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Data Mining Authors: Progress Blog, William Schmarzo, Robin Miller, Jnan Dash, Liz McMillan

Related Topics: Data Mining, Cloud Data Analytics, Big Data on Ulitzer

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Big Data Life in 2023 | @CloudExpo #IoT #M2M #BigData #ML #Microservices

Let's look out a few years and see how data and analytics might transform our lives and the lives of those around us

Instead of writing yet another “Big Data Predictions for the Next Year” blog, I thought I’d do something a bit different. Instead of predicting – like everyone else – what’s going to happen in 2016, let’s instead look out a few years and see how data and analytics might transform our lives and the lives of those around us.  Probably a silly exercise, but a fun way to wrap up 2015 nonetheless.

My Morning
My wellness band actually woke me up about 15 minutes before my alarm because it had used data about my sleep patterns to determine that I was ready to get up (it automatically turns off my alarm so that it doesn’t bother my wife).  I slept well last night. I get a gold star.  Yippee…

I pull up my tablet to review my personalized workout schedule.  My personal trainer uses my workout and wellness goals and most current workout performance numbers to construct a personalized workout schedule for me.  Yuck, it’s a weight lifting day. I hate those days. More pushups, more pull-ups, and more weight lifting. And with my wellness band recording it all, there is no lying.  It’s going to be a long morning…

After my workout, I shower (my intelligent shower minimizes water usage as required by the state of California, but at least I get to flush the toilets now!) and eat my personally recommended breakfast (more protein, less fat). The camera on my phone records what I eat and estimates the calories, protein, fat, etc. That Cap’n Crunch cereal is really going to cost me.

My tablet delivers to me the news that is most relevant to me. I see that there is trouble in the Middle East. Bandwidth problems are slowing the transmission of new educational courses for the growing student base in the Middle East. Yes, President WhosIt decided to leverage data and analytics to create personalized student curriculums with the goal of providing more free education for the youth in the Middle East. Instead of more “boots on the ground,” we now employ “books on the ground.” I am proud to say that my “Big Data MBA” textbook is very popular over there (actually, Sharif University of Technology in Iran does use my “Big Data: Understanding How Data Powers Big Business” book in their engineering classes).

At the end of the day, all the metrics about my workout, steps, diet, stress, water consumption, etc., is provided to my doctor, personal trainer and dietitian so that they can construct personalized recommendations and make snide comments about how bad of shape I am in. Yep, I expect my Wellness score to drop and my Stress score to jump. Ugh.

Work
I have a client call today that requires me to get into the office to use the telepresence room. I’d better wear more than sweats today.  I schedule a driverless Uber car to take me to the office, and when it arrives, it has already picked up my favorite Starbucks venti chai latte and has it waiting in the car for me.

Thanks to the smart city initiatives and the increase in smart cars, the trip to the office is very quick with traffic jams a thing of the past. I miss the rubber-necking…NOT!

On the ride to the office, I review the status of the data science work that we have been doing for the client. The client has asked my team to evaluate how they can best monetize an acquisition that they are contemplating. My team gathered some of the client’s and acquisition candidate data, and then merged that data with several external data sources. We have come up with several ideas for how the client can leverage the client acquisition to create new monetization opportunities with respect to new products, services and channels. Seems like a win on many fronts. Need to schedule a face-to-face meeting with the client’s senior executive team in a couple of weeks to finalize the details, so I’d better get on top of planning that trip.

Plan Client Trip
I use Fluber to handle all my travel plans. Fluber works much like Uber in that I tell it where I need to go, when I need to leave and return, and my “comfort” requirements (like larger seats and on-time reliability). Fluber combines data about me and my flying and hotel preferences and propensities with data science to create a personalize flight itinerary. Fluber has a traveler reward program, which means that I am free to fly whichever airline best meets my travel and comfort needs. I’m no longer held captive to the brutality and randomness of the airline frequent flyer programs.

Predictive maintenance efforts by companies like GE Aviation and Boeing has dramatically reduced the number of flight delays due to mechanical problems (since these organizations have taken over the maintenance of the engines and the aircrafts from the airline industries) that has greatly improved the reliability of travel.

GE Aviation, Boeing and other airline manufacturers have also leveraged analytic insights about their airline customers, passengers, and flight operations to metamorphosize their own business models, taking on more of the airplane maintenance, upgrades, certification, insurance and other related operational factors. This means that since all the airplanes are fundamentally the same, that the airlines need to compete on factors such as comfort, entertainment, amenities and the flyer experience. Many of the traditional airlines have failed and have been replaced by new airlines that have a better grasp of how to combine customer and operational data with analytics to provide an improved and more reliable flying experience. Flying is actually an enjoyable experience again.

