the company manufactured this behavior that was analytics-led. They studied all kinds of data, created a promotion, made sure that they can execute on their promotions, and customers came. And, they are using this data to further refine their promotions and store inventory.

Big Data and analytics are not only useful to instrument existing customers' behavior but they could also help create new customer behaviors. This is especially powerful when the company is in high growth mode and has a bold vision to do whatever it takes to gain a top position in the market.

As I blog this, Indian government just changed their policy to allow up to 51% of foreign direct investment (FDI) into multi-brand organized retail sector. India has miles to go before the organized retail sector shapes up; Indians still prefer to shop at mom and pop stores and not at a large organized Walmartish store. Due to lack of a mature organized retail sector the (Indian) companies don't have a pre-conceived bias on how to run a large brick and mortar store - that's a good thing. They are not localizing a global brand. They are creating a new brand, and hence new consumer behavior, from ground up. And, analytics has been playing a key role than ever before.

Photo courtesy: McKay Savage

Designing The Next-generation Review And Recommendation System

It's unfortunate that despite of the popularity of social networks and plenty of other services that leverage network effects, the review and recommendation systems that are supposed to help users make the right decisions haven't changed much.

Thumbs-up and thumbs-down or likes and unlikes signal two things: popularity and polarization. If a YouTube video has 400 thumbs-up and 500 thumbs-down it means that the video is popular as well as polarized, but it doesn't tell me whether I will like it or not. The star review system also signals two things - on average how good something is and whether it's significant or not. There are multiple problems with this approach. An item with 8 reviews, all 5 stars, could be really bad compared to an item that has 300 reviews with 3.5 stars. Star ratings alone, without associated descriptive reviews, wouldn't make much sense if there aren't enough people who have reviewed the item. Also, relying on an average rating alone could also be problematic since it lacks the polarization element. On top of it, the review and likes could be gamed.

Pandora's as well as Netflix's recommendations are a good example of using collaborative filtering to fine tune recommendations based on user preferences. The system aggregates the overall likes and dislikes and combines that with your taste profile and a few killer algorithms to recommend what you might like. If designed well and if it has large user population, it does work. But, the challenges with such system are missing descriptive reviews and lack of ability to perform any analysis on it. If I dislike a song on Pandora, it doesn't mean the song is bad in the absolute sense. It simply means it doesn't match my taste profile. This isn't entirely true if I dislike a blender. In this case, a descriptive context is more meaningful such as I don't like this blender because it doesn't crush spinach well. People who care to make smoothies and crush ice may not care about this issue. But, these consumers have to wade through large number of reviews to determine the product fit.

E-commerce sites review systems use the same descriptive as well as non-descriptive review systems, commonly used at all places on the internet, without any significant modifications, even if the expected investment of a user is much higher on their site. If I don't like a song, I can skip it. If I don't like a YouTube video, I can stop watching it and now if I don't like a movie I can stop streaming it. This does not apply in the traditional world of e-commerce. I absolutely need to make sure that I buy something that I like. Returning an item is a far more involved process than stop watching a movie. It's an exception, not a norm.

Word of mouth and passive buying

People shop in two ways: 1) they look for a specific product, research for it, and buy it. 2) they come across a product while not looking for it, like it, and buy it.

The second way of shopping, passive buying, is as important as active buying. There are many companies with a business model built around this impulse or "serendipitous commerce", but they don't leverage collaborative filtering. I would happily read reviews of products written by my friends and people that I trust regardless of whether I'm looking for those products or not. Think of it as Disqus-style aggregated reviews by people that I trust in my social graph. This is like an online version of a cocktail party conversation where someone is raving about a new phone that he just bought. I'm not looking for a phone, but I might, in a few days. This could create new interest or expedite my decision process. This isn't done well in the online world.

The word of mouth is still by far the best system for following recommendations. I invariably watch movies that my brother recommends to me and one of my friends will read all the books that I recommend to her. I have non-transactional relationship with my friends and family.

