Daily Dose of Analytics: Indian Politics

543 loksabha seats. 28 states (and 7 union territories), 2 major parties and several small to mid-sized ones. Reflect on 2009 elections and what you may remember is tonnes of analysis and political commentary that the front pages and editorial pages of a newspaper or the jarring voice of Arnab Goswami would’ve shove down our throat and mind. And in there, not many real recommendations. Lots of critique and counter critique. Almost passively taking sides. Yeah, part of the problem is that media can’t be seen taking sides too blatantly.

The limiting thing about the political commentary that we read everyday in newspapers and tabloids is that its too political and too top-down. Worse still, the best political analysts are sitting by the sidelines working for media houses and focusing on “what is happening?”, and in some cases trying to reinforce the biases that some of the media house leaders may have in favor or against some parties. The “hypotheses” get passed around as insights. If the result lines up, we are prone to saying “I said so”. And if it does not, we are smart enough to reengineer the explanation.

Where am I headed with all this? Recently, people have been really excited by the use of analytics in predicting the results of US Presidential Elections 2012. However, the next game changer could be using analytics to drive political results. Transforming the business of politics using analytics.

Why not approach the whole scene ground up? What does BJP need to do to win a seat in Kerala? Can it? The answer is always a yes, right? Given the resources, costs and commitments required, should it? Maybe not.

For instance, let’s take BJP’s predicament. A party which has been the second fiddle for too long now, and had a go at power once. Has a strong national recall, but low/moderate national appeal. A strong brand which stands for something, which probably the party isn’t playing to. Or is afraid of playing to. Strong foothold in a few states, swinging foothold in some, and no foothold in many.

The question to ask – Is there a way to become the party of choice for at least 60% of the Lok Sabha seats? (I have selected seats and not population. Because the eventual result talks about seats, and not the percentage of people who voted for you, or the voter turnout or some such metric).

Political analysts look at the problem in totality. Or, in complete isolation. None of them has ever tried to or would most likely be able to put together a draft success/growth strategy for BJP. If it were a consulting gig, there are far too many frameworks (opportunity assessment, market entry, investment planning , blah blah) that consultants would reuse/create. But then, most consulting gigs are also top down. That’s where analytics could score by being bottom up in such scenarios. Analytics is special in its ability to not lose much by switching from being top down to being bottom up. Though you know that the effort is much higher for bottom up recon in this case.

In your first series of interviews, you’re bound to encounter significant amount of experiential and tribal knowledge– “This is how it happens. I know it” or “That’s how that community has always been!”, “It’s a strong Dalit foothold”, “BJP needs to get away from its non-secular image”, “… find a strong young leader”, etc.
But, once you’re done with these discussions (and they are important for understanding the issues and perceptions and hypotheses), you will need to understand the voters, what may make them change their existing decision in favor of BJP or what they may like their next MP/MLA to do, etc. The answers, not surprisingly, will still be simple and basic. Some practical, some impractical. And a lot of data already exists to support most hypotheses including this one simple hypotheses – most elections fought on the back of strong infrastructural or social development go favorably for the incumbent. But there are many more triggers that influence consumer choice. And like in business, in politics too, customer can be the king.

This is the point where you’d ask me to shut up because I don’t know jackshit about all this. And of course, my political awareness is not top of the charts. Like that first quiz by Rambo at IIMB, you’d start looking for my name from the bottom of the list.

Maybe, you are right. Or, maybe, I don’t care. Because my final question remains – doing what you’re doing right now, what hope do you really have of changing the game in the coming elections? And focusing on what you’re focusing on right now, do you think you will get 272? And lastly, if not, then would you rather lose the next one too instead of focusing on something that can get you there, rather than hope for more idiocy from Congress leaders? For Congress, on the other hand, the question is, how long are you going to keep hoping that you are the best amongst a confederacy of dunces?

Daily Dose of Analytics : Coffee Shops And The Personal Touch

I have always found coffee shops to be a shared yet extremely personal space. Swarming with people, but you always get your quiet space, or the space to discuss the biggest and the most profound of topics.

Much before I started working, as a student with an insignificant pocket money, a friend and I would save just about enough money in a week or a month to the go have a cup of cappuccino at the newly opened Barista at Vasant Place market in Delhi. It was an aspirational act for us. Back then, a cup of coffee costing 30 bucks was a luxury that middle class students like me could not afford every day. I survived a week on bus passes and about 100 bucks. With chole bhature in college canteen costing 5 bucks, it wasn’t too difficult, in case you are wondering. However, But for the coffee shop manager, I was somewhat of a regular.

