Googler’s Screed


Or perhaps I should say Xoogler's screed instead , now that he got fired .

Strangely, the first thing that hit me was the word "screed" since the last time I heard the word was couple of decades ago in engineering college . Perhaps "manifesto" associates itself with "communism" and hence "screed" seemed to be the more appropriate word.

I went through a series of emotions as I read about the screed . It started with anger, and that is the only reason I did not start typing my views on day 1. I needed time to process the information . And doing that increased my respect for journalists who had to break the news without much time to look at it at depth , as well as Sundar Pichai who had to take a decision quickly on what to do with the guy who wrote the document . I am glad I took the time before posting a rant – it was quite educational to read many different opinions and talk to many fellow engineers, male and female .

One thing is abundantly clear – Google was put in a no-win situation. If they didn't fire the guy, they would get painted as anti-women . If they fired the guy – they will be accused of shutting down diversity of thought.

There were a few points in the debate on both sides that I thought were rather weak – like Damore's first amendment rights , and whether he should have written such a document during work hours. Google is not a government entity and first amendment should not be a big consideration in letter or spirit in this context . The guy attended a google class on diversity and wrote it in response . If google offered the class during work hours , I can't blame him for writing a response during work hours and circulating it .

I am an engineer myself and hire engineers to work with me – and it was extremely painful to read the document and realize it was a fellow engineer who wrote it . It just felt like someone did the profession a big injustice – and perhaps it's an over reaction . In any case – I would not hire a person as an engineer in my team if I suspect a significant lack of empathy . Not going to belabor the point – this is a brilliant take on it and you should read it .

The tone of the manifesto is quantitative and dispassionate from what I could interpret . When criticizing it, however, there seems to be a penchant in media to refer to it as "quasi-professional" and "pseudo-scientific" and so on . Even though the opposing arguments looked strong to me , trying to attack the tone of the writer as opposed to his central ideas and facts(?) diminished its effectiveness.

While I can't say I have a first hand understanding of what it is like to be a woman in tech – I can extrapolate from what I went through as an immigrant and have no doubts how difficult it must be . I also grew up in a family of strong women in India who fought all odds to thrive in a male dominated society . I was not in the minority growing up in India – and my appreciation for its value only happened after I moved to USA in my early twenties .

When I first came to this country, I faced a fair bit of Discrimination as an Indian programmer in the midst of mostly white male programmers – insults to my intelligence , the food I ate , the music I liked , my accent and so on were common place. I also had some very kind managers and friends and co-workers who considered me as one of them and helped me cope . For the most part, I don't feel it anymore – I developed a thick skin over time and larger number of Indians are there in the workforce now for me to feel alone.

Damore is absolutely entitled to his opinions like the rest of us – we live in a free country. But as an adult, he should also know that actions have consequences.

I think where he lost the plot of having a good debate – instead of the storm he caused – was in quoting studies and stating that all of it applied only to populations and not individuals , but then going on to make recommendations that don't follow that thread of logic . That gave me the impression that he was not arriving at a conclusion by building an argument ground up, but just finding a way to substantiate what he always believed . Irrespective of the content , that is not the hallmark of a good engineer .

He does state that he is supportive of an inclusive workforce and agrees that sexism exists. Unfortunately the recommendations are either too vague , or not backed by consistent logic. It came across like "Current diversity program sucks, so let's get rid of it. No diversity program is better than a partly effective one". Huh ?

The charitable side of me wants to believe it was mostly ignorance and lack of skill that caused him to write it the way he did , as opposed to totally evil intentions . In any case – he earned the backlash fair and square in my opinion .

To begin with, Google had a lousy episode recently of telling DOJ that 100K USD is too much money to spend on compiling payroll information for gender equality. Now if they also did not fire the guy who wrote the awful memo – it would have been an even bigger nightmare.

I do grant one thing Damore raised . If you hold conservative views in Silicon Valley, it's rare that your views will resonate in the work place, and there is a good chance you will be out-shouted . Unless of course you are someone like Peter Thiel . It's an extremely left leaning place and the lack of inclusion is not just about gender, it's about diversity of thought too .

One thing google needs more than anything to keep its leadership in the market is retaining and attracting top talent . They cannot afford to risk a bunch of their talent walking away if they think google doesn't support their ideology . There is no non-compete in CA and many of these engineers are already rich and will find multiple jobs quickly with google on their CV . Even if no one walked out of the door per se , which development manager would choose to have Damore in their team after his views became public ?

