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.

10 Tips On Navigating The “Large Enterprise” 



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 !