Monday, July 27, 2015

Reinventing Retail: Customer Intimacy in the Data Era

Retail has continually reinvented itself over the past 100-plus years. Every 20 to 30 years, the form of retail has changed to meet the changing tastes of the public. McKinsey & Company, the global strategy consultancy, has explored the history of retail in depth, citing five distinct timeframes:

*1900s: The local corner store was prominent in many towns. These small variety stores offered a range of items, including food, clothes, tools, and other necessities. The primary goal was to offer anything a person would need for day-to-day life.

*1920–1940: The corner store was still prominent but had grown to a much larger scale. In this era, department stores first began to emerge, and some specialization of stores began to occur.

*1940–1970: In order to effectively deal with some of the specialization seen in the previous era, this timeframe was marked by the emergence of malls and shopping centers. This allowed for concentration of merchants, many of whom served a unique purpose.

*1970–1990: Perhaps best described as the Walmart era — a time when large players emerged, putting pressure on local store owners. These massive stores offered one-stop shopping and previously unseen value in terms of pricing and promotions. The size of these stores gave them economies of scale, which enabled aggressive pricing, with the savings passed on to the consumer.

*1990–2008: This era was marked by increased focus on discounting and large selection, coupled with the emergence of e-commerce.

Each era represented a significant innovation in the business model, but more important was the impact it had on each part of the retail value chain: merchandise and pricing, store experience, and the approach to marketing. Each new era has longed for balancing the new innovations and expansion with a key hallmark of the past: customer intimacy.


Retail, by definition, is mass market. It has been through every era. While subtle changes in approach have occurred, very few have captured the intimacy of the original corner store. The corner store’s owner knew the customers personally; he understood what was happening in their lives, and the store became an extension of the community. In the Data era, mass marketing can reclaim the corner-store experience.

Stitch Fix

Stitch Fix is a data era retailer, focused on personalizing a shopping experi- ence for women. While many women love clothes shopping, Stitch Fix realized that it is an inefficient experience today. It requires visiting many stores, selecting items to try on, and repeating. In fact, a successful shopping trip requires a relatively perfect set of variables to align:

*Location: A store must be near the shopper.
*Store: The store itself must interest the shopper and draw them in. Clothing: The clothing in the store must be of interest to the shopper.
*Circumstance: The clothing must match the circumstance for which the shopper needs clothes (dinner party, wedding, outing, etc.).
*Size: Even if all the preceding elements are present, the store must have the right size clothing in stock.
*Price: Even if all the preceding elements exist, the shopper must be able to afford the clothing.

To some extent, it’s amazing that all of these variables ever align. And perhaps they do not, which leads to compromise. But if all the variables could align and occurred repeatedly, would the shopper be more inclined to buy? Yes, and hence the premise of Stitch Fix.

Stitch Fix is disrupting fashion and retail, targeting professional women shoppers who want all the variables to align. These women do not have the time nor perhaps inclination to search for the alignment and hence, Katrina Lake, the CEO and cofounder states, “We’ve created a way to provide scalable curation. We combine data analytics and retail in the same system.”

When a person signs up for the service, she provides a profile of her prefer- ences: style, size, profession, budget, etc. The data from that profile become attributes in Stitch Fix’s systems, which promptly schedule the dates to receive the clothes, assign a stylist based on best fit, and enable the stylist to see the person’s profile (meaning her likes and dislikes). The customer also specifies when and how often she wants to receive a fix, which is a customized selection of five clothing items. Then the data-and-algorithms team will present sugges- tions to the stylist. This recommendation system helps the stylist make great decisions. Once the customer receives the fix, she can keep what she wants and send back the rest. Stitch Fix obviously maintains the data on preferences so that, over time, it becomes a giant analytics platform, where recommendations can be catered to a unique shopper. Not since the corner store has such intimacy been available, and it’s all because of the data. Clients are happier, the job of the stylist is easier, and this data then feed into the backend processes.

Retail is a difficult business. Fashion retail is even harder. It’s not as simple as managing the supply chain (although that’s not simple) because changing styles, seasons, and tastes are overlaid against the more traditional issues of sizes and stock. Any one poor decision can destroy the profit of a fashion retailer for a particular period, and therefore making the right decisions is at a premium. Stitch Fix attacks this challenge with human capital. Said a different way, this is not your typical management team for a fashion retailer. The leader of Operations at Stitch Fix comes from, while the analytics leader was previously an executive at Netflix. In a sense, Stitch Fix is building a supply chain and data analytics company that happens to focus on fashion. Not the other way around.

