And Vijay Says…

Barring two exceptions – MongoDB and Novasoft – all my career was spent working for really big companies . And all that time my client base was “large enterprise” – companies that operate in multiple countries , have multiple divisions , make billions of dollars of revenue and employ tens or hundreds of thousands of employees . 
Over the last two decades , the biggest obstacle to career success that I have seen is the difficulty in navigating these large complex companies . It’s not just a career success problem – I have seen both my buy side and sell side analyst friends beating their heads against the wall on why “stupid decisions” are made by companies they cover. I have also seen sales and delivery people struggle navigating their client organizations which are large and complex.

Honestly , from time to time – I get frustrated with some inexplicable things about these companies too . And I get asked about these a lot by folks I mentor . And despite all these – I and many others have had good careers in this space . Size can be a significant advantage when used and understood right .

So here is an attempt to share some thoughts on this topic – and as always , strictly my personal opinion

The best way to describe life in a large company is “The Matrix Dysfunction” . This is also the working title of the autobiography I aspire to write some day when I am retired 🙂 

1. Organizational structures will change frequently – make peace with it 

With every change of any arbitrary dimension – number of employees , product lines , countries they operate in , number of senior executives whose ego need a boost and so on – comes the reflexive move that goes “let’s change the structure” . It makes perfect sense on paper to organize everything neatly on a spreadsheet or an org chart PPT every time some such dimension changes – but it is often just counterproductive to push a lot of people through massive change. Change fatigue kicks in and frustration starts creeping up . 

Some day, large companies will hopefully figure out that such org level changes are not needed frequently . But in the meanwhile , the best way to deal with it is to assume it will happen a few times in your career at any large company, and learn to live with it . The analytical ones amongst us will be driven nuts because we won’t get all the answers on the rationale – but if you don’t make peace in reasonable time, you will just tie yourself in knots . If it is any consolation – your daily work might not change much at all in most org changes . 

In any case, natural leaders emerge above the noise and if you are observant , you can figure out who can actually make things happen irrespective of their Stars and Stripes . They often can teach the rest of us what we need to learn.

2. No one “truly” owns anything below the C suite 

The most loosely used phrase in large companies is “I own the P&L for this”. To be fair – this is factually true that someone does indeed own a P&L for compliance reasons  , just that it is not how things work in the field . For example -legal entity  P&L might be owned by a geography , but product lines that drive the revenue are shared services across all geos . So if a critical decision needs to be made , two decision makers become a minimum and they may have conficting objectives to make “their numbers”. For example – a Geo leader might be measured on regional revenue and a product line leader is measured on one specific product’s revenue . As complexity of product portfolio increases – you start to spend more time on internal deal making than deal making with customers . 

This is not as bad as it seems on paper – the design point is one of “healthy tension”. It also is the reason why experienced leaders know that control is an illusion and collaboration is the only way to get things done efficiently. 

3. Policies are built for Efficiency , not Efficacy

When you have a lot of people , you can only make broad rules that can be implemented in reasonable time with low effort . The unfortunate part is that unimaginative leadership some times makes it impossible to have any nuance . A funny example would be a senior executive who runs a billion dollar business might still need her boss to approve a new $500 smart phone for work use .  

The only way to make this work is to build a culture where if a policy “looks stupid” when it’s implemented , you can go up the chain easily and point it out . Best case the policy gets changed – but worst case at least you can get a few exceptions approved . You do need to pick the battles you want to fight so that you don’t exhaust yourself in the process 🙂

4. You may get the impression that finance and HR run the company 

While rest of the organization – the line business – changes from time to time , support functions are generally a stable part of the company . Their job is to support line management – but since line management changes from time to time , and because the support function often are the communicators of the message – the prevailing wisdom in many companies is that finance and HR runs the company . 

A CFO I deeply respect once told me ” When the business leaders run the business well, my job is to report . In that scenario I rarely speak unless spoken to . When they don’t run it well, my job is to get the business to a shape that lets business leaders run it . And at those times the business leaders won’t speak unless spoken to” . He was only half joking 🙂

5. You can use the overhead functions in your favor , or you can lose it 

Overheads come in many flavors – senior execs who are not in line jobs , operations teams , overlay sales teams and so on . Whether you use them to your advantage or not , their cost is allocated across the company . And there are many ways to use them very effectively . 

Many senior executives have relationships with clients that are useful for you . Rather than do all the analysis by yourself , you can perhaps enlist the help of someone in operations and so on . 

There is a flip side to this – when things go bad for a company , the axe falls on overhead functions . But when done using broad rules and without taking input from people in the field – often this leads to “penny wise, pound foolish” decisions . Again – the only real solution is a culture that makes leaders have a constant pulse of actual execution , and staff feeling free to let leaders know the potential impact of decisions . Easier said than done !

