Going to SAP Sapphire Now as an outsider for the first time 


I am sure someone will correct me immediately that it’s the big ASUG show too 😉

For a few years now , I have been away from the SAP field in general . That is weird in itself , given SAP dominated every part of my job from the time I left business school till I left SAP Labs . I have kept in touch with several friends from SAP land ( and Bill McDermott did assure me few weeks ago that I will always be family to SAP – thanks Bill) , but I have lost track of SAP products and technologies . Although sapphire is a mega sales event , for me – this trip is mostly for education . Well,that and some networking at the Hilton bar 

There are three things I remember most fondly about the time at SAP Labs . 

1. Putting a free trial of BW and BO on HANA on AWS . https://blogs.saphana.com/2013/09/24/announcing-sap-bw-on-hana-trial-offering-on-sap-hana-marketplace/

2. On my last day in office , Debugging “simple finance” along with Hasso and realizing we are probably the only available people at that point in building 1 who knew how to work with SAP FI 😉 

3. Inviting IBM to let Watson and HANA play together https://blogs.saphana.com/2014/01/09/hello-ibm-how-about-we-let-watson-and-sap-hana-play-together/

I am sure there were many more good things , just that I can’t see to remember at the moment. There were a bunch of severe disappointments too , but I will write those off as valuable learnings 

While engineers and researchers at sap and ibm both thought bringing together these two technologies together will be awesome , for the most part nothing much happened in terms of actual integration . I left SAP and went to MongoDB , and later returned to IBM. By that time Watson had become a real business and my team was involved in selling and delivering it . 

In my second term at IBM, the focus has been away from enterprise applications and more on big data , cognitive , IOT etc . Other than occasional conversations with my friends leading our SAP practice , I had no idea of how HANA and S4Hana and HCP and all have progressed . And then lo and behold – there I see the announcement that SAP and IBM are partnering Hana and Watson . https://www.ibm.com/blogs/insights-on-business/sap-consulting/launch-of-digital-transformation-cognitive-solutions/

As excited as I am about my wish finally coming true , the most gratifying thing for me was that this was spearheaded on the technical side by my buddy ( some might say protege) Gagan Reen. Gagan was the first to jump in when I had the crazy idea to POC HANA for a Teched 5 years ago https://andvijaysays.com/2011/08/08/sap-hana-we-did-it-in-4-days-and-lived-to-tell-the-tale/ . He is still just as passionate about SAP technologies as he was when I first met him. 

Seeing him and others that I mentor grow into well respected leaders beats every other career accomplishment I might have had . Now it is even more gratifying to see these leaders paying it forward and groom another set of leaders . 

Special thanks to Mike Prosceno and Stacey Fish (absolutely the best in the business ) for getting me to sapphire again this year. I can’t wait to catch up . It’s been a while since I caught up with SAP mentors , bloggers and analysts. All I can hope is that the Hilton bar has enough beer stocked 😉

And for the first time ever , I plan to visit every vendor booth at sapphire . 

Here are the things I want to learn this time on priority 

1. Details of the new Apple partnership beyond the PR message that came out

2. What is new with HCP ? 

3. Details of cognitive solutions on S4Hana beyond what I know today 

This is going to be a blast !

Election 2016 might shake our faith in data science


First off – I am about as independent and undecided as one can get in this country. In general I am fiscally conservative and socially liberal. I am not a big fan of Clinton, Sanders or Trump – and can’t even decide who I dislike the least. But – as the primaries wind down, I am starting to follow the election with great interest. This is not merely political interest or for entertainment – it just gets me all geeked out on the potential of data science to help these candidates.

Trump is the “presumptive” candidate for GOP. That does not mean anything about his chance of winning. He is not anywhere close on any dimension to the kind of GOP candidates that went before him in past elections. So the idea of “red states” and “blue states” as it exists today do not really matter to figure out how he can win.

I am not sure if his campaign used extensive data analysis so far. On TV, it seemed like he just used his big personality (and the free media coverage in drew) as the primary weapon and it threw off the conventional campaigns of his competitors. Even the grand daddy of all data scientists who cover elections – Nate Silver – was thrown off his game by the Trump campaign. But now that he is the presumptive candidate – his campaign will probably take a more data driven approach to his approach in general elections.

The modeling approaches could get very complex for figuring out what Trump should do to attract votes. I read somewhere that his big voter base is white males without a college degree. Since education has generally increased over the years, It will be interesting how the percentages work in each state. The general theory is that black community do not like Trump because of him challenging Obama’s birth certificate issue. But if all things remain equal, even if he takes a small chunk of black community votes – he might carry the state. But then women don’t like him either apparently – which adds to the complexity of a predictive model . Past polling data and all kinds of analysis that RNC must have done – my bet is that it won’t be of much use and new models will need to be created and tweaked.

Its not any easier to predict what works best for Clinton to get the 270 magic number in general election. Math looks to be in her favor to win the primaries of her party. That said – Bernie Sanders has an extremely loyal base , especially amongst young voters. Even if Bernie himself endorsed Clinton at the convention as I think he will – I am not sure whether his supporters will care for Clinton. In that case – will they vote for her, stay home or god forbid, vote for Trump ? Also – just as GOP data scientists  will have to find what exactly works for Trump – Clinton camp will need to find messages that work against him. Given no history exists for a candidate like Trump – this exercise should wear out a lot of keyboards.

