Analytics-first Enterprise Applications
This is the story of Tim Zimmer who has been working as a technician for one of the large appliance store chains. His job is to attend service calls for washers and dryers. He has seen a lot in his life; a lot has changed but a few things have stayed the same.
The 80's saw a rise of homegrown IT systems and 90's was the decade of standardized backend automation where a few large vendors as well as quite a few small vendors built and sold solutions to automate a whole bunch of backend processes. Tim experienced this firsthand. He started getting printed invoices that he could hand out to his customers. He also heard his buddies in finance talking about a week-long training class to learn "computers" and some tools to make journal entries. Tim's life didn't change much. He would still get a list of customers handed out to him in the morning. He would go visit them. He would turn-in a part-request form manually for the parts he didn't carry in his truck and life went on. Not knowing what might be a better way to work Tim always knew there must be a better way. Automation did help the companies run their business faster and helped increased their revenue and margins but the lives of their employees such as Tim didn't change much.
Mid to late 90's saw the rise of CRM and Self-Service HCM where vendors started referring to "resources" as "capital" without really changing the fundamental design of their products. Tim heard about some sales guys entering information into such systems after they had talked to their customers. They didn't quite like the system, but their supervisors and their supervisors' supervisors had asked them to do so. Tim thought somehow the company must benefit out of this but he didn't see his buddies' lives get any better. He did receive a rugged laptop to enter information about his tickets and resolutions. The tool still required him to enter a lot of data, screen by screen. He didn't really like the tool and the tool didn't make him any better or smarter, but he had no other choice but to use it.
Tim heard that the management gets weekly reports of all the service calls that he makes. He was told that the parts department uses this information to create a "part bucket" for each region. He thought it doesn't make any sense - by the time the management receives the part information, analyzes it, and gives me parts, I'm already on a few calls where I am running out of parts that I need. He also received an email from "Center of Excellence" (he couldn't tell what it is, but guessed, "must be those IT guys") whether he would like to receive some reports. He inquired. The lead time for what he thought was a simple report, once he submits a request, was 8-10 weeks and that "project" would require three levels of approval. He saw no value in it and decided not to pursue. While watching a football game, over beer, his buddy in IT told him that the "management" has bought very expensive software to run these reports and they are hiring a lot of people who would understand how to use it.
One day, he received a tablet. And he thought this must be yet another devious idea by his management to make him do more work that doesn't really help him or his customers. A fancy toy, he thought. For the first time in his life, the company positively surprised him. The tablet came with an app that did what he thought the tool should have done all along. As soon as he launched the app it showed him a graphical view of his service calls and parts required for those calls based on the historic analysis of those appliances. It showed him which trucks has what parts and which of his team members are better of visiting what set of customers based on their skill-set and their demonstrated ability in having solved those problems in the past. Tim makes a couple of clicks to analyze that data, drills down into line-item detail in realtime, and accepts recommendations with one click. He assigns the service calls to his team-members and drives his truck to a customer that he assigned to himself. As soon as he is done he pulls out his tablet. He clicks a button to acknowledge the completion of a service call. He is presented with new analysis updated in realtime with available parts in his truck as well as in his teammates' trucks. He clicks around, makes some decisions, cranks up the radio in his truck, and he is off to help the next customer. No more filling out any long meaningless screens. His view of his management has changed for good for the very first time.
As the world is moving towards building mobile-first or mobile-only applications I am proposing to build analytics-first enterprise applications that are mobile-only. Finally, we have access to sophisticated big data products, frameworks, and solutions that can help analyze large volume of data in real time. The large scale hardware — commodity, specialized, or virtualized — are accessible to the developers to do some amazing things. We are at an inflection point. There is no need to discriminate between transactional and analytic workload. Navigating from aggregated results to line-item details should just be one click instead of punching out into a separate system. There are many processes, if re-imagined without any pre-conceived bias, would start with an analysis at the very first click and will guide the user to a more fine-grained data-entry or decision-making screens. If mobile-first is the mindset to get the 20% of the scenarios of your application right that are used 80% of the times, the analytics-first is a design that should thrive to move the 20% of the decision-making workflows used 80% of the time that currently throw the end users into the maze of data entries and beautiful but completely isolated, outdated, and useless reports.
Let's rethink enterprise applications. Today's analytics is an end result of years of neglect to better understand human needs to analyze and decide as opposed to decide and analyze. Analytics should not be a category by itself disconnected from the workflows and processes that the applications have automated for years to make businesses better. Analytics should be an integral part of an application, not embedded, not contextual, but a lead-in.
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.
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.
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
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.
