Trillium Insights

Thoughts and Insights from Trillium's Practice Leaders

Big Thoughts about Big Data

Big Thoughts about Big Data

Big Data is Not a Technical Issue, it’s About People

For 95 percent of all problems, the technology and tools are available to handle everything you need to do.  It really comes down to people, politics, and policies. The technology is there to implement, but you need buy in, you need the talent, you need the budget.  Big data culture today is really in the Stone Age.  The technology has gotten way ahead of what we can do culturally. We’re good at cranking out MBA spreadsheet jockeys who don’t really understand the raw material they use. They understand how to manipulate data, but they don’t understand it fundamentally. When there’s a problem with a Big Data implementation, it’s usually a not a technology problem but a people problem.  People don’t want to have the way they work scrutinized.  In failed Big Data projects, the people affected often were misread or ignored.  The key to outcomes that matter is tying Big Data into people’s real agendas. If you leave the person out, you will not solve the right problem.  The best analytics are ones that can give insight into who to talk to.

Big Responsibility

Beyond the practical considerations, technology professionals have a responsibility to be cognizant of the possible effects of the data we collect and analyze. The big ethical issue is that nobody thinks this is an ethical issue. The consequences are very real. We will see some really sad, heart-wrenching uses of data that will destroy an individual, and possibly groups of people.

Collecting Can Create Big Liabilities

As our technology outstrips our laws, key questions have yet to be addressed: Who has the real ownership of a given data set, technologically, legally, and societally? Who takes responsibility when agreements are violated? We simply don’t have the body of law to deal with those questions, and they are not moot. Like many other Big Data collections, the U.S. Census is protected by law from being used for any other purpose. But, laws can be fungible. Those laws didn’t stop the government from using Census data to identify Japanese Americans before sending them internment camps during World War II, for example. On a practical basis, improving your data collection and analytics may mean being held to a higher standard of quality. Just because you collect the data, does that mean you are responsible for analyzing it?  This is uncharted territory. What are you required to keep and what are you required not to keep?

The Chicken or the Egg? (Data or the Question?)

One view of Big Data holds that organizations should gather as much information as possible. When you start to dig into the data, you see things. You can find things even when you don’t know you’re looking for them. That does not mean that your sole intent should be collecting masses of data in hopes that it will solve a business problem.  Big Data by itself is kind of useless. You need big analytics to make a big impact. Besides, you don’t really even need Big Data for big analytics. Lots of important findings come from relatively small data sets.

Lasting Forever

Big Data is “a genie without a bottle,” because information once made digital can never be called back. No matter what legal or other protections it may appear to have, at some point someone you may not trust could have access to that data, because data’s lifecycle extends beyond our control.

Following Your Passion During a Job Search

Following Your Passion During a Job Search

You’re in job search mode and have a solid background in your chosen career.  However, something may be missing in your search.  You may be able to combine your personal passion with your next potential career move.  

Whether your passion is a hobby, a charitable endeavor, a specific industry, or something else, you might be able to find target organizations in your chosen area to look for a job.  A job change is a significant decision in your life, whether it is voluntary or the result of another separation, so it is a really good time to carefully consider what you want to do as your next career move.  Thinking about what you would do, if you were in a position where you could work for free, will help you to identify target positions and target organizations that can help you meld your out-of-work passion with your career aspirations.  For example, while you might be a marketing professional, your passion might be related to education.  You can start by reviewing educational companies or colleges and universities for open marketing positions, or you may know contacts within those institutions that you can ask for an introduction.  You may be pleasantly surprised that there are available positions that match your personal interest.          

Remember that your passion pursuit may also be helpful in your career pursuit.   Best of luck in your search!

Meaningful Big Data

Meaningful Big Data

For the last year, I’ve been looking for examples about how “Big Data” not just helps, but actually brings changes that transform something—a function, a business model or an industry.

