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A Simple How To For Innovation

 

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Extracted from an interesting short piece by Christian Madsbjerg is the author of SENSEMAKING: The Power of the Humanities in the Age of the Algorithm:

Silicon Valley needs to get schooled

Silicon Valley is getting antsy. It’s been awhile since we were collectively wowed by the next big thing. The iPhone is ten years old. Uber is eight. The problem isn’t a lack of ideas. As engineers keep breaking new ground, it seems like anything will be possible soon. Why aren’t more of these technologies breaking through to our everyday lives?

What Silicon Valley is missing is an understanding of people—what is meaningful to them, the way they live their day to day lives, what would make a difference for them on an ordinary Tuesday in Phoenix or Shanghai. There is a dearth of deep, nuanced cultural knowledge

From my experience working with major corporations, I would say that technological advancements are only half of the picture. Knowing how to build things is great, but if you have no idea for whom you’re building them—how these inventions will connect with people’s aspirations and challenges—you will fail, no matter how many coding geniuses and data scientists you employ.

If you, like me, are a reader of great novels, you know that almost visceral sensation when you come to understand the world of someone else – the suffering of an Afghan woman, enduring abuse and horrendous conditions to spear her loved ones, or the drab misery of life as an IRS clerk in middle America, someone who had always imagined his life would turn out differently. Literatures—like in-depth journalism, plays, music, art, and even activities like cooking—can put you in the shoes of people unlike you in profound, empathetic way. But the importance of these activities is under attack from the big data-mindset that has invaded both Silicon Valley and many of the world’s biggest corporations.

Spend a few days immersed in a great novel by Tolstoy or with the work of Greek scientist and poet Ptolemy and one is forced to acknowledge that nothing is ever entirely disrupted nor is anything ever completely new. Learning does not function independently of what has come before, but rather in dialogue with it. If executives at Google had taken some time to contemplate this fact, they might have avoided the disastrous rollout to their Google Glass product in 2014. The technology itself functioned just fine. In a narrow Silicon Valley perspective, Google Glass might be considered a successful technology. But when does a piece of technology ever exist independent of a world, a societal structure and culture? Yes, the glasses “worked” but did they belong? Google Glass wearers were dubbed “Glassholes” and people shunned Google Glass wearers at social events. Silicon Valley may have new technology, but in this instance it failed at the much larger challenge of understanding how people relate to one another.

When we use a skill set based in the humanities to understand the world, we gain insight into these deeper issues. And these are the factors that actually drive business forward. Let’s return to China: one by one, the world’s biggest and most cutting edge Silicon Valley companies—Yahoo, eBay, MySpace, Facebook, Twitter, Groupon, and, finally Google—have attempted to develop a meaningful market there. They have come armed with all of the best technical knowledge along with plenty of cash and intellectual property. And yet, today, Internet market leaders in China are still local: Alibaba, Baidu and TenCent.

Technical superiority is a very small part of this story. Limited by their “Silicon Valley” state of mind, American companies simply had no feel for the nuances that made the Chinese marketplace different. With a deeper immersion into the lives of Chinese consumers as well as into their literature, history and religion, technologists might have grasped the more subtle differences between professional and personal network building in Chinese society

When we stop valuing culture, we become blind to the very opportunities that drive “world changing” technology to mass adoption. The greatest challenges and opportunities of the twenty-first century are cultural, not algorithmic. And the greatest tools for the study and understanding of culture exist within the wealth of theories and methodologies that make up the humanities.

To those of you with a liberal arts degree, I say this: your skills are essential in today’s world, and more companies need to recognize that. To those of you with a STEM degree (or who never bothered with college in the first place), I would say: pick up a book or two every month. Go to plays. Travel and immerse yourself in a culture unlike your own.

Without a deep, empathetic understanding of other people, turning that good idea into the next big thing may prove elusive.

End

 

The original article may be read here.

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Innovation Stahl.jpg

Thus concludes Greg Satell in his efforts to find out what is common to successful companies known for their innovation. He finds in his study so many opposites in their approaches that he cautions ‘Here’s Why You Should Think Twice Before Listening To Business Gurus‘.

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to make it as easy and pleasant for the folks.

KRBlog A T and T Transaction

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Of course, at the same time not going short on clarity.

I made this [letter] very long only because I have not had the leisure to make it shorter” (Blaise Pascal)

Torrey Podmajersky is one of those writing words inside Microsoft products. A couple of examples from him on writing using fewer words (lightly edited from here):

Microsoft product example:

Here is an example where a teacher has just created a new class in Microsoft Classroom. There are a bunch of permissions that help keep the school system secure and running smoothly, and those take a while to complete. But we don’t want to show the teacher a blank screen! So we initially wrote:

few-words-1

Screenshot of Microsoft Classroom in-progress design. Screen text says “Making something special takes time! We’re working to get your classes ready. Please check back soon.”