While Trump is famous for his gut instinct driving his primary wins, there is one aspect that makes me think there is a bit of data analysis that has helped his cause. His idea of tagging Bush as “Low energy”, Rubio as “Little”, Ted Cruz as “Lying” and finally Clinton as “Crooked” seems to me like a data driven strategy. May be it did not start that way and his gut instinct gave him the idea to begin with Jeb Bush. But my best guess is that his team picked up on it and tested the other adjectives before he used them effectively in his debates and stumps. Of course I can only guess – and I would love to see what comes out after the election cycle is over and someone writes a book.

I am sure a book or two will be written – this election will put to test a lot of data science and its practitioners. I can’t wait.

 

 

 

What on earth is “cognitive computing” really ?


When I first heard the term few years ago , I thought “this is somewhere between marketing buzzword and science fiction” . It is now the single most often asked question for me in my work life , and pretty soon this is going to be no different in my social circles too . Rather than provide a definition – I am sure IBM website and Wikipedia will have some articulate definitions – let me try to provide some background on how we make decisions and then try to tie it back to WTH cognitive is .

How do we make important decisions in real life – like say ordering a beverage at Starbucks ?

  
We use some data (sugar is bad for me) , we use some past experience (the Starbucks in chandler doesn’t make a good capuccino all the time)  , we evaluate some options (should I have a frapuccino instead for this one time) and then we pick a path (no I will take a capuccino but with an extra shot) that we think has best odds ( need heavy caffeine dosage to keep up with my calendar) . It’s almost never on the basis of perfect information . Decisions are mostly made on directionally correct information (frapuccino is higher in calories) , not on precise information (it’s exactly 375 calories more ) . Speed , flexibility and agility holds higher value in decision making compared to extreme accuracy and precision . If you are a barrista making a cappuccino  – there is probably a perfect ratio of ingredients , temperature etc that you use to make the perfect beverage . But getting it roughly in those ratios in quick time is more useful to you as a barrista (and probably to the customer) , than spending an extra ten minutes in making it perfect . Beyond a point , the strive for precision gets into diminishing marginal returns .

Then there is another important aspect – we have to tune out distractions to get the job done . If you are a barrista at Starbucks at Phoenix airport – at any given moment , there are three people shouting complex orders at you . You need to focus on one , and tune out the rest temporarily , and when done – move on to the next order, rinse and repeat . If you are really good at what you do – you might even be able to take the second order roughly in parallel without making the customer repeat the instructions .
Given how quickly demand can change – getting to an answer usually needs a dialog between two or more parties . If my daughter and I are ordering together our favorite green tea frapuccino  , one of us typically will change our mind at the last minute that will need the barrista to engage in a conversation with us (or at least listen to both of us and ask clarifying questions ). This is especially true when my thick Indian accent and my long name makes it harder for them . 

Perhaps the most amazing part of how we solve problems is our ability to learn and avoid reinventing the wheel . Over time , Phoenix airport barrista has figured out my preferences and only asks me “capuccino or frapuccino today?”. She knows by now that I will always take an extra espresso shot in mornings and that I don’t like whip cream in my frapuccino in evenings . 

Finally , after we take a decision – we are usually able to explain how we arrived at the conclusion we made  . And such explanation will need varying  amount of detail depending on who you are dealing with ( this is good for you is fine with my daughter , but a longer winded explanation on why I chose the frapuccino is often needed with my wife) .

While it all sounds rather complex – we do this everyday as human beings with no problem at all . Human brain is amazing – way more amazing than any computer . Trying to mimic human thought processes  with computers is incredibly complex on many fronts – and that is essentially what cognitive computing tries to do . It tries to solve problems like how we do as human beings – except at a larger scale , using more data and faster . 

Most computer programs are built on deterministic (fancy for if-then-else) logic . That is a piece of cake for computers . Cognitive computing can of course reuse deterministic logic , but it primarily needs probabilistic logic ( option A has higher odds than option B type logic ) . Often times the computer might not know the exact answer at all -and then it perhaps needs to figure out what option has lowest risk . If a fellow human being can barely understand my accent, imagine how difficult it will be for a computer to understand the words that are coming out of my mouth ! 

Now the good news is that the smart women and men working in this field have already done quite a bit of work on making computing. More cognitive . While there are many more miles to go , what is already there can bring immediate value to customers . Siri for example is a starting point on humans talking to computers for simple questions and answers . Siri has limitations – like keeping the context between successive questions asked by a user . IBM Watson can keep context across questions and can have a more meaningful dialog with a human user. But in its current form , it will be hard for Watson to tune out noise (say my wife and I both ask a question each in parallel). But over time ( and I am positive it won’t take that long)  – those challenges will go away and technology will get more and more savvy at making computers closer to human way of thinking and solving problems .

While I am all excited about the idea of cognitive computing as the future of this industry  ( well, and present too actually given we do multiple projects already using cognitive computing ) – I don’t think it has the potential to replace human information workers in huge numbers . I will explain my rationale in another post .