The company is making the bet that better customer insight will resolve many of the common fashion retailer issues: returns (ensuring fewer returns), inventory (predicting what people will want), and higher inventory turns (stocking things that customers will buy in the near-term). While Stitch Fix may not succeed as a retailer (although we think it will), it is laying the groundwork for the architecture of a retailer in the Data era.

Ms. Lake makes it clear that the company is first and foremost a retailer, but a retailer with a unique business model incorporating data and technology. Lake says, “We are a retailer. We just use data to be better at the core functions of retail. It’s hard to buy inventory accurately without knowing your customer, so we use data in the sourcing process as well.” She cites the example of looking at not just basic sizes (S, M, L or 2, 4, 6) as most buyers would, but looking at the detail of inseam size too. They can use this level of granularity in the buying process because of data. This attention to detail leads to a better fit for their clients and a higher likelihood those clients
will buy.

Most data leveraged by Stitch Fix is generated by the company. Their advantage comes from the large amount of what Lake calls explicit data, which is direct feedback from clients on every fix. That’s specific, unique, and real-time feedback that can be incorporated into future fixes and purchases. The buyers at Stitch Fix, responsible for stocking inventory according to new trends and feedback, love this data, as it tells them what to buy and focus on. As Lake says, “What customers buy and why, and what they don’t buy and why not, is very powerful.”

Stitch Fix has analyzed over 500 million individual data points. While the company has shipped over 100,000 fixes, no two have ever been the same. That’s personalization. The company sells 90 percent of the inventory that it buys each month at full price, again because of personalization. Data and personalization have the impact of delighting clients while revolutionizing the metrics of retail.


Zara’s business model is based on scarcity. In a store, if a shopper sees a pair of pants he likes, in his size, he knows it’s the only one that will ever be available, which drives him to purchase impulsively and with conviction. Scarcity is a powerful motivator. In 2012, Inditex (the parent company of Zara) reported total sales of $20.7 billion, with Zara representing 66 percent of total sales (or $13.6 billion), with 120 stores worldwide. Scarcity can also be a revolutionary business model and profit producer.

Amancio Ortega was born in Spain in 1936. In 1972, he founded Confecciones Goa to sell quilted bathrobes. He quickly learned the complexity of fashion, extending to retail, as he operated this supply chain of his own creating. Using sewing cooperatives, Ortega relied on thousands of local women to produce the bathrobes. This was the most cost-effective way for him to produce robes, but it came with the complexity of managing literally thousands of suppliers. This experience taught Ortega the importance of vertical integration or, said another way, the value of owning every step of the value chain. He founded Zara in 1975, with this understanding.

Zara uses data to expedite the entire process of the value chain. While it takes a typical retailer 9 to 12 months to go from concept to store shelf, Zara can do the same in approximately two weeks. This reduced timetable is accomplished through the use of data: The stores directly feed the design team with real-time behavioral data. Zara’s designers create approximately 40,000 new designs annually, from which 10,000 are selected for production. Given the range of sizes and colors, this variety of choice leads to approxi- mately 300,000 new stock keeping units (SKUs) every year.Chapter 4: Personalizing Retail and Fashion 67

Zara’s approach to the business has become known as fast fashion, as they will quickly adapt their designs to what is happening on the store floor, usher new products quickly to market, and just as swiftly move onto the next thing. This fast pace drives incredible efficiency in the implementation of the business model, yet at the same time, it creates enormous customer loyalty and intimacy, given the role of scarcity. Since the business can react so quickly, there is always sufficient capacity to produce the right design at the right time.

Zara’s system depends on the frequent sharing and exchange of data through- out the supply chain. Customers, store managers, designers, production staff, buyers, and warehouse managers are all connected by data and react accord- ingly. Data drives the business model, but it’s the reaction to the data that produces competitive advantage. Many businesses have a lot of data, but very few utilize it to rapidly effect decision making.

Unsold items account for less than 10 percent of Zara’s stock, compared with the industry average of 17 to 20 percent. This is the data in action. According to Forbes, “Zara’s success proves the theory that if a retailer can forecast demand accurately, far enough in advance, it can enable mass production under push control and lead to well managed inventories, lower markdowns, higher profitability (gross margins), and value creation for shareholders in the short- and long-term.”


