Driving Data: A Slippery Ethical Slope?

When thinking about telematics, it’s easy to conjure up images of fleet tracking via GPS, satellite navigation systems for driving directions, or even the ubiquitous on-board security and diagnostic systems. However, what’s less understood is that data on your driving habits, locations and more are being collected, sometimes without your explicit knowledge.

Image courtesy of Flickr. By Michael Loke

Most people don’t realize that driving data are being collected in 80% of the cars sold in the United States.  According to an Economist article, event data recorders (EDRs) are installed in most cars to analyze how airbags are deployed.  Some EDRs can also record events such as “forward and sideway acceleration and deceleration, vehicle speed, engine speed and steering inputs.”

The Economist article also says EDR data can show if a driver stepped on the gas just before an accident, or how quickly brakes were applied. And EDRs can also record whether seat belts were locked. These data can be used to augment a police crash report, corroborate accident events as remembered by a driver, or even be used against a driver when negligence is suspected.

This brings to mind a key question – who owns this data? The Economist article says that if you are the car owner, it’s probably you. However, if your car is totaled from a crash, and you sell it to the insurance company as part of a claim resolution process, then it’s likely your insurance company now owns the data.

Data can be used for purposes advantageous and disadvantageous to a driver.

An MIT Technology Review article cites how a new $70 device is now available to hook into your car’s EDR. This device wirelessly transmits data via Bluetooth to your mobile phone on your driving efficiency, cost of your daily commute, and information on possible engine issues.  And the company providing the device can deliver a “score” for your driving habits, gas savings and safety in relation to other drivers.

Driving data can also be collected for things you did not intend. For example, a team of scientists used mobile phone location data gleaned from wireless networks to detect commute patterns from more than 1 million users over three weeks in the San Francisco Bay Area.

These scientists discovered “cancelling some car trips from strategically located neighborhoods could drastically reduce gridlock and traffic jams.”  In other words, some neighborhoods are responsible for a fair portion of Bay Area freeway congestion.  The scientists claimed by cancelling just 1% of trips from these neighborhoods, congestion for everyone else could be reduced by 14%.

Of course, drivers in urban areas could be incentivized to use public transportation, carpool or telecommute, but it’s also possible that a more heavy-handed government approach could restrict commutes from these neighborhoods—on certain days—“for the good of all.”

Data are of course, benign. However, driving data from GPS and other devices are collected daily—and sometimes without your consent.

Altruistically, these data may ultimately be used to design better cars, better freeways and improve the overall quality of life for everyone concerned. Yet, it’s also important to realize that mobile data from daily road travels can also be utilized for tracking purposes, to pin down exactly where you are located at any given moment in time, and how you arrived.

And that thought should give everyone pause.

Should You Be Wary of Big Data Success Stories?

For every successful “Big Data” case study listed in Harvard Business Review, Fortune or the like, there are thousands of many failures.  It’s a problem of cherry-picking “success stories”, or assuming that most companies are harvesting extreme insights from Big Data Analytics projects, when in fact there is a figurative graveyard of big data failures that we never see.

Courtesy of Flickr by timlewisnm.

“Big Data” is a hot topic. There are blogs, articles, analyst briefs and practitioner guides on how to do “Big Data Analytics” correctly. And case studies produced by academics and vendors alike seem to portray that everyone is having success with Big Data analytics (i.e. uncovering insights and making lots of money).

The truth is that some companies are having wild success reporting, analyzing, and predicting on terabytes and in some cases petabytes of Big Data. But for every eBay, Google, or Amazon or Razorfish there are thousands of companies stumbling, bumbling and fumbling through the process of Big Data analytics with little to show for it.

One recent story detailed a certain CIO who ordered his staff to acquire hundreds of servers with the most capacity available. He wanted to proclaim to the world – and on his resume – that his company built the largest Hadoop cluster on the planet.  Despite staff complaints of “where’s the business case?” the procurement and installation proceeded as planned until the company could claim Hadoop “success”. And as suspected, within 24 months the CIO moved on to greener pastures, leaving the company with a mass of hardware, no business case, and certainly just a fraction of “Big Data” business value.

In an Edge.org article, author and trader Nassim Taleb highlights the problem of observation bias or cherry-picking success stories while ignoring the “graveyard” of failures. It’s easy to pick out the attributes of so-called “winners”, while ignoring that failures likely shared similar traits.

In terms of charting Big Data success, common wisdom says it’s necessary to have a business case, an executive sponsor, funding, the right people with the right skills and more. There are thousands of articles that speak to “How to win” in the marketplace with Big Data. And to be sure, these attributes and cases should be studied and not ignored.

