Cruise Ship Captains and Normal Accidents

With the official death toll at eleven and plenty more persons missing in the Costa Concordia cruise ship disaster, reports have trickled in that this accident was mostly “operator error” induced on part of the ship’s captain. However, as we will see in any system –whether it is tightly or loosely coupled, there is always probability of accident, leaving us to calculate if we should build more slack or buffers into a given system, or accept risks of leaving things just as they are.

As the Costa Concordia departed from the Italian port of Civitavecchia, no one predicted it would crash and sink.  According to news reports, the ship’s captain wanted to “salute” the island residents of Giglio and took the ship near shallow waters. However, through mis-calculation, the cruise ship hit rocks, took on water and eventually capsized causing passenger injuries and some deaths.

The captain of the Costa Concordia has admitted to navigational error, even though on previous journeys he performed similar maneuvers taking the cruise ship too close to the rocky island.  And while it’s easy to pin blame solely on operator error, or in this instance bold incompetence, we should not forget that accidents such as these should be expected in moderately coupled systems.

We’re all risk takers to some degree. Implicitly, we understand whether we fly commercial airlines, drive a car, or take our family vacation on a cruise there is risk of injury or worse involved.  However with probabilities of “accident” so small, we judge risk vs. reward and usually settle on taking our chances with an understanding that accidents cannot be altogether avoided.

Author Charles Perrow reminds us that in systems—whether they are tightly coupled (little to no slack) or loosely coupled (linear interactions with possible delays) there “will be failures”.  In fact, in the maritime industry, which Perrow calls “moderately coupled”, it’s surprising there are not more boating accidents.

In his book, “Normal Accidents” Perrow mentions how the maritime industry is full of risky behavior where captains make poor judgments in “playing chicken” with other boats, or take sightseeing tours that deviate from planned navigational routes.  In addition, on the sea, operator error abounds where captains “zig when they should have zagged” or make mistakes because they often work 14-hour work days. Aboard a ship, Perrow says, the captain is supreme and with so much riding on one person, it’s not surprising many of the worse maritime disasters are due to poor decisions of the captain.

So we then realize—whatever the system—whether it’s a transformation industry like nuclear power, or one that’s less complex like maritime, there will be failures and there will be accidents. There’s no such thing as a non-risky system.

In fact, since risk in systems cannot be eliminated, we can either take drastic steps such as shutting down such systems (i.e. Germany’s pledge to eliminate nuclear power by 2022) or learn to live with risks by inserting more buffers and slack to stop chain reactions—if possible. This last strategy of course is more costly to society.

Perrow reminds us that when it comes to systems, “nothing is perfect, neither designs, equipment, procedures, operators, supplies or the environment.” Indeed, we know risk is all around us. What we can do however, as business managers and/or members of society, is to think more carefully about risks we’re willing to accept vs. plausible rewards. In addition, where possible we should strongly consider building in redundancies and planning for disaster so that when the inconceivable strikes, we’re much more prepared to deal with potential outcomes.

Questions:

• It is alleged the Costa Concordia captain took the ship close to shore so that families on the island could see the ship passing. Might system automation such as “auto-pilot” prevented the ship’s captain from deviating course?
• In the Costa Concordia disaster there is potential for fuel leakage polluting one of the “most picturesque stretches of the Italian coast”.  Perrow asks; “What risks should we promote to enable the private profit of a few?” Thoughts?

What’s Next – Predictive “Scores” for Health?

In the United States health information privacy is protected by the Health Information Portability and Accountability (HIPAA) act.  However, new gene sequencing technologies are now available making it feasible to read an individual’s DNA for as little as $1,000 USD.  If there is predictive value in reading a person’s gene sequence, what are implications of this advancement? And will healthcare data privacy laws be enough to protect employees from discrimination?

The Financial Times reports a breakthrough in technology for gene sequencing, where a person’s chemical building blocks can be catalogued—according to one website—for scientific purposes such as exploration of human biology and other complex phenomena. And whereas DNA sequencing was formerly a costly endeavor, the price has dropped from $100 million to just under $1,000 per genome.