(These images are from design drafts. Microsoft Classroom is currently in preview.)

The title could be read at least two ways: We either are demonstrating enthusiasm that the teacher’s class is special! Or, we are defensive that it’s taking so long! The text communicates to the teacher what’s happening: we’re working to get your classes ready. It will take an indeterminate amount of time, but isn’t immediate – unavoidable, on our side: the delay time depends on how their school set up their system. We’ll just get the class data back when the system is ready.

So here’s the text I recommended:

Few Words 2.jpg

Screenshot of Microsoft Classroom in-progress design, after I reduced the words. Screen text says “Almost ready

There’s no more information to tell the teacher except that it will be ready soon. There’s nothing they can do but wait, and check back later.

Teachers are astoundingly short on time. Why make them read more? We don’t have to tell teachers that their classes are special, nor that our product will be special.

Life or death example: Airplane safety placard

I’ve never been in an airplane as it made a water landing. But I have told flight attendants that I would be willing to open the emergency door, if I were asked to.

I’ve even imagined being in that state: panicked, but still alive; adrenaline coursing; heart pounding. Even as a word-savvy person, this is not the moment I’ll stop, read, and understand with great clarity.

On a Boeing 787 Dreamliner, I took this picture of the door:

Few Words 3.jpg

Interior door of a Boeing 787 Dreamliner, where a label says “VISUALLY ENSURE THE MODE SELECT HANDLE IS FULLY INSIDE THE RED PLACARD FOR ARMED AND GREEN PLACARD FOR DISARMED”

The label on the door has 19 words: “Visually ensure the mode select handle is fully inside the red placard for armed and green placard for disarmed”

My rewrite uses 11 words…

Few words 4.jpg

…but this gets dangerous. Personally and professionally, I have no idea what it means for a door to be armed or disarmed. I estimate that 99% of people on a commercial 787 flight don’t know, either. If I were a UX writer for Boeing, I’d ask: What should most people understand when they read these words? Could we label the red and green areas directly, and serve people who are red/green colorblind, too?

Using fewer words isn’t a panacea to fix every user experience; it’s just one guideline, together with all the others employed by excellent writers, designers, developers, program managers, and researchers. It’s how UX writers reduce the text to create experiences that let people to do more of what they want to do — not waste their time reading explanations of how to do it.

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Here’s the latest addition to the lore of what wonders could be wrought through end-point empowerment. Obviously no procedure manual would be able to cover even a small fraction of the large number of field scenarios that occur in real world of customer service. Remains largely unpredictable.

Here we go:

(Lightly edited for readability and conciseness from here – there was no way to reblog the article entirely from its source)

The IndiGo Way of Delighting Customers – A Case Study

indigo-1

“Excuse me, khane mein kya hai?” (“Excuse me, what are the meal options?”), asked the elderly gentleman seated with his wife just one row behind me. The question was directed to an airhostess of an Indigo flight to Pune from Kolkata (India) on a July 2016 evening. All the passengers who had a pre-booked meal, or wanted to purchase on-board, had already been served with their choice of food and beverage, and the cabin crew were busy with cash consolidation and preparing to clean up the deck.

Unlike the passengers around, I was not really taken aback by the loud and out-of-protocol address, as I was already afflicted with the couple’s high pitched conversations in Marathi and Hindi throughout the first hour of the flight. It seemed they were not used to flights. They had even interacted so audibly with their immediate neighbor, an old formally dressed man seating by the aisle seat that I knew they hailed from Satara, returning after spending some time with their newly born grandson at their son’s place at Gangtok (Sikkim). Their son had booked the journey tickets for them, the first leg of which was from Bagdogra to Kolkata, and here they were on their last part of the trip.

“Can I see your boarding pass, Sir?” asked the air hostess politely.

“Here it is”, said the elderly gentleman in a Marathi accented Hindi and extended a card to her.

“Sir, this is the one for the Bagdogra-Kolkata sector, can I please have the pass for this sector?”

To this the man seemed visibly unsettled, searching for the right card with continuous ramblings in Marathi. His wife joined the commotion with “Just see how heartless they are, we haven’t eaten anything since lunch.” The gentleman found the right card and handed it over to the lady in uniform.

As I was finishing my drink over a gripping novel, I paused for a moment to watch the drama happening live beside me.

“Sorry sir, you do not have a meal booked for this sector. You had one in the flight from Bagdogra. However, if you wish, you can now purchase any food or drinks”. The standard pitch.