But as Dr. Taleb says, “This (observation) bias makes us miscompute the odds and wrongly ascribe skills” when in fact in some cases chance played a major factor. And we must also realize that companies successfully gaining value from Big Data analytics may not have divulged all their secrets to the press and media just yet.

The purpose of this article isn’t to dissuade you from starting your “Big Data” analytics project. And it shouldn’t cause you to discount the good advice and cases available from experts like Tom Davenport, Bill Franks, Merv Adrian and others.

It’s simply counsel that for every James Simons—who makes billions of dollars finding signals in the noise—there are thousands of failed attempts to duplicate his success.

So read up “Big Data” success stories in HBR, McKinsey and the like, but be wary that these cases probably don’t map exactly to your particular circumstances. What worked for them, may not work for you.

Proceed with prudence and purpose (and tongue in cheek, pray for some divine guidance and/or luck) to avoid the cemetery of “Big Data” analytics projects that never delivered.

Moving to the Public Cloud? Do the Math First

A manufacturing executive claims that many companies “didn’t do the math” in terms of rushing to outsource key functions to outside suppliers. Are companies making the same mistake in terms of rushing to public cloud computing infrastructures?

Image courtesy of Flickr. By peachy177

The herd mentality—we know it well. Once a given topic (i.e. agile development, Hadoop “Big Data” implementation, etc.) becomes the darling of business and management publications, a gold rush usually follows to implement. Unfortunately, sometimes there isn’t much, or any, thought put into gauging enterprise fit or building a business case for the latest and most fashionable idea.

Take for example the concept of outsourcing. During the early 2000s, cheap labor rates in China and India caused senior managers to see dollar signs as they could cut labor costs nearly in half, while gaining a specialized workforce dedicated to developing and building products and/or servicing customers.

There was a catch however. When considering topics such as delivery lag times, transportation costs, loss of corporate agility, language and communication barriers and more, the so-called cost savings often failed to materialize.

“About 60% of the companies that offshored manufacturing didn’t really do the math,” says Harry Mosler, an MIT-trained engineer who runs the Reshoring Initiative. “They looked only at the labor rate, they didn’t look at the hidden costs.”

The concept of shifting compute needs to public cloud computing infrastructures is an idea gaining traction. As the C-suite contemplates methods to deliver better, respond to market changes faster and reduce costs, cloud is an increasingly tantalizing option. In fact, the market for public cloud computing is said to be $131B in 2013 and growing, according to a tier one analyst firm.

While companies are choosing cloud for myriad reasons, it’s readily apparent that procuring public infrastructure, development platforms or applications from a cloud provider is really just another form of outsourcing.

This then brings some challenges to the forefront, specifically the need to understand the business case and use cases for cloud computing for your own company. And the needs must go beyond simple cost savings analysis.

Don’t make the same mistakes of those executives who rushed to outsourcing in the past decade. Tally up the cost savings, but also spend time diagnosing “hidden risks” of public cloud in terms of well-known issues of costs of downtime/availability, data security/privacy in a multi-tenant environment and data latency.

In addition, think about the level of control you want over your IT infrastructure. Are you comfortable relying on another vendor for critical IT infrastructure needs? In case of the inevitable IT failure or worse case cyber-attack, are you one of those who would want to start working a problem right away, rather than opening a trouble ticket and waiting for an answer?

You’ll also need to consider skill sets (tally those you have, and those you’ll need), in addition to architecting your various workloads for cloud infrastructures.

Please don’t get me wrong. For many companies, sourcing computing needs to public infrastructures makes a lot of sense, but when only supported by a thorough business case, and detailed risk analysis.  You’ll need a thorough understanding of what you’re jumping into before “joining the herd,” especially when an on-premise solution might work better.

In other words, “do the math” (figuratively and literally).

Cloud Computing – More than Simply Cost Savings

In a very competitive macro-economic climate, companies seek to reduce costs and drive those savings towards either the bottom line or re-purpose savings towards innovative projects. Cloud computing is often seen as one such avenue towards cost reductions as companies can ultimately reduce capital expenditures and data center operating costs. However, as good as the concept of “cost savings” sounds, you might be surprised to discover that according to one analyst firm, cost reduction isn’t the primary driver for cloud computing.

Courtesy of Flickr. By M Hooper

Too many under-utilized data marts and application servers and too many wasted kilowatts. That’s what consortiums like Energy Star say as they report most corporate servers are only utilized 5-15% of the time. Besides wasted energy, companies are saddled with a poorly utilized asset that’s still costing precious IT dollars in maintenance and possibly software subscriptions.