6. You need to develop a healthy working relationship with your manager , but You need multiple mentors to thrive 

Since things change frequently , and since you are living in a multi dimensional matrix – you need a set of long term mentors to act as your compass . This is especially true for people new to the large company set up . Managers keep changing , but long term mentors will provide stability and insight since they have experience living through changes . Goes without saying – you need to mentor someone too . Pay it forward !

7. Very few people know everything that happens 

This is more people looking from the outside . A large company is not a democracy – decisions get taken and communicated at many different levels of the matrix . So it won’t be unusual for a senior executive to find out something important  only after it shows up in the press . Not ideal – but communication is not efficient when size and complexity of a business is large . I make peace with it by assuming two things 

1. If we agree that on a “need to know” basis of working , 90% of the time things work fine 

2. Half of the rest , you can get through informal channels if you build a good network within the company 

And some times inertia is the sole reason why something happens . I know a client who spent millions of dollars on sponsoring basketball games long after they sold off the particular product line that was targeted for the basketball campaign . It took a long time  to come to that realization ! 

8. Responsibility and authority may not be correlated 

This is perhaps the hardest part to get used to for managers . The decision on salaries , transfers, career progression etc might not be taken directly by an employee’s direct manager – but the manager often needs to be the one communicating and defending a decision . 

One way I have seen managers deal with it is to ask someone else – like an HR rep – to do the hard conversations . I am not a fan of this approach . Good or bad – managers have to own the responsibility of communicating with their team . You won’t become a true leader till you get comfortable telling good and bad news with poise , honesty and candor .

9. A template for everything !

To operate a big company , you have to minimize variance whereever possible from a top down perspective . There is no use fighting the need for consistency – otherwise you just paralyze the decision making process . The solution for me is two things 

1. Ruthlessly prioritize so that your hardest hitting points come clear on the template 

2. Find opportunities other than formal reviews to get buy in for your ideas . All the more important that you build relationships early and often 

10. Execution rules , Strategy drools 

I can’t emphasize this enough. Large companies tend to have a lot of people who work on pure strategy – some good , and several just academic and far removed from reality . You will also keep hearing strategy was awesome , but execution did not match . I call BS on this every time – a strategy that cannot be successfully executed is a bad strategy , but perhaps a good hope ! 

Tactically – I have always favored the approach of “if you can do something ethical and legal to make your client and your team successful , do it – and ask for forgiveness if you fail”. Best case – you might end up changing a made policy and make a new best practice . Worst case you get slapped a bit , but still can sleep better . Not a bad trade in my book.

I will end this with a bonus tip . Not every one is a good match for any given role for ever . Just as your employer can fire you – you can fire your employer too . If you keep your skills sharp and have an open mind, the world around us has a lot of opportunities. So if you find that there is no way you can make it work in your current role despite your best efforts   – you should consider quitting and starting afresh elsewhere, within or outside your employer  . Nothing is worth being miserable all the time . Leave on your own terms and respectfully without burning bridges ! 

Most of my adult life was spent doing consulting work for clients around the world on topics like information management and analytics. For last few years, I have also been involved in IOT, AI etc. When I look back – one thing has been common for all “data projects”, and that is the importance of TRUST…or lack there of. Billions of dollars have been spent creating BI systems ,data warehouses, data lakes, cubes, reports, workbooks, blah blah – and yet at the end of the day, spreadsheets still prevail at EVERY SINGLE COMPANY I know !  I wrote about this in the past.

But why ?

There are good reasons why users don’t trust data they get from BI, like

1. In most cases, users cannot see how it ties back to source systems
2. Transformations and enrichments to the data are not transparent to users
3. Users do not know how well the system has been tested
4. The tools may not be as intuitive as a spreadsheet….and many more

Every single one of these problems in “Classic BI” has a solution – which either a product or a service can solve. An IT expert can probably show what transformations happened, or create some reconciliation reports for example. Or a data lineage tool can trace back from report to source. So with some additional cost, we can minimize the trust issue – though the cost may eventually become prohibitive to build trust at scale.

Past is set in stone …most of the time, at least 🙂

The reason we could do all of this is because we are essentially looking at things that already happened – which are kind of “set in stone”. And the information we got from such systems had finite values as answers like “sales in north america was $10M” which is based on basic arithmetic . If I asked the same question again tomorrow, I will get the exact same answer of $10M. If I did not – I would know right away that something bad happened. If a bad decision was made –  for the most part, it is possible to trace back and validate the data and confirm if it was indeed bad, and prevent it from happening in future.

Now lets look at the world of AI .

To avoid a religious war on terminology – please allow me to use AI in its most generic sense as an all encompassing thing that includes what we call data science, machine learning, cognitive etc. Definitions matter – but for this post humor me and pretend all the right things are covered when I say AI.

