Archive for the ‘Musings’ Category

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!

Dofficially D!

Finally, I put to official use the name that was given to me by the people I loved working with (read, Inductis colleagues) – D

The Diamond Consulting Case Competition on campuses is called DConstruct. I think they think its D for Diamond. (wink wink). Its D for D!! 🙂

Couple of links I could find about DConstruct – Here and here

Desktop Analytics

Read this quote – (Measuring productivity, or the lack thereof, on the personal computer. “Desktop Analytics”) on Google Reader feed of Data Sciences Analytics

Interesting. Not getting into the contents of the real post, I loved the statement. Its funny how many times I have tried to measure my desktop productivity. My desktop (or, laptop) time includes work, watching cartoons/animations/manga, reading comic books, reading blogs, reading general stuff, mailing, searching for tid-bits, playing games, etc etc. Amongst all this, I have no idea if my desktop productivity is 50% or less than that! Oh Yes! I would like to believe that I am terribly busy and am doing a lot of work, but the fact is that, ignoring the dependencies of a workplace, my productivity in the last several years would never have been more than 50%. On a really bad day, it might be 100%. But that would have to be really bad day!

All ye data folks out there, is there a way to capture this data? Any tool? I am sure the results are going to depress the hell out of me. BTW, this blog post – would it go under productive use or unproductive use? My guess is that its unproductive use, unless people find a skewed logic of calling it a break to refresh my decaying brain cells and hence, increasing my productivity. And a break is a part of a productive work day, isn’t it?

Back from vacation

There folks! Just came back from a 2 week break (spent partly in my village, and partly in Chicago).

Will be right back with more and comments!

Analytics Start-Up Series – 2 of Many

I will continue with part 2 of the start up series and focus on the third part of the element wheel – New Product Design (Air)

New Product Design

What? By the time I had joined the team, we were still defining what we want to do, and what we don’t want to do. Analytics has come to connote not just data mining and predictive analytics, but even research analytics, product based analytics, dashboards, etc. We wanted to focus more on the marketing analytics, and that’s why we were MCoE.
However, the problem/uniqueness of our positioning was that we were focusing on a process driven analytics approach, while 90% of the listeners with different levels of analytics’ understanding had thought largely of product driven approach only. Even “SAS-skills” were “SAS” skills.

For whom?
Moreover, we needed to decide which all verticals we will build our presence in. That’s tricky. Being small and new, you want to prove a point. You are ready for any project that comes your way. However, if you do your first project in healthcare with your key focus being FS, the next time you go to a client, you have a healthcare case study to talk about. If it’s an FS client, you neither want to own the case study, nor disown it. Boom!

Why? The bottomline for a sales guy remains – why would someone buy what you’re selling? Is there an identified need? Is it expressed? Would you need to educate the buyer? In the Indian market, for instance, Fractal Analytics, I think, has done a great job of educating the financial services sector about the need of and opportunities for analytics. There are similar examples elsewhere and in other industries too. Having said that, if we look at the analytics market today, the education is taken care of. The market does have an expressed need for analytics. If we take a closer look, the first industries to adopt analytics were financial services and telecom. And both these sectors loved keeping their data to themselves. They built strong in-house teams. However, things have changed in the last few years as firms have started engaging third party vendors (just as they adopted consulting/consultants as third party unbiased experts with a broader view) for analytics. But today, a lot of other sectors including Public Sector, Healthcare, etc. have emerged as buyers of analytics. Marketics, for instance, had more depth in FMCG than FS, given the ex-P&G background of its leadership team

Where? From a third party vendor point of view, almost everyone wants a piece of the US analytics market. The other markets have been slower to adopt analytics outsourcing. The other truth is the relatively crowded analytics vendor market in US. Net result, apart from some of the early movers like Fair Isaac, not a lot of vendors have been able to build a large scale. However, my MCoE stint taught me that there is a significant opportunity lying the Asian and European market as well, if you have the right connections, credibility and content.

Lessons learnt – Identify a market that you understand well, and where you have the credibility to sell. Sell only a bit, and understand it in depth, and avoid trying to be everything to everyone.

Analytics Start-Up Series – 1 of Many

Yesterday, I was talking to one of my friends who wanted to get my perspective on what did I learn or not learn about starting an analytics company (having been a part of multiple start-up environments). That’s when I thought tt might be a good idea to pen down my thoughts on this.
Disclaimer: This applies to my understanding of a company which is doing offshore analytics.

I always looked at my learning along four dimensions


This post is going to be the first in a series of posts where I will write about my experiences.

1. Strategic Alignment With Overall Business

I am drawing largely from my first company experience here, which was with a very large Indian IT firm thinking of setting up an analytics practice. When I joined the team, Gayatri Balaji was leading the initiative, and she had Dhiraj Narang helping her. Me and Shivani Sohal joined her as part of our third training stint. Apart from Gayatri, the three of us were raw with no analytics background/experience.

