Boss Vijayasankar 3/12/04 – 12/3/2016

He was the biggest teddy bear of a pup when I picked him up from Marjorie Blake’s house in Bakersfield in April 2014 . Turned Dhanya , who was mortally scared of all dogs , into the biggest dog lover overnight 

He helped raise our daughter Shreya – she used to call him Chetta (big bro) till she was about 5🙂

He was a great big brother to Hobo , when he came home for Shreya’s 4th birthday 

He loved his toys – and food . Learned everything he needed for competitive obedience in about 4 weekends and two packs of hot dogs 

He was a sage by the time Shreya was born . He got to be a puppy again as Hobo grew up . Hobo was ten times stronger , and Boss was a hundred times smarter and wiser🙂

Along came little Ollie – Boss was already 9 by then but young enough to show the kid the ropes .

He kept alive my dream that Shreya would some day choose to handle in the show ring🙂

He loved water – and was the ultimate gold fish . I must have tossed a few thousand oranges into that pool in last decade🙂

Boss never met a stranger – he loved everyone . But for me he was my best buddy , my shadow . If I slept in – he would come find me and wake me up without fail . He loved riding shotgun with me in the SUV

He was the “boss” of the gang – from day 1 

He grew older gracefully , and we celebrated every birthday 

He was diagnosed with Hemangiosarcoma in September 2016 . He underwent surgery to get a tumor removed . The surgeon gave us three months with him , and we tried our best to make every day with him the best he had 

And today , December 3rd 2016 – he had all the ice cream , eggs and bacon he could eat . And before the vet took over – he had a giant slice of chocolate cake 

And he went to sleep on my lap , just as he did the first day I met him and brought him back on a united airlines flight back to Phoenix . 

You will always live in my heart Bossappai – you were and always will be the boss . Till we meet again , buddy ! 

Is data science doomed with Trump being elected ?

Ever since Trump won the election , the question I have faced the most from family and friends is “is predictive analytics dead?”. I also got asked if Watson would have picked the correct winner . The more savvy doubts were about how Clinton missed the trends in places like Wisconsin and Michigan .

Here are my thoughts – and pls treat them as my personal opinions only as always !
To begin with – the analytics was not all wrong , and did many things right . It also did many things wrong . Rather than saying data science  is dead , I think all it really is that it’s cloudy and some work needs to be done to make it less cloudy . 
The thing we forget the most about data science is that it is all about odds . When Nate Silver said Trump had 35% chance of winning – he meant exactly that ! Having about 2/3 chance of winning for Clinton should not have been interpreted as Clinton will win ! This problem is one I face every day with my clients too on all kinds of predictive scenarios . It’s not a binary thing as we like it to be in most cases .

That said , the predictive models all had given significant odds for Clinton and now we know something was wrong with them . So yes – data science on politics should absolutely take some significant blame for what they missed . 

To begin with – All analytics about people are hard . I wrote about it few weeks ago here . 

Models are based on history and assumptions to give them context . It’s not uncommon in this business for calibration to go out of whack  – usually because context changes , but the model continues to depend on old assumptions . Since all public analysis of this election trended the same way – I guess we can safely say that “establishment thinking” about polls needs an overhaul . 

Then there is the actual data itself that comes from polls and the bias ( like selection bias , confirmation bias etc ) that gets associated with it . I often post twitter polls to get a pulse on topics I care about – and I should know about the selection bias when I look at the results . People who collected and analyzed the data should have been way more careful about bias . 

Pollsters need to know the markets they are polling . Respondents don’t always literally say what they mean . This is nothing new – any kind of market research would have run into this scenario and there are ways to get around it . When I have done collection and analysis about foreign markets using folks who are technical experts , but largely ignorant of those markets – I have always had poor results . I have a feeling that a lot of polling was “lazy” this time around in election season . For example – if your call list only has landline numbers , you won’t know what I have to say ( I haven’t had a land line for quite some time and I am hardly alone in that ). 

Weather forecasting is something we are all familiar with since it’s been around for a long time . However , our ability to accurately predict beyond the next week or ten days is actually not that high . Little events can change weather big time.  If we extend that thought to how the sex tapes and FBI actions all came back to back – we probably can have some sympathy for the statisticians who had to deal with the data . 

