Tuesday, November 15, 2016

Everything Starts With Design — You Should, Too

“There is no such thing as a commodity product, just commodity thinking.” — David Townsend

Anthony DiNatale was born in South Boston. He entered the flooring business with his father in 1921 and began a career of craftsmanship and woodworking. In 1933, he founded DiNatale Flooring in Charlestown, working job to job, primarily in the northeast United States. In 1946, Walter Brown approached DiNatale and asked him to build a floor for a new basketball team to use. DiNatale quoted him $11,000 to complete the project, and the deal was struck.

DiNatale quickly went to work, knowing that he had to be cost-conscious to complete the construction, since he had bid aggressively to win the project. He gathered wood from a World War II army barracks and started building. He quickly noticed a problem: the wood scraps were too short for him to take his traditional approach to building a floor. So he began to create an alternating pattern, changing the direction of the wood pieces to fasten them together. He kept creating 5-foot panels, and when he had 247 of them, his work was completed.

Walter Brown was the owner of the Boston Celtics. When the Celtics moved into the Boston Garden in 1952, the floor commissioned by Brown in the year of their founding went with them. The floor was connected by 988 bolts and served as the playing surface for 16 NBA championships between 1957 and 1986.

DiNatale was a craftsman, an artist, a woodworker, but most prominently a designer. He made use of what he had and designed what would become the iconic playing surface in professional sports. The floor became a home-court advantage for the Celtics, as competitors complained about its dead spots and intricacies.

Design is enduring. Design is timeless. And, every once in a while, design becomes a major advantage.

Read the rest here.

Thursday, November 10, 2016


"Don't confuse my kindness for weakness." - Joe Schutzman

I was a 26 year old consultant on my first real project in 2000, working for a client in Fort Lauderdale, Florida. The project was a mess. We had very little subject matter expertise, even less leadership, and it was going the wrong direction. Joe Schutzman was one of the people that came to the rescue. He brought a sense of calm and composure, and the ability to simplify a complex situation. And, he taught me the difference between being principled and being stubborn.

I've known a number of stubborn people in my life, probably most notably my Grandfather. It doesn't make them bad people, it just makes them, well, stubborn. Sometimes principled people get confused for stubborn, because they are so tied to their belief system. But, there is a big difference.

Joe was flexible on details, but determined on direction. This applied in business, as well as his life. This is the essence of a principled life. He was the champion of the pirate spirt in our team at work. I believe it originally came from the quote, "I'd rather be a pirate, than join the Navy." Regardless of where it originated, it embodied his spirit of never accepting the status quo. He was the consummate transformation agent, which is not necessarily common in someone so principled. There is often beauty in contradictions.

Joe passed away this morning, with his family in Kansas City. He will be missed, as a colleague, friend, father, husband, and sibling. But, his impact will live on, setting the bar for all of us to live a principled life, yet never being afraid to disrupt the status quo. He will be missed by all that knew him.

Tuesday, September 20, 2016

The End of Tech Companies

“If you aren’t genuinely pained by the risk involved in your strategic choices, it’s not much of a strategy.” — Reed Hastings

Enterprise software companies are facing unprecedented market pressure. With the emergence of cloud, digital, machine learning, and analytics (to name a few), the traditional business models, cash flows, and unit economics are under pressure. The results can be seen in some public stock prices (HDP, TDC, IMPV, etc.), and nearly everyone’s financials (flat to declining revenues in traditional spaces).

The results can also be seen in the number of private transactions occurring (Informatica, Qlik, etc.); it’s easier to change your business model outside of the public eye. In short, business models reliant on traditional distribution models, large dollar transactions, and human-intensive operations will remain under pressure.

Many ‘non-tech companies’ tell me, “thank goodness that is not the business we are in” or “technology changes too fast, I’m glad we are in a more traditional space”. These are false hopes. This fundamental shift is coming (or has already come) to every business and every industry, in every part of the world. It does not matter if you are a retailer, a manufacturer, a healthcare provider, an agricultural producer, or a pharma company. Your traditional distribution model, operational mechanics, and method of value creation will change in the next 5 years; you will either lead or be left behind.

It’s been said that we sit on the cusp of the next Industrial Revolution. Data, IoT, and software are replacing industrialization as the driving force of productivity and change. Look no further than the public markets; the 5 largest companies in the world by value are:

As Benedict Evans observed, “It is easier for software to enter other industries than for other industries to hire software people.” In the same vein, Naval Ravikant commented, “Competing without software is like competing without electricity.” The rise of the Data era, coupled with software and connected device sprawl, creates an opportunity for some companies to outperform others. Those who figure out how to apply this advantage will drive unprecedented wealth creation and comprise the new S&P 500.

This is the end of ‘tech companies’. The era of “tech companies” is over; there are only ‘companies’, steeped in technology, that will survive.

Read the rest on Medium here.

Friday, September 16, 2016

12 Attributes of a Great Leader: #5 Engagement and Influence

"Great leaders are relaxed when the team is stressed, and stressed when the team is relaxed."

