Why Data Science Can't Find the Needle in the Haystack

A colleague of mine wants a predictive model. He is trying to determine which people on a health insurance plan will visit the hospital in the next couple of months. He has pretty good data for making this type of prediction. He knows who visited the hospital in the past; and he knows they are more likely to revisit. He knows what illnesses these people have, and which illnesses likely result in hospital visits. He knows what drugs have been prescribed, and whether patients are taking their drugs.

Even with this data and even with very good models, he still complains that the predictions are not good enough. His problem is too many false positives. And he simply doesn’t have enough employees to review every patient the model predicts.

Data science is a great tool, but it is not perfect. And if you are going to weld the data science tool, you should be aware its’ shortcomings. Data science is simply not very good at finding a needle in a haystack.

This concept can be illustrated with an example. Say a banker is managing the mortgages of 500,000 homeowners. He knows from experience that roughly 1,000 of these homeowners will default on their loan. There is plenty of data to help zero in on these 1,000 people: zip code, income, payment history, and credit rating. He knows that if I can put the right people some assistance, they may not default on their loan.

We have the data of build a predictive model. But regardless of how good the model is, it will not be perfect. When the model is run, each homeowner will be classified as at-risk for default or not at-risk. In this scenario, there are four possible outcomes for each homeowner. The homeowner is at-risk and is properly identified by the model; the homeowner is not at-risk is properly identified by the model. These are the two accurate predictions.

Every predictor gets some wrong too. When the homeowner is not at-risk, but the model says he is, that is a false positive. If the homeowner is at-risk and was not identified by the model, that is a false negative. In statistics, false positives are also referred to as “type I errors”. False negatives are referred to as “type II errors”.

Now let’s say that we construct a model that is 80% accurate, which is a rule-of-thumb threshold for a good prediction. With 80% accuracy on 500,000 loans, 400,00 will be correctly predicted and 100,000 will not. At an 80% prediction rate, 800 of the 1,000 homeowners would be predicted correctly.

Of course, an 80% success rate means there is also a 20% failure rate. I mentioned that 100,000 are not predicted correctly. There are 200 false negatives that are the balance of the 1,000 target loans. Subtracting the 200 false negatives from the 100,000-people identified incorrectly leaves 99,800 false positives. That is the extraordinary 500 times as many false positives as correctly predicted at-risk loans.

Even if the model can predict at the incredible rate of 99% the numbers of false-positives will outnumber the correctly identified at-risk cases by nearly five to one.

The problem here isn’t with data science or prediction methods. It simple math. When trying to use statistics to find a very small number among a very large number the false-positives will always greatly outnumber the actual positive prediction. This type of problem is truly a needle in a haystack.

Data Science isn't Rocket Science

Science is intimidating. It conjures up images of lab coats, telescopes and microscopes, or petri dishes and chemicals. But Data Science isn’t rocket science. Science is about discovery and Data Science is discovery in data.

You don’t need an army of PhDs for successful data science. Instead you need a basic understanding of statistics and a little computing power. Then follow this recipe of five steps to put Data Science to work for your business.

Step 1. Decide what to predict

First and foremost, you must know what you want to predict. Data Science in business is about making predictions. That predicting might be finding customers that will buy a product; or which patients will be readmitted to a hospital; or when to buy shares of stock.

Everyone knows the story of Target sending coupons for pre-natal items to a teenage girl, only to surprise her father. But that case didn’t happen by chance. Instead, someone at Target decided to specifically focus on pregnant women. That person decided to predict which of their customers were pregnant.

Make your prediction on a single thing. That thing will be represented by a prediction variable. The variable might simply be “Yes” or “No”, such as in the Target pregnancy example. It could be an item in a list, such as a day of the week. It frequently will be a number, such as the price of a barrel of crude oil.

Step 2. Make your hypothesis

Your hypothesis is the key to the puzzle. And yes, the word hypothesis comes right out of the scientific method because that’s what we’re doing; we’re applying the scientific method to data to make predictions. You decided in the previous step what to predict, now it’s time to guess how to make that prediction.

Some insight into your problem is helpful here. For example, in the case of Target above, someone had made the presumption that items a person purchased could indicate whether they are pregnant. They very likely narrowed the items down to a specific list of items or types of items. The hypothesis may have been as simple as “someone who buys pre-natal vitamins is likely to be pregnant”. Or even more specific, “a woman between 18 and 45 who buys pre-natal vitamins is likely to be pregnant”.