During my MBA days, I welcomed the opening of the Café Coffee Day inside the campus. While the poor guys had stiff competition from the legacy Nescafe machine serving super sweet desi coffee for 5 bucks or so, there were loyalists who would go to the café regularly. I would do that sporadically (continuing financial constraints). Yet, while at the café, it was a personal experience. Reason – the fellow at the counter knew me well enough by my third visit, and my order as well.

The phenomenon continued with me and Tushar playing “Jaadu Hai Nasha Hai” on the jukebox of the CCD at Ispahani Center in Chennai, or the string of coffees (mostly with biwi, TG, Shumeet, Shilpa, NehaG, Sulabh, Aziz and/or several others at Inductis) at the CCD at Solitaire Plaza on MG road. At these places, the old age touch of the coffee shop team knowing you, smiling, understanding what you’re going to order, and gradually establishing a personal connect with you was a part of the reason why I would go to the same coffee shop over and over again, even as the very cup of coffee became a standard output from one outlet to another. And more outlets, maybe closer to where I was, popped up at regular intervals. Ajay (at CCD Solitaire) even invited us for his marriage, even though he was really confused about who’s dating who for a very long time, given the NC2 combinations of coffee-ing!

Over the last few months, there are two coffee shops that I have frequented with great regularity. The Di Bella at BKC, and Gloria Jeans at Powai. However, these two are regulars because they are convenient. Whenever I am in BKC (was the norm when I was still working and continues even now with the people I meet there), it’s the only half decent option. CCD’s coffee quality has become despairingly bad in the last year and a half. GJC in Powai is also close to home, half decent coffee, has power connector points for me to work uninterrupted for some time, and enough quick bite options close by. And is open till about 1AM.

Now, in both these cases, I don’t think the folks would recognize me from one visit to another. I would probably need to strip and dance before they’d start recognizing me on my subsequent visits. Like the coffee, the customer is becoming a standardized product, is it?

The answer is no, in all likelihood. And that’s where Retail/POS Analytics should help do the job that the friendly neighborhood stores were doing so effortlessly. All the nearby stores would know me and my parents, back in the days, because of several factors – smaller/closer communities, repeat visits, continuity of the people who managed the same store over years, and lastly, a general culture of taking interest (which the modern world can called nosy as well). Retail Analytics can make this very easy for most.

An example that comes to mind – the small touches that Amex customer care often adds. For instance, last year, I called them for a query in February and they knew that my birthday and my anniversary are around the corner. How? B’day is easy. But the year before, I had some purchases around those dates, the address on my file and my wife’s file are the same, and lastly, my wife had purchases around the same date. Someone inferred it to be an important date. Not exactly what, but most likely, the flag of an important went up. The customer care executive promptly asked me if I had any vacation plans and if I needed any help. To the extent of suggesting that I could redeem some points against some of the travel options because I had a very healthy point balance.

Earlier, a lot of these required manual effort. Like that branch manager at your bank, or the store owner at the nearby store, or Ajay continuing at the same CCD for two years on the go. Now, data quality (better organized and cleaned data being made available in large volumes), and simple analysis can make it very easy for the POS person. If nothing else, a simple name to call out for and the last four digits of the credit card being swiped could start establishing the relationships, right? Next swipe, bring out that 10% discount coupon for registering – bingo – name and address collected. Follow it up with Coffee Clubs/ Loyalty – wonderful retention. Especially, for GJC, in an area where there are approx 5 or 6 coffee shops close to each other. Will occasional errors happen? Yes. But as long as the touchpoint is consciously used as a positive reinforcement, the impact cannot be negative. Analytics should take care of the machine so that the touchpoint can continue to be more human.

On that note, why hasn’t any coffee chain thought about organizing coffee evenings for groups of friends? Movies, Coffee and Sandwiches. ☺

 

[The Daily Dose series could evolve into a series of stray thoughts on analytics in daily life)

Betting Big on Big Data?

Are you in a hurry to catch up with all the Big Data news and how it’s going to affect your organization? Are you worried that you’ve missed the social media bus? Or, is someone telling you to move to the cloud? Hadoop? Terabytes and Petabytes of information that you need to process? Real time systems and dashboards? Enterprise mobility solutions? iPads? Micro-segmentation? Platform enabled solutions? 1-to-1 solutions?

If you’re close to a coffee shop, I’d recommend you walk in, get yourself a cup of coffee and sit by the window and relax. Some of us want you to over-react and buy technology, services, analytics, cloud, or something else because that’s what we always do. And that’s what you always do with your customers. Once you’ve identified a buzz word, you want everyone to catch on to it. We want you to geel that you’re missing out on something groundbreaking.