Even if all the models worked well , late happening events – like FBI director’s two notes to Congress – don’t leave a lot of room to actually act on what the model tells you . We were recently working on predictive maintenance solution at a client . The maintenance VP was very clear that if all I can give him is a 2 day window with failure prediction  , there isn’t a whole lot he can do to avoid down time . While I don’t know for sure – I wouldn’t mind making a small bet that analytics used by Clinton campaign probably highlighted the issues of Michigan and Wisconsin , just that it was too late to do anything about it . 

I am sure I am missing several other aspects – and some technical aspects are probably too boring for most of my usual readers – but I think I have given a fair idea of the thoughts I have on this topic . I am sure you will add more , or correct me in the comments . 

Some changes in the polling and predictions industry is needed , but we just need to try to NOT throw the baby with the bath water . And while I am the biggest fan of Watson , I don’t really know for sure if it would have done better . Knowing what went wrong this time – I am sure this industry will use it to its advantage and reclaim its position quite quickly . 

Parting thought – for all my pals who think AI will take over the world soon , this might be worth noting that for foreseeable future these models will need significant human help to be useful . It’s man AND machine , and we should stop obsessing about man VS machine . 

Few more post election thoughts 

In no particular order …
1. Peaceful protests are legal and a part of our culture . It can’t be denied and it’s a much needed venting mechanism . It might also give the young people a life lesson on how to be active about things they should care about , including voting 
2. Trump won fair and square . Clinton won popular vote by just 0.2% and there is no trophy for that . Saying “Trump didn’t win, she lost” doesn’t change the truth that he won by a bigger margin than most of us ever expected .
3. Trump is the president elect now . There is only one President for the whole country . There is no such thing as “he is not my president”. Calling him names is just an insult to the office he got elected to . If we care about democracy , we should give the guy a chance unlike how GOP obstructed Obama throughout . Nobody wins if we keep obstructing for the sake of obstructing ! 
4. If you don’t like Trump or GOP , work at grass root level to get democrats to power – both in legislature as well as Whitehouse next time . If he does something crazy – let’s protest it by all means. Same for GOP – listen to people and inspire them , pls don’t resort to fear mongering as primary strategy 
5. If we consider the global economy , every thing we try to get to a stalemate is an opportunity we are giving China and others to gain an upper hand . 
6. If you really want to make a long term difference and don’t want a repeat of Trump vs Clinton type for other elections – invest in education today so that a more intelligent generation follows ours . 
7. Lets give leadership opportunities to the next generation of leaders on both parties . Let’s change what “establishment” means by driving change at all levels . Let’s bring the focus to “people” ! 
8. Social media is fun , but not representative of the nation . Let’a please not forget that many of us live in a bubble we create by selection bias 

The sun rose today, and it will rise tomorrow as well 

Like most of you , I too was up till 2 AM following the election results and experiencing it’s associated mood swings . I certainly was not a Trump supporter and didn’t expect him to win . I don’t like Clinton either – just for different reasons . Between the two , I was hoping for Clinton to win . I went through publicly available polling data over last few days and concluded , semi scientifically , that Trump at best only had a 30% or so chance to win . I was wrong – dead wrong – and so were a lot of others that I know .

I am middle of the road on my political leanings – fiscally conservative and socially liberal . I also firmly believe that in a democracy , I can’t pick and choose and apply random criteria . People of this country voted and Trump won fair and square . He now has my respect as the President , irrespective of whether I like him or not as a person . It’s difficult , but I am optimistic that it will be fine . The sun rose today , and it will rise tomorrow as well .

But there are important learnings for all of us here , well at least for me for sure🙂

1. Trump listened to the pain of the people – and Clinton and many of us did not get it . He appealed to the insecurities ( perceived or real ) and got rewarded . Rest of us didn’t – and he won . If you miss the pulse of the majority of  people you deserve to lose . Few months ago I. Atlanta – I had an African American uber driver who told me he will vote for Trump because he is against gay marriage . On a national scale , I didn’t realize how many such people exist and how ignorant we are of the “silent trump supporters” .  Social media created a bubble for many of us – real world is still far removed from social media and that is something I will never forget ever again !