Contextualized long tail 

One of my favorite things, when I travel (leisure or business), is to try out at least one or two recommended Indian restaurants to see how Indian food compares from city to city and country to country (so far my vote for the best Indian food outside of India goes to London). While researching for a restaurant, I typically read all the reviews that I can find. Some reviewers are Indians and some are not. Also, for the reviews written by non-Indians, some are new to Indian food and some are not. In most cases people don't identify who they are and I end up guessing based on their username, description etc. These reviews, positive or negative, don't help me much to narrow down which restaurant I should try out.

I have always found the best food at the most unusual places. All sophisticated recommendation systems would fall short of helping me find such an unusual place. These places are not the hits. They are the long tail. Getting to this long tail isn't an easy process - a lot of asking around, digging for reviews, trying out a few awful places etc.

Privacy concerns and connected identities

As the debate between anonymity and identity continues, there has been a little or no effort to get to the middle-ground, a connected identity. As a marketer I don't care who Jane is in its absolute sense but I am interested in what she likes and dislikes based on her collective and aggregated behavior across the Internet and beyond. This is not an easy system to build and consumers won't sign up for this unless there's a significant value for them. The popularity of social networks is an example where even if users are arguably upset about their privacy they still use it since the value that they receive far outweighs their concern. And remember the social networks follow the power laws. As more and more people use it the network becomes more and more valuable to the users.

Why not design review and recommendation systems that are based on connected identities? Users don't want ads, the marketers do. If companies can focus on building good products, incentivize users to write reviews, and rely on great recommendation systems to connect the right users with right products they wouldn't need ads. The marketers are chasing the illusion of targeting the right users but the inconvenient truth is that it's incredibly hard to find those users and if they do find them, they don't really want ads. What they really want is value for their money. That is the inherent conflict between the marketers and end users.

Using connected identities beyond reviews and recommendations

Connected identities are also useful beyond reviews and recommendation systems. Comcast support is one of those examples where using connected identities could greatly improve their customer support.

Comcast started using Twitter early on to respond to customers' support issues. It was a novel concept in the beginning and they really understood Twitter as an effective social media channel, but lately that model has turned out to be as bad as their phone customer support. When I tweet to @comcastcares someones gets back to me asking who I am and what issues I have. You follow me, I follow you, you DM me, I DM you my info, and after few minutes, we are nowhere close to resolving the issue. What if Comcast allowed me to attach my Twitter account to my Comcast profile? I will OAuth that, for sure. When I tweet, they exactly know who I am, what problem I am experiencing, and how they might be able to help me. This is an example of using a connected identity without compromising privacy. Comcast knows their customer's billing information; it's transactional information. But they attempt to use Twitter to communicate with you without connecting these two identities.

I don't want to "like" Comcast or "follow" Comcast to be a victim of their spam and indifference. Comcast is easy to pick on, but there are plenty of other examples where connected identities could be useful.

Users don't like to be sold at, but they do want to buy. Let's build the next-generation review and recommendation system to help them.

Applying Moneyball To Cricket

What if a cricket team gets two batsmen to replace Sachin Tendulkar and still collectively get 100 runs out of them or have two not-so-great bowlers to replace Shane Warne and still get the other side out? If an eventual goal is to score, say 300+ runs in an ODI match does it matter how the runs are scored? What if you could find four players scoring 50 runs each instead of counting on Sehwag and Tendulkar types to score a century and lose miserably when they don't?

This is not how people think when it comes to cricket. That's also not how people used to think when it came to baseball until Billy Beane applied radical thinking to baseball, sabermetrics, now popularly known as Moneyball. On Base Percentage (OBP) became one of the most important metrics since then.

As yet another provocative aspect of Moneyball suggests, only thing that matters is whether a hitter puts a ball in play or not. Once the ball is in play the hitter does not control the outcome of that play. In cricket, when a fielder drops a catch could it be because the ball came too quickly to him, he was at the wrong position, or he was just too lame to catch it. Is there a difference between a batsman getting caught near the boundary as opposed to getting bowled? Currently, none. But, based on Moneyball, if a batsman gets caught, at least that batsman put the "ball in play." A little more practice and precision and that could have been a four or a six.