I had a chance to talk with Jerome Selva about Engagement and Influence recently.

Podcast here.

We discuss:

- Informal Influence
- Getting comfortable in your own skin
- Tools for informal influence (blogs, videos, etc.)
- Looking outside your defined scope
- Emotional intelligence

In addition, Jerome shared the following for further reading:

Travis Bradberry and Jean Greaves — "Emotional Intelligence 2.0"

Emily Sterrett — “The Manager’s Pocket Guide to Emotional Intelligence”

Daniel Goleman "What makes a leader"

HBR article:

EI test:

Friday, September 2, 2016

The 4th Dimension of Enterprise Software

Charles and Miranda first met in art school in 1979. Over time, they realized a shared passion for handwork and the elegance of handmade objects for the home. Today, Charles Shackleton Furniture and Miranda Thomas Pottery, the workshops that comprise ShackletonThomas, consist of a group of individuals who share their philosophy.

Charles and Miranda think about 4 elements when creating an object:

1) Design- the shape, decoration, functionality, and style.

2) Materials- they select the best and most beautiful materials for design.

3) Craftsmanship- the precision, finesse, and functionality for how an object is put together.

4) The Fourth Dimension- “this is the element of design caused when the object is made by human hand or a tool directly controlled by human hand. All are imperfect, like the human that created it. But, the imperfections are beautiful.”

The fourth dimension is the crucial and final aspect that makes a piece of art truly great. “This is what gives life and soul to the inanimate object.”


Every incumbent player in the enterprise software market is facing a 4th dimension challenge. The first 3 dimensions are the nearly the same for everyone; it’s how they invest their R&D/SG&A across serving users, their existing clients/products, and a platform for the future.

Read the rest here.

Friday, July 22, 2016

A Practical Guide to Machine Learning: Understand, Differentiate, and Apply

Co-authored by Jean-Francois Puget (@JFPuget)

Machine Learning represents the new frontier in analytics, and is the answer of how many companies can capitalize on the data opportunity. Machine Learning was first defined by Arthur Samuel in 1959 as a “Field of study that gives computers the ability to learn without being explicitly programmed.” Said another way, this is the automation of analytics, so that it can be applied at scale. What is highly manual today (think about an analyst combing thousand line spreadsheets), becomes automatic tomorrow (an easy button) through technology. If Machine Learning was first defined in 1959, why is this now the time to seize the opportunity? It’s the economics.

A relative graphic to explain:

Since the time that Machine Learning was defined and through the last decade, the application of Machine Learning was limited by the cost of compute and data acquisition/preparation. In fact, compute and data consumed the entirety of any budget for analytics which left zero investment for the real value driver: algorithms to drive actionable insights. In the last couple years, with cost of compute and data plummeting, machine learning is now available to anyone, for rapid application and exploitation.


It is well known that businesses must constantly adapt to changing conditions: competitors introduce new offerings, consumer habits evolve, and the economic and political environment change, etc. This is not new, but the velocity at which business conditions change is accelerating. This constantly accelerating pace of change places a new burden on technology solutions developed for a business.

Over the years, application developers moved from V shaped projects, with multi-year turnaround, to agile development methodologies ( turnaround in months, weeks, and often days). This has enabled businesses to adapt their application and services much more rapidly. For example:

a) A sales forecasting system for a retailer: The forecast must take into account today's market trends, not just those from last month. And, for real-time personalization, it must account for what happened as recently as 1 hour ago.

b) A product recommendation system for a stock broker: they must leverage current interests, trends, and movements, not just last months.

c) A personalized healthcare system: Offerings must be tailored to an individual and their unique circumstance. Healthcare devices, connected via The Internet of Things (IoT), can be used to collect data on human and machine behavior and interaction.

These scenarios, and others like them, create a unique opportunity for machine learning. Indeed, machine learning was designed to address the fluid nature of these problems.

Firstly, it moves application development from programming to training: instead of writing new code, the application developer trains the same application with new data. This is a fundamental shift in application development, because new, updated applications can be obtained automatically on a weekly, if not daily basis. This shift is at the core of the cognitive era in IT.

Secondly, machine learning enables the automated production of actionable insights where the data is (i.e. where business value is greatest). It is possible to build machine learning systems that learn from each user interaction, or from new data collected by an IoT device. These systems then produce output that takes into account the latest available data. This would not be possible with traditional IT development, even if agile methodologies were used.


While most companies get to the point of understanding machine learning, too few are turning this into action. They are either slowed down by concerns over their data assets or they attempt it one-time and then curtail efforts, claiming that the results were not interesting. These are common concerns and considerations, but they should be recognized as items that are easily surmounted, with the right approach.

First, let’s take data. A common trap is to believe that data is all that is needed for successful machine learning project. Data is essential, but machine learning requires more than data. Machine learning projects that start with a large amount of data, but lack a clear business goal or outcome, are likely to fail. Projects that start with little or no data, yet have a clear and measurable business goal are more likely to succeed. The business goal should dictate the collection of relevant data and also guide the development of machine learning models. This approach provides a mechanism for assessing the effectiveness of machine learning models.

