Tuesday, April 22, 2014

Take the Horse off the Chart

“If I had asked people what they wanted, they would have said faster horses.”- Henry Ford

Whether or not Henry Ford actually said this quote, that has been long attributed to him, is debatable. But, the spirit of the quote, certainly permeated Ford's approach to innovation. He was less concerned about what customers said they wanted, and much more focused on what he felt customers needed, whether they knew it or not. This forward-looking approach is at the heart of an innovative culture.

The first Ford Model T was introduced in 1908. While other automobiles had been available to purchase for over a decade, this was the first car that could be afforded by the masses. This is why most people remember the Model T and not the Model A, which was actually the first automobile produced by Ford. The Model A, first brought to market in 1903, was a 2-cylinder vehicle, but it was not affordable for all. Hence a key learning point on innovation: form, function, and price are often equally important.

Ford's reputation as an innovator was cemented by the 38 years of innovation that followed the Model A. Ford quickly ramped up to 1 million cars sold by 1915. The first Ford truck arrived in 1917 (it had a Model T engine), followed by an enhanced 8-cylinder Model A in 1932. Then, came the Ford Mercury in 1938 and the Jeep in 1941. Thirty eight years of innovation, never once looking back.

The product roadmap is an all too familiar chart seen across product development companies today. It doesn't matter if its hardware, software, machines, or even consumer packaged goods; everyone has a view of where the product is today and where it will go in the future. The challenge most successful companies face, which is well documented by Clayton Christensen in The Innovators Dilemma, is the trade-off between sustaining innovation (enhancing/improving your current products) versus investing to build transformative products, that attack new opportunities. The former is very easy, while the latter is technically difficult, not to mention the inherent cultural challenges.

If Ford had been pre-occupied with sustaining innovations, there would have been a horse (albeit a better horse) on his roadmap chart circa 1903.

Since we all agree that is ridiculous, then why iso many companies fail at this task? Culture, inertia, existing client requests, existing skills, etc., are among the reasons.They all make this hard, but that doesn't mean that its not necessary. Next time you are in a meeting where you see a team falling into this trap, use the phrase, "Take the Horse Off the Chart".

I'm interested in your views.

Sunday, March 23, 2014

Focus on what IS working

Winemaking is an art. While technology has brought a lot of science to the process, ultimately, the winemaker is an artist, constantly tinkering, adjusting, and optimizing for quality. If wine making was purely science, anyone with money could buy a vineyard, wrap it with technology, and produce great wine. But, they can't. In fact, countless people have failed trying.

Winemakers accept what the earth gives them each year, and adjust accordingly. If it's colder than expected, they adjust their process, and perhaps produce less barrels. But, the barrels produced are the right quality. If the soil is different than expected, they adjust, perhaps resulting in a larger production than expected. They don't try to 'fix' the soil, they adjust to the soil. Said another way, they focus on what is working, as opposed to what is not. If they were preoccupied with all the problems, they could never produce greatness.

There is a disease in corporate America that is becoming more acute: it's the inclination to focus on what is not working. I suppose it's human nature to identify problems, solve those problems, and believe that it will lead to improved performance. But, is that truly the best use of finite resources?

I recently visited a large industrial company. Like any other company that builds real things, this company has many dimensions to its business: clients that buy the machines, suppliers of components, R&D, sales, marketing, information technology, etc. Tens of thousands of employees, in hundreds of countries...highly complex operation, in a dynamic and changing industry. They are obsessed with the things that aren't working.

From what I could tell, 80% of their time and attention goes into problems in the supply chain, issues with operating in a diverse set of companies, challenges of reducing IT costs, etc. Their mental capacity is funneled into things that aren't working.

If you contrast this with a much younger and smaller start up that I recently visited, the contrast is stark. If something it not working, the startup moves onto the next thing. They understand that time and mental capacity, as the only truly finite resources, are too valuable to spend on something that is not working. They get a better return on their time, by focusing on the things that ARE working.

Does this mean that larger enterprises can ignore their problems? No. By definition, does a startup have more flexibility in what they can focus on? Yes. That being said, if you find yourself spending 80%+ of your time on things that aren't working, you are destined for mediocrity.

Wednesday, February 12, 2014

Why Relationship Selling is Doomed for Failure

I read a lot. A couple newspapers a day (WSJ, FT), ~20 blogs that I follow, countless articles/papers I find on the web/twitter (via Instapaper), and ~30 books a year (98% non-fiction). It keeps me current and I hope that it gives me an edge.