Even though the fundamentals of what is being consumed hasn’t shifted significantly. What do I mean by that?

  • Are you selling a different product?
  • Are you selling it to a new customer?
  • Have the underlying economics changed? Of creating/delivering the product experience?
  • Has the channel changed? Are all your customers shifting online?

 

And several such basic questions. What exactly are you expecting this investment to deliver for you? And while you’re still noodling over big data and analytics, think about this –

  • Big Data is a very contextual thing. For my mother, big data would mean that all three of us siblings start talking at once. For the HR department of a small organization, it would be sifting through the paperwork required to get everyone a work-permit in the various countries where our team members might be required to work. For the marketing team of another organization it could be the buzz that each of their campaigns is generating across channels, and whether its effectively being tracked
  • Data has always been big, in relative context terms. What has been a challenge is your ability to process this data. Microsoft excel moved from 65k rows to a million plus and continues integrating it with other Microsoft database tools to add more functionality. Likewise, programming interfaces started developing intuitive UIs for tech-incapacitated analysts carry on with their analysis. The tools will evolve to support the needs of the hour. Your need is to evaluate your game and what lies ahead, and not get caught up in what the critics are saying all the time (not to say that you should never listen to them). Don’t always look in the rear view mirror. And don’t always listen to the back-seat driver. Sometime’s you have to deal with the cockpits.
  • The basic rules of engagement have not changed. Analytics should focus on the business. Business should always start with the basics. One of the best managers I worked with had this habit of never recommending anything analytically complex to start with, but rather focusing on a few questions. Consulting firms take a lot of pride in their hypotheses driven approach to problem solving. The same applies here. The analysis/analytics/modeling etc. is a tool to answer the needs of the business. It is not the answer itself. I think it was Einstein who said that if I have finite time to solve a really difficult question, then I’d spend 95% of the time thinking about the right questions to ask, because asking the right question invariably gets you to the right answer.
  • Don’t let it go the IT way. Remember the large scale technology investments in your data warehouses, organization systems, POS implementations, etc. Remember how you realized every three months that something was not being captured by that system? Or, not accurately enough. Almost every client that we have worked with, and this includes the ace financial services firms, insurance firms, retail giants, etc. using analytics heavily, the quality of data has been suspect. For three reasons – the difference between legacy systems and incremental value added infrastructure for specific needs. B) No clear owner. C) Constant back and forth between business and technology on what is required vs what is possible. Analytics is at the same cross road, and combined with the mistakes made on the data quality front, you will soon find yourself repeating your mistakes in a more real time manner.
  • Differentiate between analytical capabilities, technological capabilities and business capabilities. Technology will help you process big data, but you need analysis capabilities to question the changing dimensions of your business. And if any analysis that is not tied to the business it’s impacting, it might as well stay in the analyst’s laptop.

It’s not to say that you should not invest. It’s time you start running a fact based business, if nothing else. Or, as consultants are blamed for – stop pulling insights out of your backside. It’s time you developed the capabilities to do backward and forward looking analysis backed by strong business cases and communicated through effective visualization and quality dashboards. At the same time, don’t get swayed with this large wave of discussions. Its time you pulled out the rocking chair in the attic, sat back, and thought about how to do more with less, and how to get the basic fence in place. Because, it’s also the time when confusing information will hit you at ever increasing speeds.

Appreciation. And Ownership.

I promised to reflect back on a conversation with SN – ownership and the need for appreciation.

Scene: After a very long and frustrating day of work where tempers are flared, deliverables are affected, the manager has been visibly unhappy with the proceedings, and you barely scrape to the finish line. Has most likely happened to all of us at one point or the other. That is exactly the moment someone decides to confront the manager – “You don’t appreciate us. All the hard work we put in. You just don’t appreciate.” Ouch!

Déjà vu anyone?

Side note: This is extremely reminiscent of relationships of the boy-girl kinds, and occasionally, the parents-children kinds.

The reactions I’ve observed from managers (including myself) are –

  • WTF??????
  • Appreciation? What? How? When?
  • What does s/he mean by that?
  • Give me something to appreciate damn it.
  • What was that just now? If not appreciation?
  •  You still have your job despite all the f*** ups. Isn’t that appreciation enough?
  • You’ve got to be kidding me!
  • Cut me some slack, will ya?
  • That so? Hmm. Didn’t realize.
  • Hmm. Will try and appreciate from hereon. Thanks for bringing it up. I know it’s hard man! (or some version thereof)

The lone wolf hopes against hope that the last and the least probabilistic outcome happens. But well!