I want the cricket team selectors and captains (an equivalent of baseball general managers) to apply some of the Moneyball concepts to cricket, a sport older and more popular than baseball. In cricket, even though it's a team that wins or loses, there's typically more emphasis on the ability of an individual as opposed to measuring individuals in the capacity of how they help the team.

Bowling and batting powerplays are relatively a new concept in cricket. Skippers on either side don't have access to deep analysis of current situation and performance of opposite players in deciding when to take a powerplay. They make such crucial decisions based on their gut feeling and opinion of key players on the field. This is where data can do wonders. In baseball, managers keep a tab on an extensive set of data to make dynamic decisions such as which bullpen pitcher has a better track record against the current hitter, success of a hitter to get walks as opposed to hits etc. Most recent example is of Tampa Bay Rays aggressively using field shifting against powerful lefties, a practice that most baseball franchises still don't use or approve of.

In cricket, right handed bowlers switch from over the wicket to round the wicket mostly when whatever they are trying is not working. These decisions are not necessarily based on any historic data. In this case, it could be as simple as gathering and analyzing data about which batsmen have poor performance when bowled round the wicket as opposed to over the wicket. In baseball, using a left-handed pitcher against a left-handed hitter and using a right-handed pitcher against a right-handed hitter have proven to work well in most cases (with some exceptions). That's why there are switch hitters in baseball to take this advantage away from a pitcher. Why are there no switch hitters in cricket?

Why can't there be a dedicated bowler to finish the last over of the cricket match just like a closer in baseball? Imagine a precision bowler — a batsman who is trained as a "closer" — whose job is to throw six deliveries, accurately at a spot, fast or slow.  The regular bowlers are trained to bowl up to 10 overs, 6-8 at once, with a variety of deliveries (pitches) and a mission to stop batsmen from scoring runs and getting them out. A closer would only have one goal: stop batsmen from scoring. Historically, there have been a very few good all-rounders in cricket. It's incredibly difficult to be a great batsman as well as a great bowler, but there's a middle ground - to be a a great batsman and a closer. Some batsmen such as Sachin Tendulkar have been good at bowling off and on when the regular bowlers get in trouble (an equivalent of a reliever in baseball), but invariably their task becomes getting a wicket to break the partnership. Even if wickets are important, in most cases, it's the ability to stop the opposite team from scoring in the last couple of overs brings team a victory.

There is just one baseball, but there's no one cricket. The game of cricket differs so much from a test match to one day international (ODI) to Twenty20. But, a fresh look at data and analysis on what really matters and courage to implement those changes could do wonders.

Data Scientists Should Be Design Thinkers

World Airline Routes

Every company is looking for that cool data scientist who will come equipped with all the knowledge of data, domain expertise, and algorithms to turn around their business. The inconvenient truth is there are no such data scientists. Mike Loukides discusses the overfocus on tech skills and cites DJ Patil:

But as DJ Patil said in “Building Data Science Teams,” the best data scientists are not statisticians; they come from a wide range of scientific disciplines, including (but not limited to) physics, biology, medicine, and meteorology. Data science teams are full of physicists. The chief scientist of Kaggle, Jeremy Howard, has a degree in philosophy. The key job requirement in data science (as it is in many technical fields) isn’t demonstrated expertise in some narrow set of tools, but curiousity, flexibility, and willingness to learn. And the key obligation of the employer is to give its new hires the tools they need to succeed.

I do agree there's a skill gap, but it is that of "data science" and not of "data scientists." What concerns me more about this skill gap is not the gap itself but the misunderstanding around how to fill it.

There will always be a skill gap when we encounter a new domain or rapidly changing technology that has a promise to help people do something radically different. You can't just create data scientists out of thin air, but if you look at the problem a little differently — perhaps educating people on what the data scientists are actually required to do and have them follow the data science behind it — the solution may not be that far-fetched as it appears to be.

Data scientists, the ones that I am proposing who would practice "data science" should be design thinkers, the ones who practice design thinking. This is why:

Multidisciplinary approach

Design thinking encourages people to work in a multidisciplinary team where each individual team member champions his or her domain to ensure a holistic approach to a solution. To be economically viable, technologically feasible, and desirable by end users summarizes the philosophy behind this approach. Without an effective participation from a broader set of disciplines the data scientists are not likely to be that effective solving the problems they are hired and expected to solve.