If forced to choose between the support for gender diversity and thought diversity – I firmly think gender diversity should win every time . Ideologies evolve with time and mistakes can be corrected relatively quickly , but gender doesn't follow that path. Solve the gender diversity and it will be fair game to have absolute focus on thought diversity .

In my view, Sundar Pichai absolutely did the right thing by firing the guy – but google leadership , HR and PR departments should get a B- for how it was handled . As a friend mentioned on Facebook – the only thing worse than scheduling the all hands was canceling it .

The net goodness out of this episode is that it sparked debate yet again on the importance of diversity . The sad part is that without such incidents, it doesn't get the attention it deserves.

And Jerry Says : A Path to SUCCESS with Advanced Analytics


Folks, I am very proud and happy to have my dear friend Jerry Kurtz do a guest blog on my site. Jerry runs the Cognitive and Analytics businesses in my portfolio, and is a long time IBMer. He has been in this field for 30 years across SAP, Managed Business Process Services and Analytics and now Cognitive for last few years. He is a man of many talents outside work too – a very good singer – he is the lead singer of the “midlife crisis band”, a competitive golfer, and an overall good dude to hang out with. He lives with his wife Amy, his daughter Emily , Son Adam and his 3 year old fur kid Baxter, a Chocolate lab. You can find him on twitter as @jerry_kurtz 

Jerry with Fish

Take it away, Jerry !

I will start with sharing highlights of my beliefs regarding the “10 Fundamentals of Successful Advanced Analytics Programs”.  I hope to go deeper into each of the 10 in subsequent posts.  Also, for the purposes of this blog, I will not define each element of analytics.  Rather than keeping “predictive” separate from “optimization” and “prescriptive” separate from “cognitive”, let’s just call it all “Advanced Analytics”, shall we?  We shall…

Analytics Screens

Fundamental #1 – Establishing a “Balanced” Advanced Analytics Strategy. Any analytics strategy must have three basic things.  These three basic things may seem like “motherhood and apple pie” to some of you, but it’s amazing to see how many times we have seen Fortune 500 companies make mistakes on these basics.  More on case studies in future blogs.  Your analytics strategy MUST HAVE:

  1. A Business Capabilities or “Use Case” roadmap that answers the question “what solutions do we need to implement for our BUSINESS, USERS, and BUSINESS PARTNERS to achieve our business goals”? This is the value side of the equation.
  2. An Information Foundation roadmap that ALIGNS very tightly with the Business Capabilities roadmap. A strong data foundation does not in and of itself create value (with few exceptions), it ENABLES the full range of business capabilities and VALUE to be rolled out over time.  The above two strategies MUST be aligned with each other to maximize value.
  3. An Organization / Governance approach and roadmap that also aligns with overall business strategy and the above elements of the analytics strategy. We have seen that the “technology can be the easy part”. It is often the organizational structure, culture, and related politics that gets in the way of success.

Fundamental #2 – Establish a program goal of “10X value to cost” and “Self-funded”If you have the right level of executive sponsorship and you scope analytics programs properly, you can target at least $10 of hard value for every $1 of program cost.  Also, if you prioritize business capabilities the right way, self-funding is achievable within 90 days of program start. If your analytics strategy is not meeting these metrics, you should probably rethink your strategy.

Fundamental #3 – Think and Act with “Parallelism”Self-funded analytics programs can’t be achieved by working “serially”.  We have seen clients say, “we need to get our data fixed first, then do basic Business Intelligence and Reporting, THEN we will do some advanced analytics”.  In today’s world, however, parallelism is key. For example, some advanced analytics can help fund other elements of the program.  New business capabilities can help fund data transformation.

 Fundamental #4 – Having the right level of business sponsorship Without going into too much detail (yet) I will summarize my experience that the most successful analytics programs have senior and clear business sponsorship / ownership.  In the last couple years, my most successful analytics roadmap / implementation program was sponsored by the global CFO.  Just an example.

Fundamental #5 – Picking the right place to start  If you have 50 new innovative ideas for Advanced Analytics use cases, the best place to start is usually on use cases that (1) have strong executive sponsorship / business need, (2) have data readily available to solve the problem, even if in multiple sources, (3) LOW COST but HIGH VALUE for Phase 1 (e.g. Proof-of-Value).  Again, this may seem basic, but we see mistakes all the time.  Last year, I walked into a client that had started with a global management dashboard across 10 countries.  Very expensive and very time consuming.

