Disproportionate Outcomes

“Give me a place to stand, and I shall move the Earth with it” — Archimedes

I spent some time with a good friend a while back. We started taliking about some philanthropy work that he and his wife are doing for children with special needs. This resonated with me, as my wife spent a number of years of her life working at a school for children with special needs. I mentioned to my friend that we were always amazed at the progress that the children could make with the right tools and assistance. At the time, we made it a habit of giving money to the school to purchase more tools (computer equipment and other learning aids). This definitely had an impact on that school and those children. 

My friend went on to say that he was giving to an educational institution, who was training students to teach children with special needs. He added, “I always try to give where there is the most leverage”. While ‘leverage’ is perhaps an overused word, his comment was the perfect example of the true application of leverage. If you train young adults to teach children with special needs, then you can probably touch hundreds or thousands of children over a reasonable period of time. By contrast, if you give tools to one school (like I have done in the past), you only impact a much smaller population of children (i.e. those that go to that school). 

Now, giving is giving and helping is helping…and there is probably no wrong way to do it. But, for me, this was a powerful lesson in leverage. 

Read the rest on Medium. 

Do Data Science Faster

AI is the goal for many enterprises. But, an organization needs machine learning, in order to do AI. And, machine learning is not possible without analytics. And analytics is not possible without simple, elegant data infrastructure. Simply put, there is no AI without IA (information architecture). Success in both areas is more often about culture, than technology.

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Culture is either one of a company’s most powerful assets, or it’s an obstruction. Most enterprises do not have a data culture. Many do not even know they need it. Ironically, the current culture of an organization often prevents an enterprise from knowing that they need a new one (i.e. can’t see forest due to trees).

I believe that the biggest obstacle to a data culture is the fear of complexity. Ben Thompson once wrote, “Culture is not something that begets success, rather, it is a product of it.” If an enterprise has not had visible/material success with data, how could anyone possibly expect it to form a data culture?

Our mission is to make data simple and accessible to the world. We are enabling companies to sow the seeds of a data culture, with a practical approach to achieve a successful outcome. Said another way, we are enabling organizations to do data science faster. 

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I once heard that the difference between a data science project and a software engineering project is that with the former, you have no idea if it will actually work. Even if you are a staunch ‘fail fast’ supporter, that is too much of an unknown for many. Most organizations that make an investment want some understanding of how they will generate a return on that investment. I understand that is not the Silicon Valley mantra, but most enterprises are held to a different standard of ROI than Silicon Valley. It’s not right or wrong, it’s just different. High certainty and modest returns is preferred by many enterprises over a more aggressive approach.  In economics, we call this tolerance for risk adjusted returns. 

My observation is that most of the time invested in building and deploying machine learning is not spent on algorithms and models. Instead, it is spent on the most mundane of tasks: data preparation, data movement, feature extraction, etc. These are a necessary evil and the place where most risk in a data science project resides; garbage-in/garbage-out leads to low certainty. 

With the newly announced IBM Integrated Analytics System, we aspire to solve 2 problems that I see in every organization:

1) A desire to apply data science and machine learning at scale. Now, and with certainty.

2) The intent to move to cloud, to accelerate digital transformation. 

We started with the assumption of large data sets. Whether on public cloud, private cloud, Hadoop, data warehouse, or otherwise, the ideal solution enables federation across all data types and locations. With our common SQL engine, this is easy. 

Once you can easily access all data, the next challenge is to apply data science and machine learning: building, training, and deploying models. And then, via feedback loops, leveraging those models to make predictions and automate previously manual tasks. Fundamentally, those are the 2 reasons to focus on data science: predictions and automation. 

Lastly, moving to the cloud is now as simple as a click of a button. With a common codebase across private and public cloud, it is easy to move data and run applications wherever is preferred. With Data Science Experience, you build models where you want (private or public cloud) and deploy to either environment. Your data is limited by your imagination, not your firewall. 

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AI is fundamentally about using machine learning and deep learning techniques to enable applications that are built on data. Every organization that aspires to a data culture has to pick a place to start. Deep learning will make data accessible that previously was not; if that will create momentum, with a high chance of success, that is where you should start. For other organizations, better predictions and automation will beget a data culture. Regardless of which path you choose, the objective is the same: do data science faster.

Jason

 "If put to the pinch, an ounce of loyalty is worth a pound of cleverness." - Elbert Hubbard

The great paradox of life is that when you are young, you want to be old. And as you get older, you want to be young again. For those that long to be older, there is one thing that no one tells you: With age comes loss.

I've experienced this the only way you can (the hard way) over the last few years. Most of my grandparents have passed away, I lost the first colleague that I had really grown up in the business world with, and a number of others. Last week, we lost another friend and colleague. There is no positive in a loss, other than the opportunity to remember how they made you aspire to be a better person. In fact, the only way to live up to their legacy is to conduct yourself the way you know they would have.

I met Jason Silvia in 2007. He could be intimidating in a first meeting, based on his accomplishments and stature. But, I quickly realized that the compassion went deep. He was the best of 'Boston', if you know what I mean.

Loyalty is fleeting in many circles today. Relationships are transactional and people come and go. That was not the case with Jason. His hallmark was the ability to build tight-knit teams, ever aligned to the task at hand. Their loyalty to him was immense, yet was outmatched by his loyalty to them. I once read a quote, "Loyalty means I am down with you whether you are wrong or right, but I will tell you when you are wrong and help you get it right." That was Jason, captured in a single sentence. 

I always wondered why he worked for so long. I even asked him that a few times. I guess the answer was staring at me the whole time.

The Data Science Renaissance

“If people knew how hard I worked to get my mastery, it wouldn’t seem so wonderful at all.” -Michaelangelo

Renaissance means rebirth. A variety of factors, coming together at the same time, can spark a rebirth. In the analytics world, we are facing a confluence of factors: economic disruption, a great re-skilling, and unprecedented access to data. The combination of these factors is sparking the rebirth of data science, with the expert-led model a relic of the past. History is a great teacher, and demonstrates that this Renaissance is not all that dissimilar from the original.

Read the rest on

Medium

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Suggesting a 'pilot' is a weakness

This was my observation on Twitter/LinkedIn a couple weeks ago:

Nearly everything I share in such forums is based on actual events/observations, not theory. I didn’t expect any reaction. 82 comments later, I clearly struck a nerve. The interesting thing is the dichotomy of reaction. A portion of people think I don’t understand design thinking, MVP’s, and experimentation. Another portion vehemently agree with my statement. Given the passionate debate, I felt it appropriate to clarify my thinking. The background for this starts with a couple core beliefs I hold.

Belief #1: The job of a business executive is to maximize return on invested capital.

Many companies are myopically focused on growth. Growth is wonderful, but in isolation it ignores the fact that the true value of a business is determined by the discounted value of its future cash flows. The future cash flows ultimately determine what can be distributed to shareholders, and they can be maximized by growth, but also by optimizing profit margins and capital efficiency.

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