The second trap in machine learning projects is to view it as a one-time event. Machine learning, by definition, is a continuous process and projects must be operated with that consideration.

Machine learning projects are often run as follows:

1) They start with data and a new business goal.

2) Data is prepared, because it wasn’t collected with the new business goal in mind.

3) Once prepared, machine learning algorithms are run on the data in order to produce a model.

4) The model is then evaluated on new, unforeseen, data to see whether it captured something sensible from the data. If it does, then it is deployed in a production environment where it is used to make predictions on new data.

While this typical approach is valuable, it is limited by the fact that the models learn only once. While you may have developed a great model, changing business conditions may make it irrelevant. For instance, assume machine learning is used to detect anomaly in credit card transactions. The model is created using years of past transactions and anomalies are fraudulent transactions. With a good data science team and the right algorithms, it is possible to obtain a fairly accurate model. This model can then be deployed in a payment system where it flags anomalies when it detects them. Transactions with anomalies are then rejected. This is effective in the short term, but clever criminals will soon recognize that their scam is detected. They will adapt, and they will find new ways to use stolen credit card information. The model will not detect these new ways because they were not present in the data that was used to produce it. As a result, the model effectiveness will drop.

The cure to avoid this performance degradation is to monitor the effectiveness of model predictions by comparing them with actuals. For instance, after some delay, a bank will know which transactions were fraudulent or not. Then it is possible to compare the actual fraudulent transactions with the anomalies detected by the machine learning model. From this comparison one can compute the accuracy of the predictions. One can then monitor this accuracy over time and watch for drops. When a drop happens, then it is time to refresh the machine learning model with more up to date data. This is what we call a feedback loop. See here:

With a feedback loop, the system learns continuously by monitoring the effectiveness of predictions and retraining when needed. Monitoring and using the resulting feedback are at the core of machine learning. This is no different than how humans perform a new task. We learn from our mistakes, adjust, and act. Machine learning is no different.


Companies that are convinced that machine learning should be a core component of their analytics journey need a tested and repeatable model: a methodology. Our experience working with countless clients has led us to devise a methodology that we call DataFirst. It is a step-by-step approach for machine learning success.

Phase 1: The Data Assessment
The objective is to understand your data assets and verify that all the data needed to meet the business goal for machine learning is available. If not, you can take action at that point, to bring in new sources of data (internal or external), to align with the stated goal.

Phase 2: The Workshop
The purpose of a workshop goal is to ensure alignment on the definition and scope of the machine learning project. We usually cover these topics:
- Level set on what machine learning can do and cannot do
- Agree on which data to use.
- Agree on the metric to be used results evaluation
- Explore how the machine learning workflow, especially deployment and feedback loop, would integrate with other IT systems and applications.

Phase 3: The Prototype
The prototype aims at showing machine learning value with actual data. It will also be used to assess performance and resources needed to run and operate a production ready machine learning system. When completed, the prototype is often key to secure a decision to develop a production ready system.


Leaders in the Data era will leverage their assets to develop superior machine learning and insight, driven from a dynamic corpus of data. A differentiated approach requires a methodical process and a focus on differentiation with a feedback loop. In the modern business environment, data is no longer an aspect of competitive advantage; it is the basis of competitive advantage.

Monday, June 20, 2016

iPad Pro: Going All-in

Here is my tweet from a few weeks back:

I have given it a go, going all-in with the iPad Pro. In short, I believe I have discovered the future of personal computing. That being said, in order to do this, you truly have to change the way you work; how you spend your time, how you communicate, etc. But, it's worth it and will probably make you a better professional. I knew I was hooked, when I had to go back to my MacBook for something and I started touching the screen; the touch interface had been ingrained in my work.

Here are my quick observations:

1) The speed of the iPad Pro is unbelievable. While I didn't realize this in advance, this fact alone makes up for a lot of the reasons why I could never move to an iPad before.

2) You have to master multitasking in the iPad Pro in order to make the switch. There are a lot of shortcuts on the screen, keyboard shortcuts, and hand gestures. If you are not using them, you will not understand the advantage of this form factor.

3) Keyboard shortcuts are now available for my corporate mail. That's a big time saver.

4) I never have to worry about a power cable. The battery on this is great, but even if it gets low, nearly everyone I know has a compatible charger.

5) The integration of apps on the Pro is tremendous: Box/Office, Slack, etc.

6) It goes without saying that the Pro is super light and convenient for travel.

7) Here are some things I can't do on the iPad Pro:
- Renew Global Entry
- Corporate workflow (forms and expenses)
- Blogging (writing is easy, but posting to corporate blog or even Blogger is very hard). I'm not sure why there is not a good app for this.

8) I got the smaller version of the iPad Pro. I thought the large one was just too big. It seems like the ideal size may be a size in between the two.

In short, after a few weeks, I highly recommend. You can make the switch, but you'll likely need a laptop once a week or so, for some of the items mentioned above. I haven't really gotten into the Apple Pencil yet. I've used it a couple times and may try it more over the next couple weeks.