Step 3. Get the data

Of course, this whole exercise assumes the data is available to make these predictions. You will need to collect data points on any attribute you are testing, as well as the values you wish to predict.

Again, going back to our example to finding customers who are expecting. Assuming we want to build a model on the hypothesis that “a woman between 18 and 45 who buys pre-natal vitamins is likely to be pregnant”. We will need the following data:

• ·        A list of customers who bought products.
• ·         A list of the products they bought.
• ·         For each customer, we need their age and gender.
• ·         For the products, we need to know which are pre-natal vitamins.
• ·         And most importantly, we need to know who is pregnant.

In this age of big data, you may have all the data available. That is, every customer, and every product they purchased. If you have all the data, great, you can build your model based on the population, which is the statistician’s way of saying “all the data”. Otherwise we will use a sample, which is a way of saying some of the data.

Some data points may be difficult to obtain. In our example, the fact that a customer is expecting a child may not be readily available. In this case, special steps will be required to obtain the data. Target may have performed a customer survey or used some other means of gathering the information directly from the individual.

Step 4. Build a model

Building a model is where the fun starts. This is the statistical model, or predictive model. A basic understanding of statistics and knowledge of modeling software is necessary.

Before building a model, you should run some analysis to see if your hypothesis is worth pursuing. The typical first step is testing a null hypothesis for statistical significance. The null hypothesis checks that the predictor variable affects the prediction. In our case, the null hypothesis would be “knowing a person is a woman, between 18 and 45, who bought pre-natal vitamins has no impact on their being pregnant.”

In short, we compare the number of pregnancies in a random sample of customers against 18 to 45-year-old women buying pre-natal vitamins. If the difference in pregnancy rates between these two groups is greater than 5%, the null hypothesis is disproved, and our assumption is considered statistically significant.

Some caution is needed here, because a 5% difference could be attributed to improbable random samples. You can protect yourself against improbable results by repeating the test against additional random samples.

Once you have your data. And your hypothesis is sound. Building a predictive model is relatively simple. For example, the R code for creating a model looks like this:

modelFit = train(class ~ .,method="rf",
data=trainingCV, prox=TRUE)

And the code for making predictions looks like this:

prediction = predict(modelFit, testCV)

This code is illustrative to show that modelling does not require complicated commands.

Step 5. Check your results

Before creating your model, divide your data into two sets; a training set and a test set. The training data is used to create the model. The test set is used to demonstrate that it works. Typically, you would split the original data such that 80% of it is used for training with the remaining 20% used for testing.

In our sample, let’s say we have 1,000 customers in our data. We would randomly select 800 for training and 200 for testing. But there is nothing sacred about an 80/20 split. In fact, if your dataset is very large, say 100,000 or more, you could create multiple test sets using a 60/20/20 split.

When your data is prepared, create your model; if using R then run the train() function. Then take the created model and run it against the test data; if using R then run the predict() function. The predict() function will make prediction of whether the customer is pregnant or not.

The results of the prediction are compared against the original data to determine if our model makes reliable predictions. Using our sample, we would make predictions against 200 people. If the model correctly determines pregnancy in 150 of them, then our prediction was 75% accurate.

Your software should be able to provide you with an Area Under the Curve (AUC) analysis. AUC values close to 1 indicate a very good model, those near .5 are little better than flipping a coin. You should strive for AUCs of .80 or more.

And now we’re done

Well, maybe we’re not done. If you’re AUC is poor, then you should start the process over. But it’s not a complete loss, because even a bad model gives knowledge; knowing what doesn’t work is important too.

Of course this is a blog post. And I have over-simplified every step. Still, creating predictive model is not as intimidating as one might think. It’s most definitely not like putting a man on the moon.

Beware of Expert Blindness

More and more companies are looking to big data to help them market their products or improve their services. Of course that means more companies are seeking out data scientists and statisticians. But to truly take advantage of big data means the firm must commit to the principles of data science. That is often easier said than done.

Enter the Subject Matter Expert and the common trap of “Subject Expert Blindness”.

Yes. The Subject Matter Expert; the person who has spent a career building knowledge of their business. These are the people who drive a company’s offering; or whose stamp of approval is necessary on any significant project. They believe their experience and learning has given them special insight that others simply do not have.

If you are a specialist in data science, then it is unlikely that you have spent years earning experience in any particular industry. Automotive. Healthcare. Insurance. Finance. It doesn’t matter because your expertise is data. Data is data. And you tell your story with the data.