Despite all of this reading, its probably been 10 years since I picked up a ‘how to’ book on sales. Somehow, one of these (The Challenger Sale) made it onto my Amazon wish list late last year (I don’t recall why) and my sister gifted it to me for Christmas. I begrudgingly picked it up last night and quickly realized that the authors' had managed to put into words, an issue that I was aware of and had sensed, but could not articulate. While the book carries the goofy title of ‘The Challenger Sale’, the insight is far from goofy and in fact, practical and useful.

The premise is that the world of sales has changed. While it was first about product selling and then solution selling, clients are now demanding something well beyond either of those. The problem is that the traditional sales approaches put a huge burden on the customer. If a sales rep is focused on asking questions (i.e. what is keeping you up at night?, etc.), this ‘discovery’ becomes a ‘Ping-Pong match between the supplier and customer. The customer explains their needs, the rep summarizes her understanding, the customer confirms whether or not the rep got it right, she creates a proposal…” This is a burden on the client, and that is why clients quickly lose interest. Given that everyone is time-starved and many decisions have truly become consensus-driven (across IT, lines of business, etc.) a new approach and type of sales person is needed.

The authors’ research concludes that there are 5 types of sales reps:

1) The Hard Worker- self-motivated, responds to everything, goes extra mile
2) The Relationship Builder- builds strong advocates, generous w time, gets along with everyone
3) The Reactive Problem Solver- reliably responds to all requests, ensures problems are solved
4) The Lone Wolf- follows instincts, self-assured, difficult to control
5) The Challenger- always has a different view of the world, understands the clients business, loves to debate, pushes the customer

The conclusion of the research is that most sales organizations are heavy on ‘The Relationship Builder’, along with ‘The Hard Worker’. Yet, clients only get true value from ‘The Challenger’.

The attributes of The Challenger include:

-Offers the customer unique perspectives (provocative)
-Strong 2-way communication skills
-Knows the individual customers value drivers
-Is comfortable discussing money
-Pressures the client
-Not trying to win a popularity contest. They are ok with turning off some people.
-Pushes customer out of their comfort zone

As you look at these attributes, you can see why the authors’ later conclude that in our modern sales environment, ‘ Relationship Builders are doomed to fail.’

As any of us look at our organizations, we immediately know who ‘The Challengers’ are and who the others are. It’s evident in their behavior, but even more evident in their results. The Challengers are the ones that consistently deliver and also bring in the homeruns. This is because of their ability to Teach (the client), Tailor (the message), and Take control.

The rest of the book details how to develop Challengers, structure sales cycles, and ultimately transform the mindset of a sales team. I recommend it, with the understanding that revamping a sales team in the mold would take significant time.

However, my view is that every sales rep and team can evolve to this approach, simply by being aware of it, and using it to guide the next client meeting.

Thursday, February 6, 2014

A Book

Wiley (#12 publisher in the world) contacted me a few months book, to explore my interest in writing a book on patterns in big data. Their idea for the book, came from my post here. Going through the planning process for the project was eye-opening. In brief, there are no shortcuts in writing a book. Based on a message I received this week, it seems that Wiley is no longer interested in the project. The good news is that I have other interested publishers. However, this reprieve has made me stop and consider if I should really go forward this. I've decided to post the opening of the Prologue here, along with a brief outline. I welcome hearing from anyone, if this topic sounds interesting to you (ie would you want to see a book on this?)...Thanks



George Dantzig sat in his dorm room, contemplating the next 24 hours and what it would mean for his future. He came to The University of California, Berkeley with many aspirations, but as often happens, life got in the way and his best laid plans turned into dreams for another day. As he gazed over the building immediately in the foreground, he could see Sather Tower on Berkeley’s campus, known for resembling Campanile di San Marco in Venice. George reassured himself that one of his major goals was still in his grasp; he could still earn a position on the faculty, providing an opportunity to teach the next group of eager students.

It was 3pm in the afternoon and George had until 8am the next morning to prepare for what would become his defining exam at Berkeley: if he were to pass, his spot on the faculty was virtually guaranteed. Anything less than his best, and his future would be once again uncertain. This was the kind of motivator that got him to re-open the books and apply himself through the night. The last time George looked up from his book he saw 3 AM on the clock and decided he should get some rest.

As the sunrise slowly emanated around his room, George opened one eye and then the other, immediately wondering why he had not heard his alarm yet. He figured it must be an exceptionally clear day, for that type of light to be coming through his eyelids, before the 7:15am alarm that he had set. Suddenly, George felt like something was not right, sat straight up in bed, grabbed his glasses and looked at the clock: 8:30am. The exam had started 30 minutes ago, as George quickly pulled on his pants and made a dash for the door.