Analytics, or Marketing Center of Excellence (MCoE) was a prized initiative of the company at that point. It was an attempt to move up the value chain by doing “intelligent” work (in the Business Intelligence way, not that the company was not doing any intelligent work!).
However, that being said, what our small and inexperienced team (with the exception of Gayatri) soon realized is that its one thing to say that we want to do “this”, and another to align it to the overall business.

There are four set of challenges that we discovered –


A. Existing product portfolio

Context : The company already had a BI practice, a CRM practice, and a lot of analytics was already being done in different relationship pockets. To put it bluntly, every time a relationship needed someone with “SAS skills”, they hired one and put him/her on the relationship. No need to aggregate the “SAS skills”, (which is what analytics job postings have reduced the required analytics skill-sets to!). Additionally, tools like SAP and Oracle have their own analytically intelligent layers, and SAP and Oracle are separate practices within the organization. Imagine your plight when you’re talking to a client about analytics and she says – “Well! But that’s pretty similar to what you’re BI team talked about. They probably had a higher product focus, though!” And you start looking at the account manager, who has probably introduced every practice to the client (to grow the account). It was not our fault that we were the new baby on the block. Interestingly, the leadership had never thought of aggregating the knowledge lying here and there in the firm to have a solid ground from the beginning.

Lesson Learnt:
If you’re going to cannibalize your existing product line, you need to be sure on what you are offering, why you are offering, and how will you work with the existing product lines.

B. Stakeholder Alignment

Context:
In the organizational power play – strategic positioning can mean that you are the weakest (fresh out of the closet) player, or the strongest player (the whole company is looking at you). Usually, you are not stuck in the middle. If you are the strongest, the performance becomes extremely short term oriented. Stakeholders want to see quick wins, proof of concepts and a latent potential (as visible through a practice bursting at the seams!)

a) “Who’s with you?” – We realized pretty soon that very few important players have been sold the concept and its importance to the overall soon. The attitude towards the new practice ranged from “This is neat!” to “Oh! So we are wasting money on analytics this time”. To an extent, the varying levels of cynicism is expected in large organizations. The problem we faced where cynicism in the mind of decision makers/policy makers.
b) “Who gets the credit?” – Driving from my other experiences, analytics team can potentially be at direct conflict/synergy with another practice. For example, an offshore analytics center (analytics outsourcing) model can be a potential threat as well as support to analytics consulting. Analytics (platform independent) can be a threat to product driven analytics, but can be used to augment the nature of analytics as well.
c) “Who gets the money?” – Given that analytics is a horizontal solution and not specific to industry, revenue recognition is always a challenge. All the verticals stake claim to the analytics revenue, while analytics unit may have a separate revenue target. For instance, a 100MM target for Financial Services vertical will be achieved through products, services, analytics, implementation, etc. However, the 20MM analytics target will be achieved through a combination of work done across verticals- such as Financial Services, healthcare. Every dollar generated by analytics team will be claimed by the respective vertical. However, the effort devoted to sell analytics will be lower, because there is no specific FS-Analytics revenue target.

Lesson Learnt: If you’re an outsider roped in to run a new business initiative, make sure that you understand the powerplay and relative buy-in. Understand the weak and strong points of everyone you’re compulsorily going to deal with. Equally important is to understand the relative aspirations that help you share, transfer credit of the work done in a politically correct manner. Sales is a tricky issue that we will touch separately later

(…to be continued)

Whats with going solo?

Avinash Kaushik writes stuff about web-analytics that can be used by novices as primers for building their understanding, and experts as content matter for strong debates. Avinash announced on his blog that he is going solo and will be a analytics evangelist for google for his first assignment.
Kevin Hillstrom of MineThatData has also decided to go solo. No need to tell the readers who Kevin is and the analytical depth with which he writes.

Now, lets Mine This Data, pun unintended! 🙂
A. The analytics market is growing insanely. There is need and space for a large number of such strong SMEs as Kevin and Avinash.

B. A thorough understanding of analytics is a fairly complex skillset, and rare. Anyone can come and talk data and profiling and dashboarding and modeling. But there aren’t too many people who understand the complete data analytics process well. Right from the vision, method, depth and technology for data acquisition to the expansive business application of analytical frameworks, while maintaing a sync the IT and Business startegy of the firm, is no mean task.

C. Most importantly – Intellectual bankruptcy. The number of analytics blogs that have come up, with people writing on specific subjects (Avinash on web analytics) to people writing on all analytics subjects (Kevin), is proof enough for me that the bubbling energy amongst all these intellectuals needs a vent. While blogging helps them think more, and beyond the scope of their day to day assignments, going solo implies they are ready to get dirty once again.

What do we have then – Market Skillset Desire! What comes out of it at the end – Pure Magic. All the best Avinash, Kevin!

Avinash – if the quality of your posts go down, I will start throwing hate mails at you! 🙂

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