2. Campaigning and governing are not the same . When you campaign , essentially you are pleading  to the majority as defined by electoral college votes . When governing you are everyone’s president and you can’t just think of the voters who took your side . It remains to be seen if Trump has any real solutions to back up his rhetoric of making America great again. I am not a fan of Obamacare – but I am not sure if he has. Ought through sufficiently on what to replace it with . Last thing we need is it to be repealed and replaced with something that doesn’t work .

3. Big solutions might be out of the box in nature . Building the wall , deporting illegals etc are not pragmatic . But then I didn’t see Narendra Modi making 1000 and 500 rupees notes illegal over night to curb black money either . Does Trump have such tricks up his sleeve ? I haven’t seen clues – but I hope he does have great big plans that will pleasantly surprise us .

4. House , Senate and President are all going to be from the same party now . Checks and balances were built into the constitution for a good reason . When democrats had all three , they passed Obamacare that was poorly written and poorly implemented . I am waiting to see if GOP will do any better when they own all parts of government

5. Trump is a business man and I am sure he understands the difference between marketing and execution . I have a lot of Muslims , LGBTs, minorities  and immigrants as my friends and I am super worried for their rights . I hope and pray their rights are not infringed like he told us in his campaign speeches . I don’t honestly think – despite my own multiple jabs – that Old Testament will replace the constitution of this country . Over time I fully expect Trump and the legislature to win our confidence and trust .

6. We should worry about each other more than about the president honestly . A nasty Election season has driven big wedges amongst us . Clinton and Trump were rich and famous and will continue to be so , and will both shake hands and move on . I worry about the rest of us being able to forgive and forget . A good start will be to stop shaming each other from today on how we voted . It’s over , and there are miles to go before we sleep !

Role Of Services In Artifical Intelligence

Please don’t interpret the following as IBM’s position – this is just my view . Also, after a long time, I am typing a blog post on my mac instead of on my iPhone🙂

I am not an official spokesperson for IBM . That said, I am not a spectator from the peanut gallery either. Almost every day for last couple of years, I have been involved in the sales and delivery of projects that include some flavor of Artificial intelligence. I am sticking with AI here instead of “Cognitive” to avoid the distraction of what is AI, ML , Cognitive etc . While I do not have an academic degree in AI, I have hands on design and coding experience in this domain, and I am learning more every opportunity I get.

Couple of weeks ago, I was in Vegas for a week – attending IBM’s World of Watson event. I spoke with tens of clients and prospects, several analysts and a bunch of my colleagues from other parts of the world that I don’t get to interact frequently. One of the common themes in this conversation was the role of services in AI. Variants of this topic also caught my attention on social media as well – mostly from analysts. The big difference between the people I spoke with was that many analysts were dismissive or pessimistic about the role of services, and clients did not seem to care what was product and what was services – they did not seem to care much about the distinction. Either way, this topic picked my interest bigly🙂

There are many reasons to be pessimistic about the role of services in AI

  1. Things that caught the imagination in past like ERP, data warehousing etc all had big services components and many projects were plagued with cost over runs. Why repeat the mistake with AI ?
  2. Most AI initiatives – from IBM, Google etc – are cloud based. Cloud should not need as much services, right ?
  3. AI in general comes with a cloud of uncertainty about the future of mankind. Almost every person I know has a strong position on Universal Basic Income thanks to this fear. Why accelerate the apocalypse by promoting services on AI?
  4. AI is in its nascent stage. Services is what hides the immaturity of the product and hence deserves a public take down. In any case, it is more cool to be on the side of product and be opposed to services.

If you are running out of patience already on what my take is – I believe services will be integral to the present and future of AI, for a long time . Here is why ( and its long – get a beer or coffee ).

The nirvana state of AI for me is when computers can mimic ( and accelerate) human thoughts instead of just human tasks. We are a long way away from that stage. But what is possible today is already good enough to help individuals and businesses in tangible ways – often with “order of magnitude” benefits. People who criticize AI as immature often do not recognize that many companies use it heavily today, and its a part of many apps on your phone. The future is already here, and its getting more evenly distributed as well🙂

A lot of people think – and I get asked almost every day – that systems like IBM Watson are big monoliths like traditional ERP systems. While the start was certainly that way , AI today is largely a set of APIs that application developers can mix and match for their unique needs. These are quite well defined APIs and even someone like me who is not a full time developer can figure out how to use it pretty quickly.