The term "Big Data" was reportedly first used 15 years ago, back in 2001 by Gartner. Most large technology companies today have a dedicated Big-Data section on their websites, and there are blogs and communities set up around big data and associated hardware and software.  Yet there are only banner sound bites that Big Data should be part of everyone’s vision.   What pushed Big Data into the enterprise and why doesn’t everyone use it? Velocity, variety and volume and veracity are known as the four Vs of big data--how new (or old) is the data, how different are the sources and content of the data, how much data are we talking about, and what is the quality of the data?  I don't remember there being a specific tipping point at which data became big, but it had something to do with when the volume of data started to get too big for the tools of the day to deal with it.  Social media has also been significant in big data's emergence. The impact of smartphones, around the time of the iPhone4, also saw a big hike in the amount of data available.

Technology developments elsewhere are also driving big data.  For example, in just the Airline sector alone airplane engine sensors have become more sophisticated. A Boeing jet generates 10 terabytes of information per engine every 30 minutes of flight. That means a cross-country flight by a twin-engine Boeing 737 would produce 240 terabytes of data—that could be used to streamline maintenance operations, decrease fuel consumption, improve safety, and increase customer satisfaction. If the four Vs are an essential backdrop to any conversation on big data, so is the distinction between structured and unstructured data. The former is simply data the airline controls and structures internally for specific purposes, while the latter covers just about everything else, from weather forecasts to what people are saying about your airline on Twitter.  Ticketing and reservations, customer support, trade data, irregular operations, maintenance logs and crew maintenance are examples of structured data. Aviation has been data-driven for decades, monitoring customers and operations, but has it been using it in the best way?  The traditional silo approach will not work with data as different people want to be involved - finance, IT, revenue management - and they are thinking and working together. 

Another argument is whether big data is best used for cost-reduction or revenue-generation. Big data intelligence can identify weak points in an airline's cost structure. New airline engine sensor readings can help a carrier predict and correct a mechanical fault before it has happened, reducing downtime and disruption. Turns out that airlines are essentially “flying blind” when it comes to keeping their expensive planes in the air. As passengers, we all have stories of being trapped on the tarmac due to a “maintenance issue.” Inefficient operations are more than a customer service inconvenience. Air carriers lose a whopping $10,000 for every hour spent on the ground performing maintenance, repair, and overhaul (MRO). Even more importantly, inefficient maintenance operations create safety hazards. The U.S. Office of Special Counsel has criticised the Federal Aviation Administration for years of inattention to “lax airline maintenance.” Then there’s the wasted fuel and pollution caused by poor maintenance information.

Equipment problems don’t have a schedule—they can happen at any time. But most airlines rely on the maintenance schedules for individual airplanes to address problems.  They simply aren't structured to effectively deal with the unexpected. That’s because they use overnight batch processing to track MRO. That’s tolerable for routine maintenance, but does little to address issues such as an unforeseen engine fault, or a tire that wears out prematurely. Ironically, the planes have become smarter than the backend computer systems that support them. The latest generation of aircraft produce several terabytes worth of data on a single transatlantic flight.  Trouble is, that’s just the beginning of the data. Besides the plane itself, the other sources include the airline, aircraft manufacturers, external maintenance providers, regulators, and spare parts suppliers. Some of the data are structured (e.g., held within a database) but a significant subset—a pilot’s handwritten logbook entries, a technician’s notes—is unstructured.  What’s needed is a way to gather all the data coming from the ground and the air together and keep it fresh—not just for individual aircraft but for the entire fleet. If airlines had access to all that data at once, maintenance planning could happen in real-time. For example, a maintenance planner could download a defect notification from a plane as it occurs and have the maintenance crew and replacement parts ready by the time the plane pulls up to the gate. Even better, airlines could eventually use the data to predict and take action before the problem occurs.

Utilizing Big Data for positive changes in efficiency and effectiveness can be a real possibility. I have first-hand experience.  Gathering, preparing, analyzing and applying predictive analytics utilizing these different kind of disparate data sources for Healthcare, Manufacturing and most recently Airline sectors have been three of Trillium’s success stories.