When George arrived at the exam hall, the surprise on his Professor’s face was noticeable, as he had concluded that George must be in the hospital or perhaps even dead, to have not been at the exam hall promptly. George, in a rushed voice, explained the situation, as his professor handed him the exam. He also noted, “George, there are 2 additional problems that I have written on the board, once you complete the questions on the exam paper.”

George, without any minutes to waste, sat in the front row and quickly started working through the questions. The exam was set for 3 hours, so when George arrived at 8:50am, many of the students were nearly half way through with the questions. 2 hours later, as the clock approached 11am, George finished the last question in the paper exam and shifted his attention to the 2 questions on the board. As 11am came, George was the only student left in the hall and it was clear that he would not even have a chance to explore the 2 questions on the board. He sheepishly walked up to his professor, re-explained the situation, and apologized that he did not get to the questions on the board. In an unexpected act his professor offered to let George have until midnight to try to complete the questions on the board, and George excitedly ran back to his dorm room.

It was now 3 PM, 24 hours since he reflected on his future in his dorm room. George had made progress on 1 of the questions and decided to give up on the other. He spent the next 8 hours grinding on question #1, felt confident that he had cornered the problem, and set out across campus to turn in his single answer. His disappointment was obvious in his posture; while he felt a great sense of accomplishment in solving #1, he knew that 1 for 2 would likely not make the cut. George slid the paper under his Professors door, grabbed a small bite at the campus cafeteria and collapsed into his bed at 1am.

George was awakened by the shrill sound of his phone at 7am, and heard his Professor on the line, “George, I can’t believe it. You actually solved 1 of the equations on the board. This is truly a historic day.” George confused by what he was hearing, asked his professor to explain why 1 out of 2 was so historic. His professor replied, “George, when I handed out the test, I told the class that I had written 2 unsolvable equations on the board. I expected anyone with extra time to play around with them, but they weren’t actually part of the test. You accomplished something that the rest of us KNEW was impossible!”.

It’s amazing what we can accomplish, when we are not encumbered by what we believe is possible. It turns out that George had solved an algorithm around linear programming, which eventually became simplex algorithm, the heart of Microsoft Excel’s SOLVER function. If George had been in the class, when his Professor said it was unsolvable, he would have never accomplished this feat. He was not limited by what the world felt was possible.

The Big Data Revolution is about accomplishing feats with data, that no one has ever believed is possible. It starts with a mindset and a temporary suspension of disbelief. If we aren’t encumbered by what others believe is possible, then anything is possible. This Revolution is about finding your POSSIBLE.


From this point, the book would go into depth on 5 stories, for different industries, on the impact of data. From these stories, I would harvest a set of Patterns, that I think are applicable across all industries. The stories are:

1) Transforming Farms with Data
2) Reinventing Customer Service with Data
3) Personalized Marketing in Retail (Selling to a Customer of One)
4) Real-time Insurance Underwriting (Pay as you Drive)
5) Why Doctors Will Have Math Degrees in 2020

I welcome any feedback in email or in the comment section below.

Wednesday, December 4, 2013

Drones and Big Data

Two weeks ago, I had a conversation with some colleagues where I was postulating a future bull market for drones, as I envisioned a number of commercial applications (food service, surveillance, etc). Coincidentally, this topic has gained major momentum since Amazon’s disclosure of a drone R&D project for goods delivery on 60 Minutes this week. Suddenly, everyone has a drone idea, most of which do not solve any real problem nor serve a clear purpose. Amazon’s proposed use of drones is just another step in their long march towards the optimized (time and cost) delivery of goods.

I recently finished reading The Everything Store: Jeff Bezos and the Age of Amazon. It's a detailed account of Amazon's emergence to date and provides countless lessons on product management, strategy, and how to treat clients. However, one recurring theme was their long-term focus on improving goods delivery. Starting from a single warehouse, to fulfillment centers, to fulfillment centers in almost all 50 states, to robotics for pick/pack, to now, apparently, drones. It's a singular focus on timely, efficient, and cost optimized delivery. So, what does this have to do with Big Data?

I believe the next major problem to be solved for Big Data is around the timely, efficient, and cost optimized delivery of insight. This requires the timely, efficient, and cost optimized delivery of data. Or more simply put, data provisioning.

As companies move towards a Big Data Architecture, delivering data to the right engine, at the right time, for the lowest cost becomes a necessity. And as long as Big Data is more than Hadoop, this will be a critical requirement.