One such API is speech to text . You input the audio, and you get text as output – that is pretty much it. There is no end to the useful scenarios this “simple” API can be used in ( closed captioning in TV for example). However, there are very few people relatively who know such an API exists, and even fewer who know how to integrate into existing workflows in their business to improve efficiency and effectiveness of the process. Consultants can help their clients explore how AI can be incorporated into their existing processes. This kind of integration work is perhaps the most tactical use of AI services today.

While tactical uses are awesome, most companies have little interest in things that make incremental changes. I have a client who is in the entertainment business. The leader in their industry is about 4 times bigger than them. They are betting on AI as the way to leapfrog the leader and become the top dog in their industry. A good part of that project is the strategic planning – what bets to make, what trade offs to consider, how to mitigate risks etc. AI Advisory is probably the most exciting part of the future of consulting business. It needs a unique mix of AI knowledge , the intimate knowledge of specific industries, and business process expertise. In other words – product is not the lone differentiator.

I explain this to my clients as “It is not what we make with AI, it is what we make possible with AI. We will help you build the bridge from where you are to where you need to be” . 


There are some “tangential” scenarios too for AI in the Enterprise IT world. Between ERP and Data warehousing, countless days of my life was spent in getting data cleansed and transformed. For the most part, even with SaaS and big data – we worry a lot about GIGO ( garbage in garbage out). AI will help us take a fresh look at this problem. What we call as “garbage” data might contain many useful nuggets. We might not need to transform it row by row to get meaningful insights – AI can figure out useful patterns. Coupled with the modern big data management systems, this could make a lot of ETL disappear for scenarios where directionally correct insights are more important than precise information. It needs services – the kind that generates actual value and gets rid of a lot of terribly inefficient code. Actually not just code, also the precious human effort wasted in fixing the “garbage” data !

This post is already becoming quite long, so I will stop with making just one more topic – a favorite for both my clients and my analyst friends. Companies around the world have invested heavily in ERP systems ( and satellites for SRM, CRM etc). Those assets have not all been sweated sufficiently quite yet. Some of these vendors have modernized ( mostly about moving to cloud, and making the systems faster ). However, in the promise of “better, faster, cheaper” – the “better” part is largely unfulfilled today. I firmly believe the missing ingredient is purposeful use of AI.

Lets take the mundane case of managing collections – where optimization today means you outsource it and make it someone else’s problem. Cost of collection is not trivial for most clients I have met – but they have mostly just accepted it as a fair cost of doing business. Efficiency is improved by mostly fine tuning the process  part of it – like how many dunning cycles need to be run, but with very little personalization. We recently implemented some elementary AI ( trade off analytics, speech to text , text to speech etc) for a client who is a giant in the world of education ( took less than a month to implement it) and the client saw 20% better collections immediately after that.

Optimizing collections is really a trivial use case (and even that has tremendous ROI)  if we look at the power of AI available today. Exponentially better implementations will be possible (and already in the works) when we combine AI with other emerging stuff like say blockchain .

While I have no doubts that services will play a major role in the field of AI – I will be the first to admit that practitioners have a lot of learning and retooling to do to add value to their clients. The good consultants have always been life long learners – what is perhaps different for them is just the speed at which they need to learn now. Its a fast evolving field and there is just no time to sit back and say “I am an expert” any more.

PS: If working in AI, integrating cognitive capabilities to existing systems, Advising clients on making use of AI etc are things that you are good at, and you promise to keep learning – I am hiring. Ping me at Vijay dot Vijayasankar at US dot IBM dot COM . 

Analytics about people – nothing but land mines all around 

Just so everyone who reads this is clear on my views on “HR” as a term , pls read this . For me – Nothing trumps “respect for individuals” when it comes to people .