Cloud computing, then, can often ride to the rescue in terms of creating shared pools of system resources via virtualization technologies. This in turn helps reduce the number of servers needed, slashes application licenses, and ultimately trims power and cooling costs.

While cost savings are no doubt important, a recent analyst survey cites the primary driver for CIOs to approve cloud computing are: “(delivering results to the business) better,” followed by “(delivering results to the business) faster.” Cost savings comes in a distant third.

That’s because global product lifecycles are speeding up as companies adjust to niche consumer demands, and more nimble/agile competitors.

For evidence of faster product lifecycles, see GE appliances. In an Atlantic magazine article, GE appliance manager Lou Lenzi says that a refrigerator model design was formerly good for at least 7 years before a complete product refresh was necessary. Now because of accelerated product cycles, models are only good for 2-3 years as customers regularly clamor for new features, colors, styles and models such as “$3000 smart refrigerators.”

Cloud computing can help companies respond to faster product cycles. With cloud, customer demands can be met, almost instantaneously because analytic resources for product and customer analysis are “at the ready” and can grow and/or shrink as business demands. No more waiting for capital budget refreshes, or IT to find cycles to accommodate immediate business needs. And best of all, these resources are generally available on a pay-per- use or subscription basis, so they’re easier to fund from OPEX budgets.

And user friendly cloud self-service options help enable business managers to create analytic development “laboratories” where they can carve out system resources to work on special projects, test out new theories, or collaborate with product, marketing and R&D teams on a global basis.

In short, cost savings are a very important aspect for CIOs, but not always the primary driver for cloud adoption. CIOs then, fundamentally seek cloud computing options to help them align more closely to business user demands and better meet customer needs.  Indeed, with shrinking product lifecycles, the ability to source immediate compute, storage and analytics—for faster business results—could mean the difference between an extremely profitable or disastrous quarterly result.

No Gold Medals for “Black Swan” Criers?

It’s extremely unfashionable to be the “Black Swan” crier in your organization, or the person who warns line of business managers about the heavy impact of extreme but unlikely events.  In fact just the opposite is the norm, where plenty of company executives get rewarded in career growth and compensation for ignoring risks, or sweeping them under the rug for others to tackle down the road.  It’s time to listen—really listen—to what Black Swan criers in your own company are saying.

Courtesy of Flickr. By Al S

In 18^th century England, the town crier would be dressed in fine clothing, given a bell, and told to “cry” or proclaim significant news to merchants and citizens alike. Sometimes the town crier brought bad news—such as tax increases. Fortunately, such a person was protected by laws stating that anyone causing harm to the town crier could be convicted of treason.  Wikipedia notes the phrase; “don’t shoot the messenger” was a real command!

Fast forwarding to our current time, there are few rewards for those who “cry” or warn about the dangers of “Black Swans” or extreme but rare events that carry a high impact.  See here for a list of “Black Swan” events since 2001.

Case in point, leading up to the September 2008 financial crisis, only a few prognosticators could see that quasi-government agencies such as Fannie Mae and Freddie Mac were buying too many no-documentation, no-income (NINJA) loans that could go bust if the US economy went into recession.  Nassim Taleb, author of the Black Swan, was a key figure that needed no more than a glance at these agency’s financials in 2007 to declare, “(They seem) to be sitting on a barrel of dynamite, vulnerable to the slightest hiccup”.

And of course, that dynamite was lit as the global economy teetered on the edge of major depression, and the agencies ultimately lost a combined $15B. Of course, Mr. Taleb was ridiculed as a “clown” and “rabble rouser” for many of his prognostications.

Today’s corporate potential whistleblowers don’t fare much better in terms of warning about everyday risks whether they reside in supply chains, nuclear power plants, cloud computing infrastructures or other such complex systems prone to fragility. It’s much easier to carry on with business as usual, than plan and prepare for events that however unlikely, could end up disabling or dismantling your organization in one fell swoop.

Indeed, Taleb argues it’s much easier for managers to tout what they “did”, rather than what they avoided by taking proper risk management precautions.  “The corporate manager who avoids a loss will often not be rewarded,” he says.

Business executives should not turn their eyes and ears from their own “town criers” preaching Black Swans. While painful to listen to, and sometimes counter-intuitive for today’s “business wisdom”, those closest to your business operations often see what can blow up, long your before mid-level and corporate executives gain visibility.

These “Black Swan” criers may never be personally rewarded with a gold medal for highlighting key risks, but it’s the smart business that ultimately finds a way to seek their opinions and at least scenario plan for their noted “worst case event” outcomes.