Fundamental #6 – Scaling beyond science projects For now, let’s just say that the technology aspects and “finding smart people” will be the easy part.  The “soft stuff” will make or break the project.

Fundamental #7 – Embrace diversityI grew up in the ERP market where there was a fair amount of homogeneity across project resources e.g. similar background, similar training backgrounds, etc.  During my last several years in the Advanced Analytics space, I have met hundreds if not thousands of people, and I can best summarize them by saying that “they are all from different planets”.  While the incredible diversity in this space can make it much more difficult to assemble a “winning team”, I personally LOVE the challenge and so should you.

 Fundamental #8 – Teamwork / collaboration – At the risk of being too high level for now, I will summarize by saying that it’s all about resource “mix” including both mix of skills but also personality types.  For example, I would rather work with an A- data scientist who works well with others rather than an A+ data scientist who is always “the smartest person in the room”.

 Fundamental #9 – Analytics practitioners must be life-long learners e.g. “Adapt or Die”As Thomas Friedman explains in his recent book Thank You for Being Late, we have reached a point where technology is changing faster than humans are able to adapt. We and our teams had better keep up with rapid change or we risk becoming obsolete.  This challenge can only be overcome through life-long learning and constant, adaptive change.

 Fundamental #10 – Be Hands OnWe ALL need to find ways to be hands on with analytics technology.  If you are “only” a project leader or a business analyst or a practice leader, you should find ways to “sign-on” and learn your trade at a hands-on level.  Generalists with minimal technology savvy will struggle in the coming years, but “hands-on” specialists will thrive.

I hope you have enjoyed reading this as much as I have enjoyed writing it and sharing with you.

Thank you.

Jerry

IBM Watson is just fine, thank you !


ibm-bets-watson

Over the last couple of days, I have seen a bunch of articles on my social media feed that are based on a research report from Jefferies' James Kisner criticizing IBM Watson.

I am a big fan of criticism of technology – and as folks who have known me over time can vouch, I seldom hold back what is in my mind on any topic. I strongly believe that criticism is healthy for all of us – including businesses, and without it we cannot grow. If you go through my previous blogs, you can see first hand how I throw cold water on hype.

Unlike my usual posts, I cannot claim to be an impartial observer in this case. As much as I am a geek who wants to make my opinions known on technology topics, I am also an IBM executive , and I run a part of IBM GBS business in North America that also includes services on IBM Watson (including Watson Health) . I also own IBM stock via ESPP and RSU. I don't set product direction for Watson – but my team does provide input to the product  managers. So I was in two minds over the weekend about blogging about this – but net net, I think I will go ahead and say some things about this , and as always I am happy to debate it and stand corrected as need be. So here we go.

IBM Watson's primary focus is on enterprise, not consumer !

This should be obvious to most people but perhaps the technical and use case implications are not super clear when they conclude Watson is in trouble.

Lets take an example of something that is often used to make the point in favor of consumer AI tech – Alexa. I often get asked Watson versus Alexa/Google assistant questions. You can tell Alexa or Watson to check the weather and they will both do it. The big difference is – Watson keeps the context of the first question while you ask the second question, and Alexa treats the second question as if the first one was independent of the second one. In the set of use cases Alexa solves, this is not a big problem – but the ability to keep context is important for the use cases that Watson solves, like customer service. In a customer service scenario, you cannot engage in a conversation without knowing and interpreting what has already been said.

That said – it is very easy to combine Watson and Alexa. For example , if you have echo installed at home, you can invoke Watson via a voice command and keep having a conversation without even knowing it is Watson that you are talking to.

While Watson cannot solve every possible customer service scenario – it can solve several and deliver very high value. For example – lets say you are a utility company that gets calls from clients who want to pay a bill, check a balance, find outage restorations etc. Those are all things Watson can do just fine, and leave the high value tasks – like being an energy advisor , or a retention specialist – to expert humans. Imagine the type of value generated for that utility, and the consistent and fast customer service for their clients . Consumer AI does not tackle these kinds of problems – and that is a big difference. There are many such examples like this in enterprise side of the house – like this video about how Watson acts as an expert engineering advisor for Woodside, and H&R block using Watson as a tax expert.