The expert does not rely on data. Or only needs it to confirm their preconceived insight. The expert, then, becomes blind to alternatives hidden in the data.

Take the case of a recent project of mine. I was approached by a firm looking to find groups of people who were likely to be the most expensive customers to service. The expert provided a list of twenty such groups and asked that we demonstrate that these are statistically more likely to consume services than an ”average” customer.

But the notion of pre-determined groups is silly in the world of data science. Why not run have the data tell us what the highest risk groups are? If the results match the expert’s groups then great, her hunches are confirmed. But the expert will never find the hidden gems that the data often exposes. The expert is simply blind to the alternatives.

The bottom-line: let the data tell the story. Don’t force the story onto the data. Resist the temptation to rely on personal experience to shape the story before the data is even crunched.

Ask them “What do you want to learn?”

You hear the question all the time, "What do you want to be?" I'm even guilty of asking it myself. This is how we start advising our youth when they are considering colleges.

It's the wrong question. And it's part of a pervasive thinking causes kids to spin through multiple majors and spend more time in school than is necessary. We are programmed to think of university study as job training. It's not. And if you think I'm wrong, ask the most successful people you know if they are working in their field of study (very possibly not). Then ask them if college was a waste of time (most definitely not).

Instead, college is where we go to broaden our knowledge. It's where we sharpen communication skills. It's where we learn how to work independently; it's where we learn to work with others (and no, those aren't mutually exclusive). It's an opportunity to explore topics in depth because it interests us, rather than because we have to. And more importantly, it shows future employers that we can set a long range goal, work hard, and finish it successfully.

The right question then, is "what do you want to learn?" If the person already knows what she wants to be, then she probably already knows what she wants to learn. More importantly, though, if the thought of learning a subject is distasteful, then that career choice is not wise.

Now here's the tricky part for us adults giving guidance: how do we respond when the young man answers our question with "Literature" or "Philosophy"? Typically, the thought is "what kind of job can you get with that?" That is wrong thinking.

Literature? What business or agency couldn't benefit from a person who has deep knowledge of communication?
Philosophy? What business or agency couldn't benefit from a person who has deep understanding of how people are motivated?

Most importantly, though, when a student considers what they want to learn; And when they spend time exploring that subject; they will find ways to apply that knowledge to other areas. Their new knowledge will guide them into an appropriate career. So as crazy as it may seem, the study of the oceans can help a person with a later career in sales. Or the study of music can help enrich one's later family life.

So next time you are congratulating a high school senior on their graduation, ask the right question. Ask them, "so now what do you want to learn."

It’s education, not job training.

Several years ago I found myself behind a podium in a packed gymnasium. Hundreds of people stared up at me and yet the cavernous room was almost silent. Half the crowd were high school graduates; the others their proud parents and families. I was giving the commencement address. Next to me, on the floor, I had a bag of props; a vinyl record album, Michael Jackson. A compact disk, Pearl Jam. A cell phone.

The topic of my speech was how fast the world changes. And as examples I displayed my props and explained how that, in only the time the students had been going to school, changing technology dramatically affected our lives. The tie-in to the day, the commencement, was that their education needed make them ready for this changing technology, rather than the common notion of learning the current technology itself. Because, as they could already see, their world was evolving fast.

It's one of the most common mistakes made by people in general; the mistaken belief that the purpose of schooling is to provide job training. It's why graduates are rarely asked what they want to study, instead they are asked what they want to be (or do). It's why philosophy majors are asked "what are you going to do with that?"

While this may seem very subtle, instead it's a huge leap to understand the greater purpose of education is to prepare us to have careers; not train us in a career. Education, especially college degrees, demonstrate the abilities to apply oneself toward a goal that takes years to achieve; often without direct guidance or intervention from parents. Imagine you are hiring for an entry level marketing position in an established corporation. All things being equal, would you pick an English student who finished in four years, or a business student who took six years and changed majors? Obviously it's not the material studied, but the work habits formed that is the important learning.

The thing is, careers (and life in general) require a strong foundation of skills that everyone needs to learn. Basic math. Proper writing. Fundamentals of science. Even history and art. You can even make an argument that it's important to understand demand curves and probabilities and logic. Providing these kinds skills should be the goal of every educational system. Then freed of the stress of "what job will I find", students can apply themselves in areas of interest. Then those that have an interest in health can learn to be doctors; and those that like to build things can learn to be engineers.