I am a big fan of using analytics as a decision support mechanism in the corporate world . However , the importance of context is unusually high when it comes to using analytics to make decisions about people – hiring , career progression , firing or anything else of that nature . If we are not context sensitive , I can almost guarantee you that analytics about people will almost always lead to poor decisions .

People – unlike resources ( the R in HR) or Capital ( the C in HCM ) – are not fungible. 

When we do analytics on cash , inventory etc every unit is the same and we can make good decisions on aggregated information . People are not the same – and at an aggregated level, the data is usually misleading .

Let’s say our Sales is going south and we need to find a list of sales reps to fire . The standard analytics is to find quota attainment of all reps , and take the bottom list and fire them . More experienced managers might give some allowance for forward looking pipeline to see if some of the people need to be given a second chance . If the decision to fire is taken at top management level – everyone in sales looks the same and finance department could just say “you need to let go of 5 telesales reps ” . The spreadsheet does not usually tell you all the different characteristics that make each Rep different ( like say only one of the reps is multilingual and that is super useful in real life)  , whether there are mitigating circumstances ( 4 managers changed for telesales team in one year and each had a different strategy) , etc .

Unlike assets and resources , people care about other people 

If you choose to discount your widgets heavily to do a fire sale , or throw away a few to scrap – the remaining widgets don’t get affected . It’s not the same with people. 

If your friends and colleagues are poorly treated – especially for reasons you have to guess because no one told you – It will affect your performance on the job. Best case it decreases your performance on the job temporarily , and worst case you will walk out of the door and find another job . How you will react is also probably different from how any of your colleagues will react to the same decisions . 

The new VP for sales might cost more than existing Sales leaders . That is market reality and you can’t fight it if you want to hire top talent . Analytics can even prove that the salary is par for course . What it might not tell you is the cost of replacing the existing top gun sales leader who is pissed off when she finds out the new person is making more . Unlike assets – people talk ! 

Analytics can’t be big brother 

Aggregated data is the problem – but analytics can still figure out a lot about individuals by gathering fine grained data about each person, and in many cases without violating laws . This still doesn’t mean it is ethical to “snoop”. If you leave a social media exhaust , you are not really permitting your employer or anyone else to use it against you . World will be a terrible place when employers  (and government ) keeps tabs on you all the time . 

Finance and HR don’t look at “people” the same way 

If you ask the CFO and Chief HR officer of a big company on how many people work there – the chance is high that you will get two different answers . And each will only trust their own number . There are good “technical” reasons for this discrepancy ( like how each function defines FTE )  – but this also causes a lot of bad decision making when HR and finance data is combined to make people decisions 

I could go on with what else causes bad HR decisions , but let me wind up here with a parting thought . In near future , Robots will become part of what HR supports today . All this analytics that don’t work as intended when it comes to people – they will work much better when it comes to machines . So perhaps it was not all wasted effort after all🙂

Making Business Travel Bearable 

I had a huge fascination for air travel as a kid . My dad traveled frequently ( at least once a month) on work , and I had some rich relatives who flew regularly to America and Europe for family vacations . They would tell me great stories of their travels and bring me back little goodies – like a can of Coke ( which was not available in India at that time ) My resolve became stronger over teenage years that I need a job that let me fly frequently all over the world .

My wish was granted . Fast forward 20 years since I graduated – and millions of miles behind me – all I can say is WHAT THE **** WAS I THINKING ???

By conventional terms for me  – 2016 so far has been a “light travel” year. And today I saw that I had already qualified (yet again) for the highest tier of my Airline frequent flier program for following year , as well as for the hotel chain I use . I am sure more than half my colleagues had those same emails 6 months ago. The last time I felt good about getting such emails was the first time I got it and I can’t even remember which year that was . 

Those who know me can vouch that repetition bores the heck out of me . That is why despite the extreme dislike for travel – I am still in the consulting business . I get the variety of challenges that keep me motivated every day I wake up . If clients for any reason choose not to challenge me – I am sure my employer will pick up the slack and throw a few challenges my way …. you know , just to keep me sharp🙂

Effective and efficient travel is a life skill for anyone in this business . I keep picking up new skills and make little tweaks as I conquer the sky miles . Here are a few that I think are my basics – with no claims that it will work for you too🙂

1. Be a minimalist about everything you pack

If you need 3 shirts for the trip and want 5 , stick to 3 . If you run into an emergency – buy a new one or hit the laundry. In 20 years I have had to do that maybe three times . 