“Yeh kaise ho sakta hai? Plane mein khana milna hai to? Pehla flight mein bhi diya tha?!” (“How come that’s possible? Planes serve meals, isn’t it? We were served food in the first flight!”), stated the gentleman with a demeanor that said won’t-pay-whatever-hell-comes-up-you-better

The lady excused herself for a quick whisper with her senior, handing over the boarding pass to her.

indigo-2

The lead lady, trained to expect the unexpected, came to the spot in quick time and was straight to the point, “Sir, what would you like to have?”

Seriously, none of the nearby passengers including me was expecting this.

“Dekha? Maine bola tha na?” (“See? I had told you!”), the man said with a smile, oblivious that he was going to receive a free meal. “What do you have in the meals?”

The no-fuss actions that followed next were heart-warming. The lead lady served them 2 sets of sandwiches and mixed fruit beverages with a smile and a wish, “Enjoy your meals, Sir!”.

The couple happily gorged themselves on the food over a high-pitched conversation in Marathi.

I returned to my novel.

Even though the sentences in the book were running in front of my eyes, my mind was absorbed in something else. I was reminded of a talk by Subroto Bagchi, co-founder of Mindtree Ltd…his point was on the right mix of process along with empathy in building and running an organization. All problems of the world can’t be solved by following the right process, unless you have an empathy element to back it up. It becomes particularly important when one deals with the most important aspect of one’s job, people.

If our lead lady had adhered to the laid down process, she would have rightfully refused to oblige the old couple with food packets. That was we had expected out of her. But when she decided to exercise her acumen of empathy, it suddenly made more business sense to all of us…Probably Indigo lost INR 500 (peanuts compared to their daily transactions) as a result, but what they gained was vastly in excess. It satisfied two old people without hassles, averted a possibly ugly scene, created many appreciating passengers, and made me write this blog lauding them.

“Process is not a substitute for building an emotionally rich organization. Process without emotion can quickly bring you down to the lowest common denominator.”

Subroto Bagchi, Co-founder, MindTree Ltd

Let’s not lose sight of the key enabler here: Indigo’s empowerment of its field staff – the end-point delivering the service – that encouraged the lady to make the gesture she did.

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Source: Amit Dey, Deputy Manager, Learning & Development | HR at EXL at linkedin.com. And thanks to Anshuman Deshmukh, HR Manager at Genesys International for bringing the article to my notice.

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And no maths, no equations.

This is from an article that appeared sometime ago edited for readability, deleting (irritating, light-weight and thoroughly avoidable) references to some IT specific code snippets and operations. Here you go:

How the Circle Line rogue train was caught with data

The MRT Circle Line (London underground) was hit by a spate of mysterious disruptions in recent months, causing much confusion and distress to thousands of commuters.

Like most of my colleagues, I take a train on the Circle Line to my office at one-north every morning. So on November 5, when my team was given the chance to investigate the cause, I volunteered without hesitation.

From prior investigations by train operator SMRT and the Land Transport Authority (LTA), we already knew that the incidents were caused by some form of signal interference, which led to loss of signals in some trains. The signal loss would trigger the emergency brake safety feature in those trains and cause them to stop randomly along the tracks.

But the incidents — which first happened in August — seemed to occur at random, making it difficult for the investigation team to pinpoint the exact cause.

We were given a dataset compiled by SMRT that contained the following information:

  • Date and time of each incident
  • Location of incident
  • ID of train involved
  • Direction of train

We started by cleaning the data…

This gave us:

picture-1Screenshot 1: Output from initial processing

No clear answers from initial visualisations

We could not find any obvious answers in our initial exploratory analysis, as seen in the following charts:

  1. The incidents were spread throughout a day, and the number of incidents across the day mirrored peak and off-peak travel times.

picture-2Figure 1: Number of occurrences mirror peak and off-peak travel times.

  1. The incidents happened at various locations on the Circle Line, with slightly more occurrences on the west side.

picture-3Figure 2: The cause of the interference did not seem to be location-based.

  1. The signal interferences did not affect just one or two trains, but many of the trains on the Circle Line. “PV” is short for “Passenger Vehicle”.

picture-4Figure 3: 60 different trains were hit by signal interference.

 

The Marey Chart: Visualising time, location and direction

Our next step was to incorporate multiple dimensions into the exploratory analysis.

We were inspired by the Marey Chart, which was featured in Edward Tufte’s vaunted 1983 classic The Visual Display of Quantitative Information. More recently, it was used by Mike Barry and Brian Card for their extensive visualisation project on the Boston subway system:

In this chart, the vertical axis represents time — chronologically from top to bottom — while the horizontal axis represents stations along a train line. The diagonal lines represent train movement.

Under normal circumstances, a train that runs between HarbourFront and Dhoby Ghaut would move in a line similar to this, with each one-way trip taking just over an hour:

picture-5Figure 5: Stylised representation of train movement on Circle Line

Our intention was to plot the incidents — which are points instead of lines — on this chart.