So this summer, when you are making small talk with recent graduates, don't ask them what they intend to do; instead ask them what they want to study. Then remind them that the real learning is finding their way to the finish.

What I am reading | "Savages"

Savages: A Novel
Don Winslow

Nothing makes a long flight more tolerable than a good read. So long along those lines, I found myself in the Phoenix airport looking for a novel to entertain me on my trip back to New Jersey. I settled on Savages because it looked like an engaging quick read.

Anyway, the book is certainly a quick read. Honestly, though, this is probably the dumbest story that I've read since, well, maybe since ever. The characters are all stereotypes. The plot is cliche'. The prose is minimalist. It felt like Winslow tried to channel Cormac McCarthy, but the writing doesn't engage in the same way. I felt I was reading a story written by a sixth grader. Speaking of which, Winslow's poetic license of English grammar is worse than a sixth grader; the writing really has no form.

After reading an action passage, my son summed it up nicely, "wow, he made that explosion sound boring." Oops.

What I am reading | "Ultra-Marathon Man"

Ultramarathon Man: Confessions of an All-Night Runner
Dean Karnazes



I finished this book recently. It's a pretty entertaining read. While Dean is obviously not a writer by trade, his stories draw you in. While I have no desire to run ultra-marathons, the stories inspire me to continue my running. I found the marathon to the South Pole to be particularly interesting. In any case, this is an easy read; I recommend it for runners at all levels.

What I am reading | "Don't Make Me Think"

Don't Make Me Think! A Common Sense Approach to Web Usability
Steve Krug

I'm slogging my way through this guide to designing easy to use web sites. I had bought it with the idea that it would provide insight into good (web) application design. However, after reading a couple of chapters, it is apparent that web sites, those that provide content, and web applications, those that do something, are different animals.

The book is fine, and is a recommended read for anyone new to web design. Unfortunately for me, Mr. Krug hasn't provided any new ideas that I haven't seen, tried, or evangelized at some point. It's easy to read with good examples; follow the guidelines in the book and your web site won't suck.

Time to Kill the Relational Model

It’s time to kill the relational database model. Well not the relational model per se, but rather the practice of building systems by developing relational schemas first.

This is quite the revelation for me. When first I discovered relational database design over three decades ago I thought it was religion. The simplicity of tables and relationships represented freedom from the rigid hierarchical structures of the day. Since then, relational design and its’ normal form remained a constant foundation for the systems I constructed.

But during a recent project I tired of the burden that relational design placed on our development effort. In particular, a tight adherence to rules of normalization and relational philosophy stifled our opportunity for nimble software engineering. What on paper looked like a method of enforcing integrity became complex morass of surrogate keys and multiple joins.

Fundamentally my complaint is over the complexity that normalization brings; an odd turn considering that relational databases were popularized on simplicity of tables. Frankly, though, there is nothing intuitive about joining tables; and the more joins the more complexity.

While relational databases are not (and should not) go away, there are flaws with some relational theory that makes writing software difficult. For example…

• Normalization produces a lot of tables. A lot of tables translate into a lot of joins. A lot of joins is a red-flag for complexity. When queries frequently have three or more joins, the schema is probably overly complex.
• Relational theorists discourage use of null foreign keys. The only way to accomplish this goal is to introduce a link table for the one to many relationships in the model. This introduces two problems, first, the extra table adds an additional (and unnecessary join). And second, it is harder to determine if a row in one table is related to a row in the other.
• Using data to determine state (or status) introduces complexity. While not necessarily a tenant of relational models, some designers prefer to compute the state of an item on-the-fly based on data in the tables. This is easy enough when all the data needed to determine the state are stored on the same table row. Unfortunately it is frequently necessary to scan additional tables on multiple rows to calculate the status.

A better method of system design starts with a design of objects. The database schema, then, is modeled to keep these objects intact. In this way, the database becomes nothing more than the repository for data at rest. The validity of the data is managed by methods within the objects and not by referential integrity or database triggers. Think of this as object first, data last design; there OFDL, I just coined a new acronym.

The Key To A Great Meeting Is Kicking Some People Out Of It | Fast Company

The small-group principle is deeply woven into the religion of simplicity. It’s key to Apple’s ongoing success and key to any organization that wants to nurture quality thinking. The idea is pretty basic: Everyone in the room should be there for a reason. There’s no such thing as a “mercy invitation.” Either you’re critical to the meeting or you’re not. It’s nothing personal, just business.