I see companies evolving their architecture to this vision:

This diversity of capabilities is driven by one truth of enterprise technology: one size does not fit all. Clients have different workloads, needs, deliverables, and clients to serve. This reason alone is why Hadoop will not take over enterprise IT (as most companies that only focus on Hadoop postulate in a self-serving manner). Hadoop is and will be critical, but it will not be the only capability. Data provisioning (the drone of big data) is the differentiating capability to ensure an optimized (time and cost) delivery of insight. There is a substantial business opportunity in solving this problem.

One Bezos quote in the book is when he tells Tim O'Reilly, “We don’t have a single big advantage,so we have to weave a rope of many small advantages.”

This has certainly been our approach as we build out a big data platform to support our clients. Data provisioning is just one many small advantages.

Wednesday, October 2, 2013

Why Now?

Yesterday, we announced our intention to acquire The Now Factory. I think Bert did a nice job of summarizing the logic here (as usual, Silicon Angle has a great point of view), but, I wanted to provide some additional color on 3 macro trends.

Trend #1: Massive Investment, Coupled with the Need for ROI

There is a macro-economic issue that is hindering ROI in telco. In short, its the need for massive investment, in order to remain relevant. Hundreds of billions have been spent in acquiring spectrum (US- $60B, Germany- $51B, UK- $31B, India- $21B), further billions have been spent in modernizing the network (AT&T- $20B, Verizon- $17B), and hundreds of millions is being spent on driving differentiation through devices (CSP's subsidizing devices, etc). Anytime you see investments of this magnitude, ROI will be in doubt, unless there is a clear path to monetization.

Trend #2: Shift From Voice to Data

While this one may seem obvious, I'm not sure the magnitude of this shift is well understood. Plus, this has a strong relationship to Trend #1, in that while operating expenses have grown massively, the revenue (ROI) has not yet caught up. This puts the telco providers in a quandary. Two charts best illustrate this shift:

Global Mobile and Voice Data Traffic (Source: Cisco)

US Wireless Average Revenue Per Unit Per Customer (Source: Chetan Sharma Consulting)

Even worse, adoption of 'over the top' VOIP services is forcing the providers to quickly rebuild their businesses based on data. This requires a differentiating capability to monetize data.

Trend #3: New Business Models

The dynamics presented by the first 2 trends, are forcing telco providers to find new sources of revenue and profit. Emerging business models include things like mobile ads, content delivery models, and real-time charging. However, none of these business models can be executed, without the right data and data services, and integration with backend business systems.

All of which brings us to The Now Factory.

To succeed in the presence of these macro trends, telco providers must deliver new higher value products and services. In order to do that, the core capability needed is the ability to integrate network events with backend systems, in real-time. That is a hard problem to solve. You can't just throw Hadoop at it, as Hadoop alone is not insight.

The Now Factory has superior technology to deliver on the promise of insight and analytics, from network data. When you couple this with our technology in Big Data analytics, we have a unique ability to help telco providers navigate the key macro trends.

Monday, August 12, 2013

Big Data: Insight, Not Integration

In the movie Hoosiers, there is a classic scene where the coach of Hickory, Norman Dale (played by Gene Hackman) wants to calm the nerves of his team before the state championship game. His concern stems from the fact that the game is played in a large stadium, with a huge crowd, media, and other distractions. In order to focus his team, Coach Dale walks the team into the empty gym, pulls out a tape measure, and asks the team to measure the floor, then the baskets. The team promptly reports back that the baskets are 10 feet, just like every other basketball court. The team quickly grasps the message: the baskets are 10 feet and the gym is exactly the same as any court they have ever played on. Therefore, they need not worry about external distractions (gym, court, media, etc), they merely need to focus on themselves (the players) and what they do (they plays).

Big Data has reached the point of maturity that is marked by unusual announcements, with unexpected companies/organizations/parties trumpeting their use of data. Here are some recent ones I've seen:

The interest in exploiting data is not a fad, its simply a reaction to the fact that more data is now available. However, there is one pattern that I see in these examples and many of the organizations that I talk to: The world is much more interested in insight from data, than in tools to analyze data. Sure, tools may help you glean insight, but wouldn't you just prefer to have the insight, without having to look for it? Paradoxically, I see most of the IT industry focused on building tools, data management technologies (Hadoop, etc), and other infrastructure.

The winners in this race will be those that can provide insight, without the need for expansive (and expensive) tools and integration. Like Coach Dale showed his team, its not about where you play, its about what you do when you are on the court. When it comes to Big Data, the world wants insight, not integration.