Just like with classic BI, we use a lot of data and transformations. However, the fundamental idea now is that we are not just reporting on what happened – we are trying to make the best educated guess on what will happen in future. We are not in the world of “only finite answers” here – instead of the exact sales that happened this quarter in North America, we are often trying to find what are the odds that sales will be greater than $10M next quarter, for example.

Enter the trust issue

On one hand, it is quite useful to have a system that makes such predictions for us so that we have a window to the future. On the other hand, if we do not trust the answer – it is a lot more difficult to explain how the system arrived at the answer. And if the system told me today that I had 80% chances of hitting $10M for the quarter, it could very well tell me tomorrow that I only had 50% chances of making that $10M number.  I and everyone else in my team might think the system is foolish because we can see the math to get to the $10M number we want . Lets say the quarter finished and we did exceed the $10M number – this still does not mean the system was either right or wrong. That is the beauty (and pain) of how probability works !

Can’t the creator explain the creation ?

I am often asked “Can’t you just ask the programmer or data scientist who built this to explain how the system predicted?”. Yes I can – and some times that is all it takes to get the answer. But many a time, they may not be able to give that answer with the precision you expect. AI systems are learning systems (with or without human help) – and they learn and get smarter mostly by going through a lot of data, as opposed to just crunching logic fed by a human. By the time I asked the $10M question the second time, the system might have learned something from a new pattern it detected.

AI can piss you off

A sales forecast, in the larger scheme of things, is probably not going to change the world for most of us. However, if we think of other scenarios like say salary planning  or promotions where an AI system scores everyone in a team on a complex set of parameters and makes recommendations – it is hard to accept a decision that cannot be explained in an easy to understand way. The system may be totally right – or it might have all kinds of bias built into it with the model, or the data it trained with. It might have false positives and false negatives. There are techniques to minimize all these problems – BUT If it cannot explain its results to us as users – how will we know for sure ?

Can you trust machines ?

There is another version of the trust issue – when machines need to make choices that affect us. Lets say you are a factory supervisor driving in a self driving forklift that is picking up a heavy load from one top shelf and putting it in another top shelf, while your workers are walking below its arm. The machine probably has visual recognition capabilities, and can crunch lots of parameters from data and make good decisions. Lets say one day, the machine detects the load is too much to bear and it has two options – flip on the side and injure you, who is sitting inside or drop the load and injure your workers. What should that machine do ? And if you don’t know what the machine will do – or at least know that you can over ride it – will you work with that machine ?

AI – its just like us, except it isn’t 

I also get asked “Well, AI is supposed to think like a human, so why can’t it explain its thought process like we can?”. This is an excellent question and it presents two issues. 1. We don’t all think alike – even in the fictitious forklift example, I am sure different people will choose differently.  And 2. We often take a decision, and find an explanation for the decision later if someone asks. We can’t always explain our decisions very well either to someone else except for simple cases. And finally, we make poor decisions too. So mimicking human thinking as-is perhaps is not the best way to think about AI either 🙂

AI is everywhere, and mostly harmless

I am of course not generalizing that all AI scenarios run into a trust ( or ethical or moral) issue. Many don’t – for example an AI algorithm might predict how much longer a device will work before battery runs out. I doubt I will have a trust issue if I see it work approximately well for first few times. And there are several of those kinds of “little” AI solutions all around us – and many might not ever be visible to us. We just take them for granted ! Even in the sales forecasting or promotion examples – over a period of time, we may trust what the system tells us. But the trouble is – will we give it enough time to let it work long enough to earn our trust ?

So what can we do, really ?

Just like other projects, AI projects need some basic education and expectation setting for stake holders before we embark on them. Unlike basic math, and if-then-else logic – statistics concepts needs a bit more hand holding. People tend to use terms like confidence, significance, sampling etc loosely and it is very easy to set wrong expectations with stake holders even with the right intentions. And then there is the issue of trust, and its ethical and moral considerations. It is important to discuss these thoroughly upfront, and during the projects . When done right – and transparently – AI can and does add significant value to us. Its on all of us in this industry to make sure we let AI earn trust the right way !.

 

 

 

 

There is nothing that bores me more than repetitive work ! The irony is that I don’t have this problem outside work. I can eat rice three times a day for rest of my life. I can listen to the same set of Ilayaraja songs every day. I have watched Gandhi movie 30+ times , All 7 seasons of West Wing multiple times on Netflix and probably will do it again this Christmas. I love reading the same books over and over every few years. I can spend months perfecting every last bit of synchronization between my dog and me before we compete in dog shows.

But when it comes to work – I cannot stand repetition !