There are two sides of the story, all the time –
The top-down story:

  • We are under the kind of pressure that you cannot imagine right now (P&L, salaries, collections, quality perception, client breathing down our neck, the next client, etc.). While I shield you from all that, I expect you to hold your end of the bargain – engagement/product delivery/ product release, etc. I expect you to do work which you own, which is error free, and which can hold itself up to the highest standards of quality. And if you don’t, does the hardwork count for much?
  • It’s a hard fought race and everyone’s running hard. At the end of it , you end up on the podium. Or not. And then, appreciation comes in two formats –On the podium, the pat on the back, the high fives, the whistles, the champagnes. In the backroom, you all hug each other, wish luck for the next time, and get ready for another one.
  • If you have time to cry, you have time to get more done. I don’t get time to appreciate myself either. And I can’t wait for someone’s appreciation to make it work. And work better.
  • Didn’t you walk in saying you are the smartest go-getter thing since Kamaal R Khan? Prove it!
  • Sometimes, just accept that our experiences might have made us a shade wiser and humbler. Also, we are the ones getting busted out there. Can you for once, just take what I am asking you to do and get it done? Why does the world always have to be perfect?

The analysts’ view

  • Is it my problem that you don’t have a life?
  • Is it my problem that I have 4975 friends on facebook who all are as active as me? And that I need to know? And Like their updates? Otherwise, they won’t like mine?
  • Isn’t number of hours and face time considered to be the metric for performance?

OK! Jokes apart –

  •  You never told me that we were signing up for this?
  • I am just starting here. So, I will make mistakes. Is it that big a crime to make mistakes?
  • It just gets hard when the air is so intense all the time
  • I actually had no idea what the work is going to be like. I am not sure if I am enjoying it.
  • What’s the need to shout when the same thing can be said nicely?
  • I need some coaching here, and I am not getting it.
  • That guy does not work half as much as me in his project. Or, her manager is so cool about coming late/ working from home.
  • How were you doing it when you were an analyst?

I have no intention of sitting in judgment here. And employee motivation and mentoring is a field of research of its own. Just that it was an interesting discussion about an oft discussed topic 🙂

 

Refocusing & Restarting

A ‘moment’ happened earlier today, as I was wondering why in the name of heavens is the quality of thinking so poor in the analytics industry in India. Despite the fact that there is a lot of good, great, interesting talent out there. One thing led to another, and I was looking at the etymology of analytics and analysis. Somewhere there (wiktionary and such sites), I re-learnt that analysis is about dismantling and loosening. And analytics is the set of principles governing various forms of analysis. And in there, I felt that analytics, by definition, should be, then, a top down process. One should always start with a problem and keep dismantling it using structured processes and principles and frameworks to get to the insights/solutions that is being sought. However, I reflected on the hundreds of interactions (interviews and otherwise) that I’ve had, and realized that a vast majority of these conversations are bottom up. People look at large volumes of dismantled information and try to aggregate them into meaningful buckets.  There is very little structured dismantling and a whole lot of fishing. Over a period of time, it turns most smart analysts into “crunchers”, rather than “thinkers”. And somewhere in there is the bane of the entire analytics industry. There are volumes written about how analytics has not been central to business, but just a support. This, when the truth is that all strategy is nothing but analytics.

Its been a while since I have posted regularly o this blog. Two big reasons – the work schedule was so consuming that I hardly ever felt like thinking and writing about analytics once the day was over. I filled the rest of my day with my other interests, and the movie blogging took over. Second reason, which contributed heavily to the radio silences over the last year or two was the navigation around restrictions imposed by the firm’s social media policy. My previous organisation, being a large audit firm before it became an advisory firm, was a lot fastidious and careful about the personal blogs maintained by employees, especially at more senior levels. And as most of you know, I have a tendency to be frivolous on my personal blog. That’s a strict no-no for the firm’s so-me policy.

To start with an update, I have finally taken the big decision. I am not working anymore. I put in my resignation a few months back, and at the beginning of this month, had moved on completely from my job. Having taken the last three works as cooling off period,and also to do a lot of thinking about what I really want to do going forward, I am happy to announce that I haven’t moved an inch. I am still unsure, still pondering, still evaluating, and thankfully, writing down my options.

Hmph. With that update on the side, one of the things I have promised to do better is to pay attention to my blogging. I have accumulated a volume of experience (presumably, large) over the last 9-10 years, and I believe it’s in an industry where skills are somewhat nascent. We are all learning as we speak. So, there is some merit in me reflecting on those years and organizing what I learnt.