Steve Jobs actively resisted any behavior he believed representative of the way big companies think--even though Apple had been a big company for many years. He knew that small groups composed of the smartest and most creative people had propelled Apple to its amazing success, and he had no intention of ever changing that. When he called a meeting or reported to a meeting, his expectation was that everyone in the room would be an essential participant. Spectators were not welcome.

If big companies really feel compelled to put something on their walls, a better sign might read:

How to Have a Great Meeting

1. Throw out the least necessary person at the table.
2. Walk out of this meeting if it lasts more than 30 minutes.
3. Do something productive today to make up for the time you spent here.

Keep reading:
The Key To A Great Meeting Is Kicking Some People Out Of It | Fast Company:

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Lesson of the day: the planets are really far apart.

Bill Nye Demonstrates Distance Between Planets - YouTube:

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6 Time-Management Tips From Accelerator Programs | Fast Company

6 Time-Management Tips From Accelerator Programs | Fast Company: "


Sage advice from Alina Dizik...

1. Avoid the email time suck.
2. Choose your most important goal each week
3. Know your productivity limits.
4. Be like Dorsey: Take breaks to prevent burnout.
5. Skip some meetings.
6. Say "no" when you need to



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Some music ages so well...

I'm up at 5:30am every morning for a 5K run. This morning my legs were stiff and my thighs burning from extra mileage I put in over the weekend. As I rounded the last bend and mustered strength for one more hill, The Who's Tommy pounded through my iPod;

Listening to you,

I get the music.

Gazing at you,

I get the heat.

Following you,

I climb the mountains.

I get excitement at your feet.

Right behind you,

I see the millions.

On you,

I see the glory.

From you,

I get opinions.

From you,

I get the story. 

The music made me come alive and I charged up the hill and sprinted to the finish as if I was on fresh legs. I was only nine years old when The Who recorded Tommy. I knew virtually nothing about it until a decade later in college. I liked it, but not enough to buy a copy. Now thirty years later, and more than forty since the album's release, I catching up with it again. Some music ages really well.

Microsoft Office 365 Cloud-Based Productivity Service Now Helps Customers Comply with HIPAA Privacy and Security Standards - Microsoft in Health - Site Home - MSDN Blogs

Microsoft Office 365 Cloud-Based Productivity Service Now Helps Customers Comply with HIPAA Privacy and Security Standards - Microsoft in Health - Site Home - MSDN Blogs:

With reimbursements falling and medical loss ratio minimums rising, hospitals, physicians, and health plans are under unprecedented pressure to drive down operating costs while still improving the quality and safety of patient care. The economic advantages of cloud-based productivity solutions to drive down operational costs and complexity are well understood, but for most health organizations, HIPAA security and privacy concerns have been a showstopping barrier to realizing the full anywhere, anytime productivity potential of cloud-based technologies.


That is, until now. Today ... Microsoft is helping remove that barrier by embedding privacy and security capabilities in Office 365, our next-generation cloud productivity service. This means that Office 365 is now a cloud-based platform that complies with leading information privacy and security standards for customers operating in the United States and European Union. As part of its contractual commitment to customers, Microsoft will now sign business associate agreements under the U.S.-mandated Health Insurance Portability and Accountability Act (HIPAA).


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Great anecdote from "Users, not Customers"

I just started the book "Users, not Customers: Who Really Determines the Success of Your Business." It starts with a great short anecdote about comparison shopping in our brave new world. If the rest of the book stays as intriguing, this will be a great read.

"My wife loves seltzer water. I can’t stand it, but she will hardly drink water if bubbles aren’t in it. So I thought it’d be great to buy her a soda maker. One afternoon, I passed by a Williams-Sonoma store and decided to stop in. Lo and behold, they had one sitting on the shelf: a SodaStream Genesis drinks maker for $150. But it seemed expensive. I could buy her a pantry full of 150 bottles of premade seltzer for that price. So I decided to shop around. 

I opened the RedLaser app on my iPhone and used it to scan the machine’s bar code to find out what other retailers charged. Bed Bath & Beyond carried the same thing for a hundred dollars. Success! Fifty dollars in savings. I waved down a sales clerk and showed her my findings. But she declined to match the price. 

So right there, in the middle of a beautiful Williams-Sonoma store in a high-rent location on the Upper East Side of Manhattan, I bought the SodaStream Genesis drinks maker—from Bed Bath & Beyond by using my mobile browser."