Preparing the data for visualisation

With the data processed, we were able to create a scatterplot of all the emergency braking incidents. Each dot here represents an incident. Once again, we were unable to spot any clear pattern of incidents.

picture-6Figure 6: Signal interference incidents represented as a scatterplot

Next, we added train direction to the chart by representing each incident as a triangle pointing to the left or right, instead of dots:

picture-7Figure 7: Direction is represented by arrows and colour.

It looked fairly random, but when we zoomed into the chart, a pattern seemed to surface:

picture-8Figure 8: Incidents between 6am and 10am

If you read the chart carefully, you would notice that the breakdowns seem to happen in sequence. When a train got hit by interference, another train behind moving in the same direction got hit soon after.

What we’d established was that there seemed to be a pattern over time and location: Incidents were happening one after another, in the opposite direction of the previous incident. It seemed almost like there was a “trail of destruction”…

Could the cause of the interference be a train — in the opposite track?

picture-9Figure 9: Could it be a train moving in the opposite direction?

We decided to test this “rogue train” hypothesis.

We knew that the travel time between stations along the Circle Line ranges between two and four minutes. This means we could group all emergency braking incidents together if they occur up to four minutes apart.

We found all incident pairs that satisfied this condition: We then grouped all related pairs of incidents into larger sets…This allowed us to group incidents that could be linked to the same “rogue train”…These were some of the clusters that we identified:

[{0, 1},
{2, 4},
{5, 6, 7},
{8, 9},
{18, 19, 20},
{21, 22, 24, 26, 27},
{28, 29, 30, 31, 32, 33, 34},
{42, 44, 45},
{47, 48},
{51, 52, 53, 56}]

Next, we calculated the percentage of the incidents that could be explained by our clustering algorithm. The result was:

(189, 259, 0.7297297297297297)

What it means: Of the 259 emergency braking incidents in our dataset, 189 cases — or 73% of them — could be explained by the “rogue train” hypothesis. We felt we were on the right track.

We coloured the incident chart based on the clustering results. Triangles with the same colour are in the same cluster.

picture-10Figure 10: Incidents clustered by our algorithm

How many rogue trains are there?

As we showed in Figure 5, each end-to-end trip on the Circle Line takes about 1 hour. We drew best-fit lines through the incidents plots and the lines closely matched that of Figure 5. This strongly implied that there was only one “rogue train”.

picture-12Figure 11: Time of clustered incidents strongly implies that the interference could be linked a single train

We also observed that the unidentified “rogue train” itself did not seem to encounter any signalling issues, as it did not appear on our scatter plots.

Convinced that we had a good case, we decided to investigate further.

Catching the rogue train

After sundown, we went to Kim Chuan Depot to identify the “rogue train”. We could not inspect the detailed train logs that day because SMRT needed more time to extract the data. So we decided to identify the train the old school way — by reviewing video records of trains arriving at and leaving each station at the times of the incidents.

At 3am, the team had found the prime suspect: PV46, a train that has been in service since 2015.

Testing the hypothesis

On November 6 (Sunday), LTA and SMRT tested if PV46 was the source of the problem by running the train during off-peak hours. We were right — PV46 indeed caused a loss of communications between nearby trains and activated the emergency brakes on those trains. No such incident happened before PV46 was put into service on that day.

On November 7 (Monday), my team processed the historical location data of PV46 and concluded that more than 95% of all incidents from August to November could be explained by our hypothesis. The remaining incidents were likely due to signal loss that happen occasionally under normal conditions.

The pattern was especially clear on certain days, like September 1. You can easily see that interference incidents happened during or around the time belts when PV46 was in service.

picture-13LTA and SMRT eventually published a joint press release on November 11 to share the findings with the public.

Final thoughts

When we first started, my colleagues and I were hoping to find patterns that may be of interest to the cross-agency investigation team, which included many officers at LTA, SMRT and DSTA. The tidy incident logs provided by SMRT and LTA were instrumental in getting us off to a good start, as minimal cleaning up was required before we could import and analyse the data. We were also gratified by the effective follow-up investigations by LTA and DSTA that confirmed the hardware problems on PV46.

From the data science perspective, we were lucky that incidents happened so close to one another. That allowed us to identify both the problem and the culprit in such a short time. If the incidents were more isolated, the zigzag pattern would have been less apparent, and it would have taken us more time — and data — to solve the mystery.

Of course, we were most pleased that all of us can now take the Circle Line to work with confidence again.

Daniel Sim, Lee Shangqian and Clarence Ng are data scientists at GovTech’s Data Science Division.

 

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Source: https://blog.data.gov.sg/how-we-caught-the-circle-line-rogue-train-with-data-79405c86ab6a

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