In 2007, I had started an analytics start-up series, which I never got down to completing. Today, probably, some of it would change, and some of it would remain the same. Maybe, it’s time I completed my research paper. 😉

Between 2007 and 2012, Mckinsey has given the term Big Data to the world, and a million people are going crazy defining what it means to the world. And more importantly, creating a serious amount of confusion about it. I intend to put my own cent(s) to this confusion, and hopefully, simplify (for myself) what it means.

And now, every day, there is more that is being spoken, discussed, forecasted. I do have my point of views on those, as well as some original thoughts. I hope to start getting these inked as well.

Wish me luck!

Big Data – A Hype?

Almost uncanny, this thought coming from Jim Davis, around the same time as this post from me.

Here’s how I like to look at it: High-performance computing is, simply, an enabler. Most importantly, it enables you to get answers faster than before. But – and this is important – high performance computing is only as good as what you’re computing.

No matter how fast you go with summary statistics, you’re never going to get to the future.

At its most basic level, high-performance computing reduces the time dimension.

The Pit, The Fall and The Reichenbach Retreival

The world is abuzz with words like Big Data, Cloud, Efficiency, Real-Time, Analytics, Power, Complex, etc. Most companies are being implored to think about these words. By organizations that are building “capabilities” around it.

Somehow, I think, we are missing the point.

The problem can never be smaller than the tool that solves it. And that to me, has been the bane of the world of analytics/big data/ <fit the new key word> professionals. They create a world where the tool becomes bigger than the end goal it serves. Right from poorly implemented data warehouse solutions, to point-in-time  dashboards that answer a question relevant 5 years back. Do you need to solve your “Big Data” problem? Or, do you need to solve for your business issues?

  • Nevertheless, over close to a decade of trying to be an analytics professional, I have seen more examples of organizations trying to latch on to a fad rather than focus on what they have. Social media is just an example.

You’re walking towards a pit, and you should do it only if you’re fond of them and the treks and the views they offer. Not to fall.

If you are just about venturing into the web world, a free plugin of google analytics can reveal a lot to you, to get you started. Instead of a million dollar investment in sophisticated tools and dashboards. Secondly, a good looking dashboard does not always reveal something additional. It reveals in a palatable way. If you’re focused on what you want, that is, if you’ve figured out your business problem, you don’t always need that sexy solution.

Reminds me of that debate we had about two attractive girls – the difference between the two was that the first was sexier, but the second more marriageable. The second had more suitors while the first evoked desire a lot more often.

  • A lot of these giant ideas fail. You do fall in those pits. Investments that don’t seem to be worth it. Models which stop being predictive unless fresh blood is pumped into them at regular intervals. Technology that becomes obsolete faster than your ability to eat French fries. You will make mistakes, and I guess that’s not that bad an idea, but the least you need to do is be aware of the costs.

For instance, internet was the in-thing. It still is. But the world is already looking at mobile as the next big thing. Right from iOS to Android enabled devices to extremely interesting content delivered in real-time. For every dotcom that succeeded, there are many that got bust because the fundamental idea itself was not thought through.

Microsoft Excel could handle 65k rows of data with great difficulty at some point. Today, it can handle a million rows with lesser amount of difficulty! And people have started talking about billions of data points. However, a lot of beautiful insights are driven sometimes out one unbiased and strong validated hypotheses. Which often, smartly done, does not take a million rows.

  • Last point – it’s difficult to recover from a large investment gone wrong. And I do not always mean monetary investment. Most firms fail to look at executive time investment on half-baked investments as a loss. The opportunity cost of such falls may be significant.

EndNote: The environment today has given us more power in our own hands than most of us are capable of handling. It overwhelms us. We run hither tither and grope around to latch on to the keywords that supposedly enlightened souls are spewing at venomous speeds. No-one is wrong. Yet, the right question for you needs to be the same as it has always been – Is this what the business needs? No. Not wants!

In the Indian mythology, the tale of Bhasmasur is fairly popular. Bhasmasur was a demon who practiced austerity of several years in extreme conditions to please the gods. Lord Shiva appeared in front of him and agreed to grant him a boon in return for his perseverance and dedication. Bhasmasur requested for a power that allowed him to burn anything down to ashes (bhasma) that he’d put his hands on. As the powers, and hence, the tyranny of Bhasmasur reached its pinnacle, the other Gods implored Lord Vishnu to save them. Vishnu took the form of a beautiful danseuse (Mohini Avatar), and tricked Bhasmasur into copying his (or her) dance steps. However, as Mohini put her hand on her own  head while dancing, so did Bhasmasur on his head. And thus, Bhasmasur burnt himself down.  Bhasmasur forgot the reason why he aspired for that powerful hand. The hand that burnt him down.

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