Outside-in thinking and encouraging wild ideas

As I have argued before, the data external to a company is far more valuable than the one they internally have since Big Data is an amalgamation of a few trends - data growth of a magnitude or two, external data more valuable than internal data, and shift in computing business models. Big Data is about redefining (yet another design thinking element, referred to as "reframing the problem") what data actually means to you and its power resides in combining and correlating these two data sets.

In my experience in working with customers, this is the biggest challenge. You can't solve a problem with a constrained and an inside-out mindset. This is where we need to encourage wild ideas and help people stretch their imagination without worrying about underlying technical constraints that have created data silos, invariably resulting into organization silos. A multidisciplinary team, by its virtue of people from different domains, is well-suited for this purpose.

What do you do once you have plenty of ideas and a vision of where you want to go? That brings me to this last point.

Rapid prototyping

Rapid prototyping is at the heart of design thinking. One of the common beliefs I often challenge is the overemphasis on perfecting an algorithm. Data is more important than algorithms; getting to an algorithm should be the core focus and not fixating on finding the algorithm. Using the power of technology and design thinking mindset, iterating rapidly on multiple data sets, you are much likely to discover insights based on a good-enough algorithm. This does sound counterintuitive to the people that are trained in designing, perfecting, and practicing complex algorithms, but the underlying technology and tools have shifted the dynamics.

Learn To Fail And Fail To Learn

"I have never let my schooling interfere with my education" - Mark Twain

In a casual conversation with a dad of an eight-year old over a little league baseball game on a breezy bay area evening, who also happens to be an elementary school teacher, he told me that teaching cursive writing to kids isn't particularly a bright idea. He said, "it's a dying skill." The only thing he cares about is to teach kids write legibly. He even wonders whether kids would learn typing the same way some of us learned or they would learn tap-typing due to the growing popularity of tablets. He is right.

When the kids still have to go to a "lab" to work on a "computer" while "buffering" is amongst the first ten words of a two-year old's vocabulary, I conclude that the schools haven't managed to keep up their pace with today's reality.

I am a passionate educator. I teach graduate classes and I have worked very hard to ensure that my classes — the content as well as the delivery methods — are designed to prepare students for today's and tomorrow's world. At times, I feel ashamed we haven't managed to change our K-12 system, especially the elementary schools, to prepare kids for the world they would work in.

This is what I want the kids to learn in a school:  

Learn to look for signal in noise:

Today's digital world is full of noise with a very little signal. It's almost an art to comb through this vast ocean of real-time information to make sense out of it. Despite the current generation being digital native the kids are not trained to effectively look for signal in noise. While conceited pundits still debate whether multi-tasking is a good idea or not, in reality the only way to deal with an eternal digital workflow and the associated interactions is to multitask. I want the schools to teach kids differentiate between the tasks that can be accomplished by multitasking and the ones that require their full attention. Telling them not to multitask is no longer an option.

I spend a good chunk of of time reading books, blogs, magazines, papers, and a lot of other stuff. I personally taught myself when to scan and when to read. I also taught myself to read fast. The schools emphasize a lot on developing reading skills early on, but the schools don't teach the kids how to read fast. The schools also don't teach the kids how to scan - look for signal in noise. The reading skills developed by kids early on are solely based on print books. Most kids will stop reading print books as soon as they graduate, or even before that. Their reading skills won't necessarily translate well into digital medium. I want schools to teach the kids when to scan and how to read fast, and most importantly to differentiate between these two based on the context and the content.

Learn to speak multiple languages:

I grew up learning to read, write, and speak three languages fluently. I cannot overemphasize how much it has overall helped me. One of the drawbacks of the US education system is that emphasize on a second or a third language starts very late. I also can't believe it's optional to learn a second language. In this highly globalized economy, why would you settle with just one language? Can you imagine if a very large number of Americans were to speak either Mandarin, Portuguese, Russian, or Hindi? Imagine the impact this country will have.