Stitch Fix and Zara each provide a glimpse into the future of retail. It's not simply about ecommerce and automation. Instead, with the power of data, a retailer can redefine core business processes and in many cases, invent new ways of interacting with customers. This new level of intimacy changes the role that a retailer plays in a consumers life; from a sales outlet to a trusted advisor. However, knowing what needs to be done is easier than actually doing it — therein lies the challenge for all fashion designers and retailers.

This post is adapted from the book, Big Data Revolution: What farmers, doctors, and insurance agents teach us about discovering big data patterns, Wiley, 2015. Find more on the web at

Monday, July 6, 2015

100% Effectiveness

In a recent profile, Reid Hoffman declared that he is only operating at 60% of capacity/effectiveness. Given that this is coming from the founder/Chairman of LinkedIn, and someone who is also a Partner at Greylock, it makes you think twice. It made me wonder if I'm setting the bar too low.


The Stanford Graduate School of Business has done a nice job with its 'Insights' program. All/most of them are available to view online. I recently watched the one with Steve Schwarzman and his views on talent and hiring resonated with me.

He talks about assessing the talent in your organization on a scale of 1 to 10 (10 being best). He says,

"If you're a 10, God bless you. You'll be wildly successful. If you attract 10's, they always make it rain if you need rain. A 10 knows how to sense problems, design solutions, and do new things.

A nine is great at executing. They come up with good strategies, but not great strategies. A firm full of nines, that's a winning firm. Eights, they just do stuff that you tell them. And sevens and below, I don't know what they are since we don't tolerate them."

Let me paraphrase and augment the descriptions a bit:

-designs great strategies
-leads from the front
-senses problems/issues and resolves them
-constantly drives new initiatives and creates new value
-executes and delivers...over and over again

-designs good strategies
-demonstrates attributes of a great leader
-executes flawlessly
-resolves issues quickly, as they are understood or highlighted

-executes flawlessly

7 and below

I realized when I heard Schwarzman talking and then paraphrased per above, that my post on "Principles of Great Performance" was a bit off. In that post, I really defined the principles of an 8 or 9 performer. This confirmed that I am mentally setting the bar too low.

Perhaps I am operating at a mere 50% of capacity.


Other great Stanford Insights interviews:

Ajay Banga, Mastercard
Marc Andreesen, a16z
Vinod Khosla, Khosla Ventures

Tuesday, June 16, 2015

Monday, June 15, 2015

Scale Effects, Machine Learning, and Spark

“In 1997, IBM asked James Barry to make sense of the company’s struggling web server business. Barry found that IBM had lots of pieces of the puzzle in different parts of the company, but not an integrated product offering for web services. His idea was to put together a coordinated package, which became WebSphere. The problem was that a key piece of the package, IBM’s web server software, was technically weak. It held less than 1 percent of a market..”

“Barry approached Brian Behlendorf [President of the Apache Software Foundation] and the two quickly discovered common ground on technology issues. Building a practical relationship that worked for both sides was a more complex problem. Behlendorf’s understandable concern was that IBM would somehow dominate Apache. IBM came back with concrete reassurances: It would become a regular playing in the Apache process, release its contributions to the Apache code base as open source, and earn a seat on the Apache Committee just the way any programmer would by submitting code and building a reputation on the basis of that code. At the same time, IBM would offer enterprise-level support for Apache and its related WebSphere product line, which would certainly help build the market for Apache.”

-Reference: The Success of Open Source, Steven Weber 2004


In the 20th century, scale effects in business were largely driven by breadth and distribution. A company with manufacturing operations around the world had an inherent cost and distribution advantage, leading to more competitive products. A retailer with a global base of stores had a distribution advantage that could not be matched by a smaller company. These scale effects drove competitive advantage for decades.

The Internet changed all of that.

In the modern era, there are three predominant scale effects:

-Network: lock-in that is driven by a loyal network (Facebook, Twitter, Etsy, etc.)
-Economies of Scale: lower unit cost, driven by volume (Apple, TSMC, etc.)
-Data: superior machine learning and insight, driven from a dynamic corpus of data

I profiled a few of the companies that are exploiting data effects in Big Data Revolution —CoStar, IMS Health, Monsanto, etc. But by and large, big data is an unexploited scale effect in institutions around the world.

Spark will change all of that.