These advances are built on the back of Moore’s Law where computation power doubles every 12-18 months paired with plummeting data storage costs and very sophisticated software for data analysis.  And from a predictive analytics perspective, there is quite a bit of power in discovering which medications might work best for a certain patient’s condition based on their genetic profile.

However, as Stan Lee’s Spiderman reminds us, with great power comes great responsibility.

The Financial Times article mentions; “Some fear scientific enthusiasm for mass coding of personal genomes could lead to an ethical minefield, raising problems such as access to DNA data by insurers.”  After all, if indeed there is predictive value via analyzing a patient’s genome, it might be possible to either offer or deny that patient health insurance—or employment—based  on potential risks of developing a debilitating disease.

In fact, it may become possible in the near future to assign a certain patient or group of patients something akin to a credit score based on their propensity to develop a particular disease.

And something like a predictive “score” for diseases isn’t too outlandish a thought, especially when futurists such as Aaron Saenz forecast; “One day soon we should have an understanding of our genomes such that getting everyone sequenced will make medical sense.”

Perhaps in the near future, getting everyone sequenced may make medical sense (for both patient and societal benefit) but there will likely need to be newer and more stringent laws—and associated penalties for misuse) to ensure such information is protected and not used for unethical purposes.

Question:

• With costs for DNA sequencing now around $1000 per patient, it’s conceivable universities, research firms and other companies will pursue genetic information and analysis. Are we opening Pandora’s Box in terms of harvesting this data?

Ring in the New Year with New Data Products

For web-based businesses, and of course, those with a web presence (which is just about everyone) there’s a goldmine of behavioral data accessible with the right tools. The trick is getting past static web analytic reporting (bounce rates, page views, session times etc.) and going further into unlocking the rich treasure trove of machine data, text and weblogs that create “big data” insight.

In your business, gigabytes if not terabytes of multi-structured data are likely just waiting to be coupled with your imaginative thinking and analysis to create new data products that ultimately help drive customer interactions and revenues

For example, take a look at what LinkedIn is doing in creating new “products” with data they collect and analyze with a MapReduce approach and other techniques.

According to a recent whitepaper “Building Data Science Teams”, LinkedIn’s former Chief Data Scientist shows how smart thinking can be paired with compute power and huge quantities of multi-structured data to create innovative new products such as:

• Products that provide personalized content (which makes customers feel products/services are handpicked for them based on their wants/needs)
• Products that drive the company’s value proposition (For LinkedIn, it’s their “People You May Know” or “Jobs You May Be Interested In” algorithms which drive further customer engagement)
• Products that facilitate an introduction to other products (to funnel customers into other relevant areas of your website and thus lower your bounce rates)
• Products that prevent dead ends (ex: smart algorithms that suggest other potential purchases, i.e. “People like you also bought…”)

And of course, many of the above “products” are more than simply focused on nebulous metrics such “customer engagement”—they can directly tie to revenue improvements.

There are even opportunities to drive news cycles with unlocked insights. Companies such as LinkedIn can use information gleaned from their web server farms to build press releases such as:  “Top Ten Phrases Recruiters Want to See” or “Top Ten Job Growth Areas in the United States”.  These kinds of press releases are interesting to local newspapers, bloggers and media outlets, especially if there’s a unique angle relevant to readers/viewers.

There are plenty of companies turning to outside firms, crowds, and even their own customers for innovation. And there’s certainly nothing wrong with any of these approaches. However, those approaches may be trying too hard – especially when there’s a goldmine waiting to be unleashed just a few web servers away.

Cloudy with a Chance of Wrecking Your Business Model

Cloud computing is changing the manner in which consumers and businesses buy, manage and use technology. However, the impact of cloud on technology providers is causing an even more pressing adjustment—as business models shift from simply selling and servicing technology to instead helping companies consume it.

The business model for plenty of technology companies hasn’t changed much over the last fifty to sixty years. Sell equipment or software, install it, provide a bit of training, and reap contracts from subscriptions and/or maintenance.  And if it isn’t broken don’t fix it, right?

The rise of cloud computing is changing this paradigm.

In an October 2011 report, Bo Di Muccio and Thomas Lah of TSIA Research suggest a drastic change is coming to technology service providers.  Instead of simply installing and managing technology (via shared or managed services), cloud computing will force companies help users “consume” or use technology to achieve business benefit.