IBM Watson does not share one client's data with another client

This design principle is very key to enterprise clients. Data security and privacy drives a lot of AI decision making. Consumer AI generally keeps the data all users give it and uses it to learn and get better. I am sure those companies have high ethical standards and the data won't get misused. But that is not how enterprises look at their data. It is important for clients to have full trust that their data is not shared with others that they don't want to see it.

A lot of the criticism that Watson takes a long time to learn and needs data in a specific format that is hard to do for clients come from this principle being not fully understood. Watson can learn from a given client's data – usually unstructured data – and keep getting better, but will not use company A's data for Company B's system to learn. Even if we ignore Watson and look at data science as a general topic – there is no way to shy away from an AI model having to learn. That is the core of the value prop of AI.

This is not to say every client starts from scratch. In many cases, there is a well established starting point. Lets take a Telco call center as an example. If a client wants to put Watson to augment a telco call center, they don't need to build intents from scratch. Instead, they can use "Watson for Telco" that has hundreds of prepackaged intents and just add of change as needed. Over time, this will be applicable to all industries. These are all repeatable patterns – another point that observers don't seem to notice.

IBM Watson has plenty of successful implementations , including Healthcare 

The Jefferies report calls out MD Anderson project uses that to extrapolate that Watson is doomed. I don't see any mention of Mayo Clinic trials,  Or Barrow ALS study, or  Memorial Sloan-Kettering-IBM Watson collaboration   .  Where is the balanced analysis that led to the dooms day conclusion ?

Watson is in clinical use in the US and 5 other countries, and it has been trained on 8 types of cancers, with plans to add 6 more this year. Watson has now been trained and released to help support physicians in their treatment of breast, lung, colorectal, cervical, ovarian, gastric and prostate cancers. Beyond oncology, Watson is in use by nearly half of the top 25 life sciences companies.

IBM Watson is delivered as APIs that its ecosystem can easily use

When Watson won Jeopardy, that incarnation was largely monolithic. But that is not how Watson works now. It is now a set of APIs. I am under no illusion that IBM will be the only game in town, although I strongly believe we are one of the best. Partners and clients will build Cognitive applications using Watson in a much more productive way because the functionality is exposed as APIs.

This gets painted as a negative by some of the articles. You can't have it both ways. As I mentioned above, where it makes sense to package something for a given industry or domain, IBM or someone in the ecosystem will of course package it. But the decoupled nature is the most flexible way of innovating fast and at scale in my opinion. The fact that billions of dollars of investment is directed into this field is good for IBM and its ecosystem – let the market decide on merits who succeeds and who does not.

IBM Watson some times needs consulting , but it only helps adoption

Let me also point out the role of consulting – be it my team at GBS or another consulting company. Clients are still largely tip toeing into Cognitive computing. They need significant help to understand what is possible and what is not in their industry and their specific company – which is what we call advisory services. The actual integration work is not complex and can be done by in house teams or a qualified SI. The other service I often see that is requested by clients is for design. In some other cases, they also need services for instrumentation (like in IOT use cases).

If we rewind couple of decades and go to the time when SAP was just starting out in ERP, What was the role of consulting ? Did consulting  services help or hinder the adoption of SAP globally ? None of this is any different from any other technology at this stage of its life cycle. So I am not sure why there is an extra concern that adoption will tank due to consulting.

IBM Watson team does great marketing, and we already have amazing AI talent 

To be perfectly clear, I am not a marketer – nor do I have any serious knowledge of marketing other than a couple of classes I took in business school many years ago. However, I am VERY proud of the work IBM Marketing has done about Watson. Its an early stage technology – and that needs a certain kind of messaging to get clients to take notice. If all we did was fancy videos and panel discussions and there were no customers using Watson today, I would have gladly joined the chorus to boo Watson. But that is not the case – All over the place leading companies are using it and as I have quoted above, several are public references.

From what I could learn internally, there are about 15000 of us working on this at IBM. This includes about a third of IBM Research. And we are hiring top AI talent all the time. In fact if you are an AI developer and want to work on Watson, shoot me an email and I will get you interviewed right away. While we of course use job boards etc to attract talent, that is not the only way we find people. We already have more AI folks than a lot of our competition – so perhaps that should be factored in to the discussion on "look at job postings, IBM Watson is short on talent" part of the story.

So why is IBM not publishing Watson revenue specifically ?

I am not an official IBM spokesperson – and I am not an expert on this topic. So this one aspect – I have to direct you to people with more stars and stripes than me in the company.