Thirty days ago, we launched Hack Spark in IBM, and we saw a groundswell of innovation. We made Spark available across IBM’s development community. Teams formed based on interest areas, moonshots were imagined, and many became real. We gave the team ‘free time’ to work on Spark, but the interest was so great that it began to monopolize their nights and weekends. After ten days, we had over 100 submissions in our Hack Spark contest.

We saw things accomplished that we had not previously imagined. That is the power of Spark.

To give you a sampling of what we saw:

Genomics: A team built a powerful development environment of SQL/R/Scala for data scientists to analyze genomic data from the web or other sources. They provided a machine learning wizard for scientists to quickly dig into chromosome data (kmeans classifying genomes by population). This auto-scalable cloud system increased the speed of processing and analyzing massive genome data and put the power in the hands of the person that knows the data best. Exciting.

Traffic Planning: A team built an Internet of Things (IoT) application for urban traffic planning, providing real-time analytics with spatial and cellular data. Messaging queues could not handle the massive and continuous data inputs. Data lakes could not handle the large volume of cellular signaling data in real-time. Spark could. The team exploited Spark as the engine of the computing pool, Oozie, to build the controller module, and Kafka as the messaging module. The result is an application to processes massive cellular signal data and visualizes those analytics in real-time. Smarter Planet indeed.

Political Analysis: A team built a real-time analytics platform to measure public response to speeches and debates in real-time. The team built a Spark cluster on top of Mesos, used Kafka for data ingestion and Cloudant for data storage. Spark Streaming was deployed for processing. Political strategists, commentators, and advisors can isolate the specific portion of a speech that produces a shift in audience opinion. The voice of the public, in real-time.

Spark is changing the face of innovation in IBM. We want to bring the rest of the world along with us.


Apache Spark lowers the barrier to entry to build analytics applications, by reducing the time and complexity to develop analytic workflows. Simply put, it is an application framework for doing highly iterative analysis that scales to large volumes of data. Spark provides a platform to bring application developers, data scientists, and data engineers together in a unified environment that is not resource-intensive and is easy to use. This is what enterprises have been clamoring for.

An open-source, in-memory compute engine, Spark powers a stack of high-level tools including Spark SQL, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Today, business professionals have analytics in their hands in the form of visual dashboards that inform them what is happening. Think of this as descriptive analytics. Now, with Apache Spark, these can be complemented with analytics smarts built into applications that learn from their surroundings and specifies actions in the moment. Think of it as prescriptive analytics. This means that, with Spark, enterprises can deploy insights into applications at the front lines of their business exponentially faster than ever before.

Spark is highly complementary to Hadoop. Hadoop makes managing large volumes of data possible for many organizations due to its distributed file system. It has grown to a broad ecosystem of capabilities that span data integration and data discovery. It changed the speed at which data could be collected, and fundamentally changed how we make data available to people. Spark complements Hadoop by providing an in-memory compute engine to perform non-linear analysis. Hadoop delivered mass quantities of data, fast. But the real value of data cannot always be exposed because there isn’t an engine to push it through. With Spark, there’s a way to understand which data is valuable and which is not. A client can leverage Spark to augment what they are doing with Hadoop or use Spark on a stand-alone basis. The approach is in the eye of the beholder.


While there are many dimensions to the Spark ecosystem, I am most excited by machine learning. Machine learning is better equipped to deal with the modern business environment than traditional statistical approaches, because it can adapt. IBM’s machine learning technology makes expressing algorithms at scale much faster and easier. Our data scientists, mathematicians, and engineers will work with the open source community to help push the boundaries of Spark technology with the goal of creating a new era of smart applications to fuel modern and evolving enterprises.

With machine learning at the core of applications, they can drive insight in the moment. Applications with machine learning at their core get smarter and more customized through interactions with data, devices and people—and as they learn, they provide previously untapped opportunity. We can take on what may have been seen as unsolvable problems by using all the information that surrounds us and bringing the right insight or suggestion to our fingertips right when it's most needed.

It is my view that over the next five years, machine learning applications will lead to new breakthroughs that will assist us in making good choices, look out for us, and help us navigate our world in ways never before dreamed possible.


I see Apache Spark as the analytics operating system of the future, and we are investing to grow Spark into a mature platform. We believe it is the best technology today for attacking the toughest problems of organizations of all sizes and delivering the benefits of intelligence-based, in-time action. Our goal is to be a leading committer and technology contributor in the community. But actions speak louder than words, which brings us to today’s announcements:

1)IBM is opening a Spark Technology Center in San Francisco. This center will be focused on working in the open source community and providing a scalable, secure, and usable platform for innovation. The Spark Technology Center is a significant investment, designed to grow to hundreds of people and to make substantial and ongoing contributions to the community.