Prior to cloud computing, companies buying technology had no choice but to accept “complexity”, say Di Muccio and Lah. To reap benefits of technology, business line managers enlisted system integrators or consulting firms to install, integrate and manage technology on their behalf.  In addition, companies had to write an upfront check (capex) for hardware, software, training and implementation.

Cloud changes this model.  Instead, as more business managers get comfortable with cloud and its inherent benefits, Di Muccio and Lah argue technology service providers will be forced to adopt a “consumption economics” model where they no longer receive payment for shipping, installing and servicing a box, but instead receive revenues based on usage (pay per use).

Di Muccio and Lah also mention that cloud based computing shifts “risk” from buyers to technology service companies. For example, in previous years a business line manager might pay half a million dollars to a vendor, whether he or she uses the technology or not. With cloud computing’s pay-per-use model, the risk shifts to the vendor which only gets paid when technology is consumed.

To be sure, the shift in mix of complexity vs. consumption is not occurring overnight. However, with adoption of cloud computing increasing exponentially, technology and service providers must make plans today to meet tomorrow’s business expectations.  This means development of new pricing models, products, services, skills and training to make companies more than “buyers” but also successful “consumers” of technology.

Questions:

• What new types of skills and services will be needed to help companies “consume” technology?
• Di Muccio and Lah say the move towards simpler cloud based products will reduce the need for core professional services. Agree or disagree?

Are Data Scientists the Next Masters of the Universe?

Back in the late 1970s, traders buying and selling mortgages were pushed aside for new masters of the universe—“quants” or individuals that used mathematics to slice and dice mortgages into debt tranches. And in the same way, today’s traditional Business Intelligence (BI) professionals must be looking over their collective shoulders as business and IT publications tout the emerging role of “data scientist”.

Before Lew Ranieri came on the scene, mortgages were a very staid business. Banks would loan money and keep assets on the books for up to thirty years (depending on how quickly the loan was paid back). Except for underwriting skills, there wasn’t much complexity to the mortgage business.

As a trader for Salomon Brothers, Lew Ranieri changed all that.  Ranieri’s insight was that mortgages could be bundled together and then sliced into different tranches of varied risk.  This slicing exercise was quite complex because of a buyer’s ability to prepay their loans early or refinance.  Michael Lewis, of Liar’s Poker fame writes; “Mortgages were acknowledged to be the most mathematically complex securities in the marketplace. The complexity arose entirely out of the option the homeowner has to prepay his loan…mortgages were about math.” 

Suddenly the very boring business of home loans became a very complex business challenge in how to slice the pie based on risk profiles and cash flows from interest and principal. Lewis writes; “Different investors place different prices on risk. Risk could be canned and sold like tomatoes.” And this mathematical complexity demanded a new skill set—quantitative analysis—to perform the necessary mathematical modeling to ensure investment banks remained profitable in this new business.

Pushed out by a new breed of mathematical whizz-kids, many former investment bankers and traders either retired or left for smaller financial firms. And the rise of the quants—or the new masters of the universe—was complete by the mid-1980s.

Is a similar shift happening in the field of Business Intelligence with the emerging “data scientist” role? The skill set of today’s data scientist is much more robust than one who solely performs BI or ETL application development.  With new sources and types of data (i.e. multi-structured), the data scientist must be able to develop new data driven products such as churn models, create recommendation algorithms, assist marketers with behavioral segmentation and targeting and more.

But that’s not all. Fellow SmartDataCollective contributor Daniel Tunkelang says the data scientist; “Also needs to possess creativity and strong communication skills. Creativity drives the process of hypothesis generation, i.e., picking the right problems to solve that will create value for users and drive business decisions.”  Tall order to find all these skill sets in one person, much less build an internal competency center with such talent.

Perhaps for the foreseeable future, there’s room for both traditional BI professionals and the new breed of data scientists, as today both are valuable contributors in the field of analytics. However, with data growth on a fast paced exponential curve, much less the complexity and velocity of multi-structured data, it’s easy to see how the mix of skill sets to succeed in the future will tilt more in favor of the data scientist role.

The mortgage bankers never saw Lew Ranieri coming. Regarding the rise of data scientists—should traditional BI professionals be worried?