I get bored very quickly. In the early stages of my career, this kind of worked in my favor – I just switched the areas I worked on every year or two and had a fun time picking up new skills and having fun, and in general my career progressed at a fair pace. In hind sight – I was probably taking way too much risk (while doing this on an H1B visa at the time) , and I had a lot of great managers who never stood in my way when I wanted to do something new. But once I was in leadership roles at work – it became abundantly clear that repetition is a good thing for abundance and it is futile to avoid it. On the bright side – I was also quite lucky that people I got to work with mostly loved optimizing repetitive work and gaining efficiencies. But the problem remained that I still could not feel excited to do encores – and since I generally won’t ask my teams to do anything I wont do myself, this started becoming a bloody pain for me.

And that is where side projects became a life saver for me ! They gave me enough variety and stopped repetitive work from distracting me.

The first thing I tried my hand was in blogging – on what was then called SAP Developer Network. And thanks entirely to Marilyn Pratt, I started enjoying it and later branched to having my own personal blog. Only a small % of my blogs talk about technology or work related stuff – and I mostly write to get things off my chest, as a venting mechanism. So I write on the wordpress app on my iPhone for the most part, never proof read or spell check. But thanks to some very loyal and generous readers , it has always been a fulfilling activity. What I realized is that it also benefited my work some how. Many a time, I have landed at airports across the world where someone recognized me and chatted, and I have had clients give me business because they googled me and found my POV on a topic of mutual interest. All this is net goodness – but 500+ blog posts (and podcasts and videos and all), I should say that it is hard to find motivation to write these days. From 2 blogs a week at one point, I barely write one post every other month now.

Then I picked up my old hobby of dog shows. I was quite successful as a competitor growing up in India and have won my fair share of top awards – and lost more than I care to count. My claim to fame was my german shepherd who could go to our corner store and buy milk by himself. The local news paper and TV channel there had that story covered when I was in college. What I realized in the second round was that my love for dogs is now significantly more than my love for dog shows. Long story short – I did not push myself or my fur kids hard enough and we mostly just hang out and cuddle.  Again – there were many positives to take to work. One is increased patience and the other is the invaluable lesson that any problem can be solved by breaking it into several smaller problems and solving each and putting it back together.  And a side “benefit” – having dog hair on your trousers are a great conversation starter with fellow dog lovers at work 🙂

My day job these days is to run a small “hyper-growth” business at IBM ( pretty sure if you name a buzz word, I have it in my portfolio – Cognitive, predictive, IOT..check, check and check) – with more people than I have ever led before, and a target with more zeros than I have ever managed before. There is something new to learn every day, to say the least. Obviously, doing such a business profitably includes a fair mix of repetitive work, and first of a kind work. Thanks to the long plane rides – my coding and math skills are somewhat kept sharpened through all this 🙂

As luck would have it – I also got the best side project I have ever had in my career. I am the executive sponsor for our consulting school for senior managers – which we lovingly school “The Bee School”. There is nothing more fulfilling than joining hands with our very passionate and skilled Learning and Education team, and a bunch of enthusiastic fellow executives to teach and learn from our next generation of leaders. It has been an eye opening experience for me to see how an education program is designed from ground up, how we train the trainers, how we execute the training and how we learn and adapt quickly. I am seriously thinking of being a full time educator now. The best part of the week we spend with a hundred senior managers is the Cognitive hackathon night where people who have no prior coding background build a virtual agent using Watson and realizing how easy modern technology is , and how much value it adds to clients ! I have to thank my “big boss”  Ismail Amla for the opportunity .

My latest side project is to build up a garden – flowers, veggies , anything that grows. The last time I did this was when I was in grade school. What makes this a fun project is that I live in AZ, where the summer temperature is a cool 100F to 120F. I have a willing co-conspirator in my mother in law who has a beautiful garden in India. My core design principle was “hyper growth” – so I had to do my research on what plants will thrive in our climate, which ones will yield flowers and fruit the fastest, and how to build for some future scale in case this actually works.

We already have some success – yesterday we ate the first egg plant and tomato from the garden . And there are about 20 different shades of hibiscus flowers in the yard. The ROI for these vegetables is now something like every $5 of veggies cost about $500 ! But the sheer satisfaction of seeing “hyper-growth” is priceless 🙂

There are some good lessons I could potentially take to my work – 1. Time spent in upfront research saves a lot of rework ( The few plants we lost in the process were all mistakes that we could have avoided if we took time to read more and ask around a bit more). 2. Automation is a good thing and something you need to put in place upfront ( everything on timed automatic irrigation now). 3. You cannot just trust process and automation if you want solid results ( Till plants take root, you need to water extra , give them support, remove weeds and generally treat them like a little baby 🙂 )

The current gardening project should keep me going for a little . But I am already thinking of my next side project – and one idea is to buy a German Shepherd puppy and train for Schutzhund/IPO. I am inspired by 7 year old Samantha – watch this video and see for yourself. Or perhaps start writing my book – The Matrix Dysfunction !