A recent research has proven that bilinguals have heightened ability to monitor the environment and being able to switch the context. A recent study also proved that bilinguals are more resistant to dementia and other symptoms of Alzheimer's disease.

Learn to fail and fail to learn:

"For our children, everything they will 'know' is wrong – in the sense it won’t be the primary determinant of their success. Everything they can learn anew will matter – forever in their multiple and productive careers." - Rohit Sharma

As my friend Rohit says you actually want to teach kids how to learn. Ability to learn is far more important than what you know because what you know is going to become irrelevant very soon. Our schools are not designed to deal with this. On top of that there is too much emphasis on incentivizing kids at every stage to become perfect. The teachers are not trained to provide constructive feedback to help kids fail fast, iterate, and get better.

Our education system that emphasizes on measuring students based on what and how much they know as opposed to how quickly they can learn what they don't know is counterproductive in serving its own purpose.

Learn to embrace unschooling:


Peter Thiel's 20 under 20 fellowship program has received a good deal of criticism from people who are suggesting that dropping out from a college to pursue entrepreneurship is not a good idea. I really liked the response from one of the fellows of this program, Dale Stephens, where he discusses unschooling. He is also the founder of UnCollege. Unschooling is not about not going to school but it's about not accepting the school as your only option. Lately if you have looked at the education startups, especially my favorite ones — Khan Academy, Coursera, and Codeacademy — you would realize the impact of technology and social networks on radically changing the way people learn. Our schools are neither designed to comprehend this idea nor to embrace it. This is what disruption looks like when students find different ways to compensate for things that they can't get from a school. This trend will not only continue but is likely to accelerate. This is a leading indicator suggesting that we need a change. Education is what has made this country great and it is one of the main reasons why skilled immigrants are attracted to the US. Let's not take it for granted, and let's definitely not lose that advantage.


Originally, I had written this as a guest post for Vijay Vijayasankar's blog

Photo courtesy: BarbaraLN

With Yammer, Microsoft Begins Its Journey From Collaborative To Social

Confirming what we already knew, today Microsoft announced they are acquiring Yammer for $1.2 billion in cold cash. Here's a blog post by David Sacks, the CEO of Yammer.

Microsoft doesn't report a revenue breakdown for their individual products but SharePoint is believed to be one of the fastest growing products with annual revenue of more than $1 billion. Regardless of how Microsoft markets and positions SharePoint, it has always been collaboration software and not really social software. Microsoft does seem to understand the challenges it faces in moving their portfolio of products to the cloud, including SharePoint. Microsoft also understands value of having end users on their side even though SharePoint is sold as enterprise software. Microsoft's challenges in transitioning to the cloud are similar to the ones faced by other on-premise enterprise software vendors.

But, I really admire Microsoft's commitment by not giving up on any of these things. Skype's acquisition was about reaching those millions of end users and they continue to do that with their acquisition of Yammer. Going from collaborative to social requires being able to play at the grassroots level in an organization as opposed to a top down push and more importantly being able to create and leverage network effects. It's incredibly difficult to lead in with an on-premise solution retrofitted for cloud to create network effects. Native cloud solutions do have this advantage. Yammer will do this really well while helping Microsoft to strengthen SharePoint as a product and maintain its revenue without compromising margins. If Microsoft executes this well, they might unlock a solution for their Innovator's Dilemma.

With Yammer, Microsoft does have an opportunity to fill in the missing half of social enterprise by transforming productivity silos into collaborative content curation. As a social enterprise software enthusiast, I would love to see it happen, sooner rather than later.

Instagram: $1B. Yammer: $1.2B.And that, my friends, is why you do enterprise software.
— Aaron Levie (@levie) June 15, 2012

At personal level, I am excited to see the push for social in enterprise software and a strong will and desire to cater to the end users and not just the decision makers.  I hope that more entrepreneurs recognize that enterprise software could be social, cool, and lucrative. This also strengthens market position for the vendors such as Box and Asana.

It's impressive what an incumbent can do when they decide to execute on their strategy. Microsoft is fighting multiple battles. They do have the right cards. It's to be seen how they play the game.