Why Digital Talent Doesn’t Want To Work At Your Company | Fast Company

I like this blog post so much that I am re-posting it in it's entirety.


Why Digital Talent Doesn’t Want To Work At Your Company | Fast Company:

Why doesn't digital talent want to work at your company? It’s not because you’re a consumer packaged goods company, rather than Google. It’s not because you’re in Ohio instead of Silicon Valley. It’s not because your salaries are too low, or because you don’t offer free food and laundry services.

It’s because you’re not providing them the right opportunity. The talent you want would be happy to work in an un-air-conditioned garage in New Mexico if it meant the chance to change the world.

This, the opportunity to do great things, to make a real difference, is what drives most digital talent--whether they’re developers, designers, producers, marketers or business folks. 

Most companies don’t offer this, so they skip your company and work somewhere that’s more innovative and exciting. End of story. But the good news is that you can offer them something exciting and great. The promise of changing a giant, behind-the-times organization into an Internet-savvy business is an incredibly exciting challenge and a big way for ambitious people to make an impact.  

But it takes more than lip service to make the sale. Job candidates and new hires with digital chops must truly believe in the company’s dedication to digital transformation and they must see that they are empowered to make this change. Trouble is, many big businesses aren’t structured to deliver on this type of opportunity. The attributes of a soul-crushing, Sisyphean, anti-digital workplace run deep.  

Digital talent won’t want to work at your company if:  

• Every element of their work will be pored over by multiple layers of bureaucracy. Even if that’s how the rest of the company operates, it can’t spill into the digital department. In a technology environment, new products and businesses spring up daily and a new endeavor can go from conception to launch in a matter of months. Reining in the momentum will be read as inaction and a clear signal the company isn’t willing to grasp the new way of the world.  
• Mediocre is good enough. While clocking out at 5 p.m. is attractive to some, it will discourage digital talent. They want to be expected to do something great. They want to be pushed. They care about their work. Their leadership, and those they rely on to get things done, must match their appetite for success.   
• Trial and error is condemned. The freedom to try out new ideas allows employees to take initiative, make decisions, and learn from their mistakes. It also demonstrates an attractive and inspiring entrepreneurial spirit.
• Your company is structured so it takes a lifetime to get to the top, and as such there are no digital experts in company-wide leadership positions.Digital talent--often in their 20s and 30s--need to see a clear path for uninhibited career development that’s based on merit, not years spent, and that’s beyond the confines of the digital department. If they don’t, they won’t see a reason to stay with the company in the long term.  
• Your offices are cold, impersonal and downright stodgy. It may sound like it conflicts with the “you don’t need to be in Silicon Valley point,” but appreciate the nuance. A traditional office layout is designed to communicate power among certain individuals and barriers between departments. This does not support the collaborative ethos which is intrinsic to the web. Companies should do everything possible to provide the digital team friendlier, open office space. A location in a hip, young neighborhood (which surely exists in every mid- to large-sized city) is also a big plus.  

When all of these digital-talent deterring points are addressed, company leadership has effectively and proactively demonstrated the company’s dedication to a digital transformation. It is at this time that their words, a broadly communicated firm stance on the significance of the company’s digital goals, will make the most impact. Without this conspicuous top-down support, politics in the organization or simply one influential disbeliever can hinder the effort, limit the extent of digital integration possible, and discourage valuable employees.  

You need them more than they need you. Demand for their services is so high, they can afford to be finicky. If they don’t like where they’re working, another firm with a more attractive culture and more grand opportunity will quickly swipe them up. That could be your company. But it could just as easily be someone else.

Adapted from Users Not Customers: Who Really Determines the Success of Your Business(Portfolio), by Aaron Shapiro, CEO of HUGE, a digital agency that helps companies including PespiCo, Comcast, Target, HBO, and Unilever reimagine how they interact with their customers and manage their business in the online economy. Visit aaronshapiro.com.

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Opinion - Image - NYTimes.com

Opinion - Image - NYTimes.com:

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Steve Jobs Resigns As CEO Of Apple | TechCrunch

Steve Jobs Resigns As CEO Of Apple | TechCrunch: "Title says it all. More to come. For now, the letter:

To the Apple Board of Directors and the Apple Community:

I have always said if there ever came a day when I could no longer meet my duties and expectations as Apple’s CEO, I would be the first to let you know. Unfortunately, that day has come.