2)IBM is contributing its industry leading System ML technology— a robust algorithm engine for large-scale analytics for any environment—to the Apache Spark movement. This contribution will serve to promote open source innovation and accelerate intelligence into every application. We are proud to be partnering with Databricks to put this innovation to work in the community.

3)IBM will host Spark on our developer cloud, IBM BlueMix, offering a hosted service and system architectures, as well as the tools that surround the core technology to make it easier to consume. Our approach is to accelerate Spark adoption.

4)IBM will deliver software offerings and solutions built on Spark, provide infrastructure to host Spark applications such as IBM Power and Z Systems, and offer consulting services to help clients build and deploy Spark applications.

IBM is already adopting Spark throughout our business: IBM BigInsights for Apache Hadoop, a Spark service, InfoSphere Streams, DataWorks, and a number of places in IBM Commerce. Too many to list. And IBM Research currently has over 30 active Spark projects that address technology underneath, inside, and on top of Apache Spark.

Our own analytics platform is designed with just this sort of environment in mind: it easily blends these new technologies and solutions into existing architectures for innovation and outcomes. The IBM Analytics platform is ready-made to take advantage of whatever innovations lie ahead as more and more data scientists around the globe create solutions based on Spark.

Our strategy is about building on top of and around a successful open platform, and adding something of our own that’s substantial and differentiated. Spark is that platform. We are just at the start of building many solutions that leverage Spark to the advantage of our clients, users, and the developer community.


IBM is now and has historically been a significant force supporting open source innovation and collaboration, including a more than $1 billion investment in Linux development. We collaborate in more than 120 projects contributed to the open source community, including Eclipse, Hadoop, Apache Spark Apache Derby, and Apache Geronimo. IBM is also contributing to Apache Tuscany and Apache Harmony. In terms code contributions, IBM has contributed 12.5 million lines of code to Eclipse alone, not to mention Linux— 6.3 percent of total Linux contributions are from IBM. We’ve also contributed code to Geronimo and a wide variety of other open-source projects.

We see in Spark the opportunity to benefit data engineers, data scientists, and application developers by driving significant innovation into the community. As these data practitioners benefit from Spark, the innovation will make its way into business applications, as evidenced in the Genomic, Urban Traffic, and Political Analysis solutions mentioned above. Spark is about delivering the analytics operating system of the future—an analytics operating system on which new solutions will thrive, unlocking the big data scale effect. And Spark is about a community of Spark-savvy data scientists and data analysts who can quickly transform today's problems into tomorrow's solutions. Spark is one of the fastest-growing open source projects in history. We are pleased to be part of the movement.

Wednesday, June 3, 2015

Technical Leadership

As companies grow and mature, it is difficult to maintain the pace of innovation that existed in the early days. This is why as many companies mature (i.e. Fortune 500), they sometimes lose their innovation edge. The edge is lost when technical leadership in the company either takes a backseat or evolves to a different role (different than the role it had in the early days). I see a number of companies where over time, the technical managers give way to "personnel" or "process" managers, which tends to be a death knell for innovation.

Great technical leaders provide a) team support and motivation, b) technical excellence, and c) innovation. Said another way, they lead through their actions and thought leadership.


As I look at large organizations today, I believe that technical leaders fall into 3 types (this is just my framework for characterizing what I see).

The Ambassador
A technical leader of this type brings broad insight and knowledge and typically spends a lot of time with the clients of the company. They drive clients in broad directional discussions and will often be a part of laying out a logical architectures and approaches. They are typically not as involved where the rubber hits the road (ie implementation of architectures or driving specific influence product roadmaps). Most of the artifacts from The Ambassador are in email, powerpoint, and discussion (internally and with clients).

The Developer
A technical leader that is very deep, typically in a particular area. They know their user base intimately and use that knowledge to drive changes to the product roadmap. They are heavily involved in critical client situations, as they have the depth of knowledge to solve the toughest problems and they make the client comfortable due to their immense knowledge. Most of the artifacts from The Developer are code in a product and a long resume of client problems solved and new innovations delivered in a particular area.