Teaching
I have to teach at the university tonight and need to make sure that I am on top of the students' project progress. I created six teams with five students each, and each team has a different client scenario (scenarios provided by my consulting clients) for which they need to apply the Big Data MBA and Thinking Like a Data Scientist concepts to uncover actionable insights. The stakes are high because the goal of the class project is to see if I can get each group a job offer from their respective client prior to their graduation. Last year I had a 100% success rate, but have only gotten five out of the six teams job offers at this point with two weeks to go. Ugh

I’ve scheduled driverless Uber to get me to the university and back home, but need a car with a video screen so that I can review progress with certain teams on the 45-minute ride to class. The analytic tools have improved to the point where MBA and business students can easily download data sets into an analytic sandbox and use visualization tools to quickly tease out some insights about the data. There are some basic analytic tools that help the business students to apply rudimentary data science techniques to quantify cause-and-effect and determine the goodness of model fit.

Plan Vacation
I have submitted our vacation plans to our favorite vacation-planning site (where we want to go, when we want to go, how long we want to stay, special activities), and that site has pulled together all the travel plans (they probably use Fluber as well) and made the appropriate resort reservations. We are pretty predictable as we go to the same resort about the same time every year, so we’ve built quite a bit of clout with that resort.

The resort leverages our customer data to deliver a more personalized experience, meaning we get a special room and some special promotions tailored exactly to what we like to do and eat. They have also created some recommendations as to new restaurants that we might want to try (with a coupon) plus some different activities (no, I still am not going to try parasailing). The personalized experience is one of the reasons we keep coming back to that resort every year.

Evening
I complete my evening by reviewing my priority email messages. I have a message from Miranda, my financial advisor, with some investment recommendations. I have a CD coming due, so Miranda recommends a moderately aggressive mutual fund that matches an increase in my life expectancy calculation (due to an upgrade in my exercise, wellness and diet scores). I select the [Accept] option.

Finally, MySpending optimization application has identified a deal for my favorite running shoes that will save me $60 off of the regular price. The application also recommends that I order some of household staples (favorite branded toilet paper, tissues, toothpaste and cereals) in an upcoming Target promotion that will save me $48 off of my budget for the month. I select the [Accept] option.


The year 2023 should be a lot of fun and life should be a bit easier as organizations of all sizes learn how to leverage data and analytics to provide a more compelling customer experience. And we will all be the beneficiaries of that!

I expect that 2016 will continue to advance us towards this vision of 2023.


Here are a few developments that I expect will happen in 2016:

  • Year of the Hadoop data lake. There will be a growth in awareness, methodology, tools and success stories that will push more organizations to the data lake.  We’ll see data warehousing teams using the data lake for both rudimentary (off-loading ETL from the data warehouse) to the more adventuresome data warehousing tasks (moving their data warehouse platform to the data lake).  Check out my blog “How I’ve Learned To Stop Worrying And Love The Data Lake” for more details on the role of the data lake.
  • Data Science is NOT Business Intelligence 2.0. Too many traditional BI organizations and vendors continue to confuse the market about data science. Check out my blog “Dynamic Duo of Analytic Power” for a simple example of the differences between data science and business intelligence.
  • Mid-market is the biggest Big Data Winner. While most of the press is around how large organizations are using big data within portions of the organization to deliver compelling financial results, large organizations have much working against them before they will see wholesale, organization-wide adoption of big data.  And that is where Mid-market organizations will excel (see my blog “Mid-market Big Data Call to Action” for more details).
  • The Internet of Things and wearable computing (smart watches, fitness trackers, magic bands) will drive a flood of new data, especially with technologies like Apache NiFi for real-time data collection and dataflow management. These new devices and resulting data will create a “connected” world, but organizations need to understand the decisions that these new devices and data sources can help support in order to transition to a “smart” world (smart cities, smart cars, smart schools, smart utilities, etc.).

Finally on the personal side, I hope that I can continue to provide useful and actionable insights to my readers and followers.  I am honored every time someone reblogs, likes, retweets, shares, or comments on my blogs.

Oh, and one last thing (as a true Chicago Cubs fan):  Wait ‘til next year!

Here’s to a GREAT 2016!

Big Data Life in 2023
Bill Schmarzo

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.