A big part of traveling comfortably is to pick a great bag to be your constant companion . Although not fashionable ( and un-executive like according to my mom)  – I use a backpack for my laptop and books , and one stroller for my clothes be it a one day trip or a 5 day trip. Many friends choose multiple bags to suit length of travel. 

Many consultants go the same city every week for several months . When I had that kind of travel,  I used to leave some dailybuse stuff at my regular hotel (or under my desk in a bag) to avoid carrying it . 

I dress for comfort . Unless the client needs me to – I won’t wear a suit and tie . Comfortable shoes that also look decent is probably the best investment I make on shopping front.

2. Try as hard as you can to not checkin luggage 

You cannot buy time . Even when you have nothing better to do – it’s better spent reading a book , or (and?) drinking a beer at the airport bar than standing in line to check in your bag and then waiting to pick it up at the destination . 

3. Ignore the pain and earn top tier loyalty levels at airlines and hotels ( optionally car rentals too)

Pick an airline that works for 80% of your travel and stick to them till your breaking point . I have come dangerously close to getting out of it a few times but I haven’t taken the final step yet . As you travel more – upgrades become your best friend . And the bonus points help a lot . I forgot the last time I paid for a hotel or airline when I took a family vacation . I have stopped renting cars almost fully – mostly because of the nature of my current job . I stick to cabs and uber now and it works splendidly . But in many cities – rental cars still make sense . 

4. Choose a credit card wisely for travel 

Those points help with vacation . Some will also make airline club memberships cheaper . Balance it against annual fees and pick one up and use it regularly . Always keep a backup card too – Murphy is always watching you ! 

5. Enroll in TSA-Pre and Global Entry

Although those lines are getting longer compared to when it got introduced – for the most part it’s easier to get through them than the regular frequent flier lines. I have a few friends who don’t enrol due to privacy concerns – and it makes a great beer conversation after their tired selves join me at the bar after a two hour journey through the regular line . For me this is the best $100 a consultant can spend every 4 years 

6. Minimize the need to travel 

It’s really hard to not travel at all for business – except in a few cases ( say where you have extremely good skills and are in a hot market without a lot of competition ). But all of us can minimize travel by good use of phone , email , social media etc . I often choose to travel even if I can get work done through electronic media – mostly because human-to-human interaction has greater quality . 

7. Build a time saving routine 

Routine keeps us sharp and reduces variance and hence reduces risk. I am on autopilot for several things when I travel . Be it packing , booking , driving , eating or exercising – build a routine and it will help tremendously over time . For example – I know it takes 12 minutes for me from gate to exit at PHX airport . So I book my uber ride to perfectly match when I am stepping out and have zero wait 

8. Strike conversations every chance you get 

I can check email later – but if I can strike a conversation with a stranger , I will do it . These are not long boring talks and if the other person is not interested I move on quickly. But I have learned a lot from these conversations – especially from cab drivers across the world . When I have hypothesis to test on social and political issues – nothing beats airport and hotel bars . Over the years – I have even built the foundation of a few business relationships this way . Funny enough – I have met more fellow IBMers in airports than at any other place🙂

9. Never eat alone 

I try really hard to not eat alone when I am on the road . After work conversations over food and beverages are the best way to know your customers and colleagues . It has the Magic effect of building solid relationships over time . And it keeps the boredom away 

10. Call home 

I usually call in evenings to catch-up with my wife and daughter . And I call my parents and in-laws from cabs on my way from airports . And I post updates during the day on Facebook so that they know what I am up to . What I don’t do well is to restrict the audience to just my wife , my sister , my mom etc – but that is just my laziness . 

11. Music , reading , writing , exercise ..

I use my phone for listening to music , to read and also to write emails and blogs . I always prefer being agile over being elegant and formal and it mostly works for me . I am not big into working out – so I use airports and offices to walk fast , climb stairs and so on to make up for it . I don’t always succeed . 

What are your tips ?