The Ninjas
A technical leader that is deep, but broad as appropriate. They integrate across capabilities and products, to drive towards a market need. They have a 'build first' mentality or what i call a 'hacker mentality'. They would prefer to hack-up a functional prototype in 45 days, than do a single slide of powerpoint. Their success is defined by their ability to introduce a new order to things. They thrive on user feedback and iterate quickly, as they hear from users. Said another way, they build products like a start-up would. Brian, profiled here, is a great example of a Ninja. Think about the key attributes of Brian's approach:

1) Broad and varied network of relationships
2) Identifying 'strategy gaps'
3) Link work to existing priorities
4) Work with an eye towards scale
5) Orchestrating milestones to build credibility

That's what Ninja's do.


Most large companies need Ambassadors, Developers, and Ninjas. They are all critical and they all have a role. But, the biggest gap tends to be in the Ninja category. A company cannot have too many, and typically does not have enough.

Monday, April 20, 2015

Principles of Great Performance

I enjoy having the opportunity to discuss and explore career options/goals with a number of people. I captured some of those learnings in my career decisions blog. But, sometimes the discussion is not at all about decisions, and is more focused on career progression. Said another way, people ask, "How do I get ahead in my career?"

I tend to think that getting ahead is a matter of 3 items:

1) Exceeding your business targets consistently. (whatever they may be)

2) Performing tasks beyond the scope of #1 (pro-actively) and exceeding expectations on those too.
3) Distinguishing yourself as an A-player, in any and all endeavors.

I think most people typically concern themselves with #1, ignoring the other two. That may be acceptable in the short term, but will likely catch up to someone that is interested in career growth, over time.

To help you think about personal development, I want to share my view of the principles of great performance. This is primarily relates to #3 above. You've seen many of these before. These are the kind of things that probably don't fit in a performance review per se, but they do directly relate to your ability to have an impact on any business. I don't take credit for penning all of these, but I have found them to be good guideposts for success. Lastly, no one can do all of these well, But then again, aspiring to achieve them is half the battle...

Principles of Great Performance

1) Study and read often. Be the smartest person in the room on your topic. Have a point of view, grounded in facts.

2) Be proactive, not reactive, in all matters.

3) Leverage your management and executive team for timely escalation of critical topics. On the other hand, don't cry wolf.

4) Differentiate yourself: Be responsive, Be decisive, Be passionate for the business, Have a bias for action, Understand details and be organized, Prioritize, Contribute to the overall team. Don't just go through the motions, Show a sense of urgency. Be an active participant.

5) Be on time and prepared for all interactions. Whether internal or external, be prepared and ready to contribute in a professional manner.

6) Demonstrate a capability for effective "straight talk," so that tough or controversial problems are dealt with directly, with the relevant party, rather than indirectly or not at all. The ability should be demonstrated inside and outside of your company.

7) Communicate swiftly and concisely. Expand into detail when prompted or when needed.
 Brevity in communication leads to greater impact.

8) Develop productive relationships across the company, leveraging them to solve problems at the lowest level possible in the organization.

9) Complete assigned tasks in a timely manner, without requiring reminders.

10) Produce grammatically well written documents, notes and presentations with simple and clear communication. Same applies to verbal communications. Strive for clarity of all messages.

11) Take risks, make mistakes, learn from them, and strive to not repeat them.

12) Accept accountability and responsibility for all actions (committed and omitted).

13) Measure everything important and know where you stand versus those measurements. Measure goals on a consistent and excuse-free basis.

14) Exude optimism and confidence, but not hubris.

15) Lead, don't manage.

16) Have an Edge, in everything you do. If you don't have the Edge, someone else does.

17) If you are a manager, it's not about 'You', it's about 'Them'.

18) Develop a superior external network.

19) Work with an enthusiasm unknown to mankind.

20) It's better to be a pirate, than join the Navy.

21) Plan, Prepare, Plan, Prepare...repeat.

22) Master the art of motivating others.

23) Be a contrarian, with facts to support your position.

24) Develop a core competence or skill. Become 'known' for it.

25) Panic is not a plan. Don’t panic. Just plan and then improvise.

26) Confidence is a learned skill. It’s developed through practice.

27) Avoid the ABC’s of personal and business decay: Arrogance, Bureaucracy, and complacency.

28) Share your expertise actively with modern social tools: blogging, twitter, connections, etc.

29) Make your family and personal relationships a priority, despite your career goals and the demands of the job.