I hereby resign as CEO of Apple. I would like to serve, if the Board sees fit, as Chairman of the Board, director and Apple employee.

As far as my successor goes, I strongly recommend that we execute our succession plan and name Tim Cook as CEO of Apple.

I believe Apple’s brightest and most innovative days are ahead of it. And I look forward to watching and contributing to its success in a new role.

I have made some of the best friends of my life at Apple, and I thank you all for the many years of being able to work alongside you.

Steve"

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Getting in shape the geek way

I caught a Wired magazine article about Tim Cigelske. He is attempting to run a mile and drink a beer, every day for a year. While my days of drinking beer (nearly) every day are long past, the article inspired me step up my own workouts. I should note that I’ve been a runner on and off for a couple of decades, even completing a marathon in my thirties. So it isn’t a stretch for me to start running again.

Of course, like a true geek, I’m getting help from the Web with my training. I’m using FitBit to keep track of my exercise and diet. FitBit is a pedometer type device that is used in conjunction with their web site. The device is tiny, well designed, and stylish. I can wear it on my belt and it is barely noticeable or I can keep it in my pocket. There’s no bulky nerd case like those that hold phones (or calculators back in the ‘80s). It keeps track of your exercise by counting your steps throughout the day. You may also wear it at night to track your sleep patterns (I find the sleep data pretty intriguing).

The FitBit connects to a web site by syncing through a computer. The process of getting started is very simple, but you have to download a small software program and register on the site. FitBit walks you through this step by step. The device syncs via a USB cable that is provided and comes attached to a cute little stand. The software sits in the background, waiting for you to plug in device in; once plugged in, syncing data is automatic.

But then there’s the really nerdy part. The FitBit web site has forms to enter what you’ve eaten, types of exercise you’ve done, or your weight. Nutritional information has been crowd-sourced, so virtually anything you buy at a restaurant chain or grocery store is listed. Because it’s crowd-sourced, though, there are often many entries for the same thing, the trick is finding the most accurate entry. Of course, if you want, you can enter the nutritional information yourself. The web site also tracks daily weight, body fat, or measurements (like your waistline) and plots them on a graph.

So I am thirteen days into my running streak. Next week I’m off to camp with my son, so I may modify my goal to run, bike, or swim every day. My FitBit came yesterday, so I’ve only just started the geek side of my training. I can see that entering calorie and weight data could get tedious, especially if the graphs trend in the wrong direction. So I’m taking the step of connecting sharing by FitBit data on Facebook and Twitter. I’m hoping that public exposure will keep me motivated. Wish me luck.

What's up with Google?

Several years ago a friend introduced me to Google Maps. At the time, MapQuest was king, but MapQuest presented a tiny square on a page that painfully refreshed as you scrolled or zoomed. Google Maps was entirely different and now we take it for granted. But the way a map was presented via tiles, the size of the map, and the speed of panning was amazing. That mapping is so pervasive, even MapQuest works that way today (does anyone use MapQest anymore?).

This was roughly the same time as Gmail was introduced, and everything that Google did was impressive. Google was developer paradise; the place where all software engineers dreamed of working. Its’ reputation of encouraging innovation was, and is, beyond all others. Even the Google doodle, the sometimes funny and irreverent logo presented on its’ search page, was creative. All of that creativity is funded by the immense engine of search; ad supported search.

The company is still a financial juggernaut. Remember the IPO? They set their price at $85; it’s now $547.

With all its’ growth and success; with all its’ creativity and innovation; I still sit back and wonder: what’s up with Google? I have never known any other company that has thrown more ideas, poorly executed ideas, into the marketplace. It’s although the company simply operates on a “try it and see what happens” strategy. As examples, look at this list…

• ·         Orkut
• ·         Google Wave
• ·         Buzz
• ·         Google Health
• ·         Google X
• ·         Google Video
• ·         Google Answers

You could argue that the crazy popular acquisition, YouTube, may never make money. And the critically acclaimed Google Docs remains an also-ran next to Office, which even as a web application is far superior. The jury is out on Google Music and Google+, but the company doesn’t compete well with entrenched players like Apple and Facebook, especially on their own ground.

So what’s my point here? I guess I am stunned and amazed at the search giant’s success. I find it incredible that a company can grind out such a large number of failures. And yet to this day, any developer worth their salt would go there in a heartbeat. The businessman in me would advise them to stick to their success, but the inventor in me loves the way they keep trying.