Forbes Predictions of Big Data

The attached Forbes article outlines 17 predictions for the future of Big Data. In particular I want to touch on a few points that are made within the article and try to expand on what they mean for the future of business. There are clear logical connections between the different points that the article makes, ranging from the way they continue to develop an industry and change the economy as whole.

To start, it’s crazy to think how young the field of Big Data really is. Recording, tracking and analyzing statistics, particularly in the world of business, is something that profoundly affects success, but hasn’t been able to be performed until recently. That being said, the article outlines how the volumes of data are going to continue to grow. Not only will how much be added on but also what can be tracked. More specifically, turning things that seem qualitative into quantitative forms. Doing this allows a company to instantly track something that before seemed harder to understand. This is another example of how the ways that data is analyzed will continue to improve.

Not only will the technology used to analyze it improve but the ways that us, as the directors, learn to think about the data.  As we gain a better picture of the capabilities of data, we will be able to become more specific about what we realize it can tell us. This will not be an easy task and all companies will have expanded divisions for digital departments (whether it be for purely collections, processing and analyzing or for privacy and protection). As much as humans are afraid that we will lose our jobs to computers and AI will take over the world one day, in these instances, it’s really only creating more positions to build, maintain and improve these things. In addition to companies expanding their own departments on these things, more companies created to handle these things in particular will emerge. So much of our economy is based around specialization and in an up and coming field like this, there’s no better place. Whether it be specialized around housing and collecting data or processing and reporting it, there are plenty of opportunities and more importantly, money to be made.

After the data is collected, the steps that follow will be improved-upon. Software that is created to analyze the data much faster is in high demand. The article references the uses of prescriptive analytics as well as machine learning, which both make the job significantly easier. After all, the point of collecting and reviewing such data is ultimately to make informed business decisions, and this is will decrease the turn-around time for such decisions to be made. Since the article also outlines how all businesses will be data businesses, this will naturally make all fields more competitive (though barriers to entry we decrease as well since entrants will have access to much of the same software will be available, they will just need to collect their own data in the field). No matter what industry a company is in, they will be able to collect much richer data regarding their user-base. In turn, the consumer experience will improve in virtually all areas. It may be either through an account on an app or a website, or though notifications sent through a rewards program that runs through a company card (like Target or Stop and Shop), the consumer is naturally going to be attracted to the best experience. This isn’t something that the article focuses on, but I do believe it may be overlooked in the field in general because it creates a new perspective on why Big Data can be so essential.

In all, there are plenty more that can be said about this Forbes article, feel free to read it yourself and provide your own commentary! The link to it is: https://www.forbes.com/sites/bernardmarr/2016/03/15/17-predictions-about-the-future-of-big-data-everyone-should-read/

Big Data in Sports

Growing up an athlete, sports never seemed like something that I would consider to have an analytical side. As the field of Big Data grows, so does people’s abilities to use it on the field itself, the field of sports that is. Sparked by the recent Michael Lewis movie “Moneyball”, people are much further intrigued by the capabilities of analytics in the world of sports.

The attached article outlines some ways that the field is growing. It talks about the English Premiere Soccer team Arsenal, the NBA’s Dallas Mavericks and other sports, taking different approaches to create unique statistics within the game allow teams to gain new perspectives overall. In reality, their statistical approaches span beyond the field of play. Things such as nutrition (both through diet and hydration) and sleep habits are two easily trackable areas that have a profound impact on performance.

Another example within the article is about how soccer teams install sets of 8 cameras within the stadium to track every player’s movements and their interactions with the other players. In the past, most stats have to do with the player in possession of the ball, but these cameras allow things to be tracked away from the ball which still end up extremely meaningful to the outcome of the game.

Big data in sports will continue to grow and expand in many different ways. 

https://www.forbes.com/sites/bernardmarr/2015/03/25/big-data-the-winning-formula-in-sports/#32d83bce34de

Malcolm Gladwell on the Limit of Data's Answers

Does gathering data guarantee you more success? Malcolm Gladwell does think so. Author of books such as The Tipping Point, Blink and Outliers, is known for being outspoken about his opinion in market trends. In his article, Gladwell speaks about the limits of data – further, the questions that would not be able to be answered regardless of how much data were collected.

Gladwell uses some modern day references to illustrate an issue that arises from data. He gives examples of developmental changes against generational changes; developmental topics have not changes the course of history, such as the horror genre of film against hip hop music, which is deep rooted in culture. He then brings up other topics to ask which category they fall under, which he proves data can’t answer for us.

Skipping the bulk of further details, Gladwell brings up very valid points about how humans are needed, at an intelligent level, to be properly interpreted or else it is essentially meaningless. It’s easy to lose the point of data collection when immersed in the details, but we are looking to improve our business decisions at the end of the process. Although the industry of digital data collecting will continue to grow, it’s important to also recognize the capability of what we are doing.

http://www.geekwire.com/2015/author-malcolm-gladwell-more-data-doesnt-mean-you-know-everything/

Big Data in Chess

As an avid chess player, I have often been amazed by the amount of data that is available. The range of data ranges from players (past and present), openings against their winning percentages (how often you will win, draw and lose when playing a certain opening) to games overall and when positions have been played in the past. Databases have been compiled with so many games, which players are able to study in hopes of improving their own games. But data in Chess goes way beyond this.

A challenge of Chess has always been to find concrete points – or mathematical certainties within a game. One of the first things that players are taught about Chess is the value of the pieces. Though there are keen situational differences, being able to assign a point value to each of them makes in-game decision-making much simpler. Different people will assign slightly different value to their pieces, but one person used advanced data to weigh several different factors against each other to find so called values which are more precise than traditionally-accepted ideas. As Chess games are often as unpredictable as life itself, there will likely be shifts in the way the game is played within the framework of the same rules, so it can still be something subject to change over time. Regardless, Chess is a game that revolves around logic and data.

The article is available at: http://www.sumsar.net/blog/2015/06/big-data-and-chess-followup/

Storytelling in Reporting

Using data to do research, then presenting that data in a meaningful way takes a refined skill set. Beyond just setting objectives and collecting data to lead you towards a conclusion, reporting requires a narrative. Often this can be the most challenging aspect, since it's not always the most apparent. Ray Poynters article outlines seven tips to lead you, the writer, towards the story latent within your data.

Though some of this can be considered reminders of what we already know, that's often exactly what we need when we are struggling to tell a story. From building a framework to looking at the big picture, many of the tips are not particularly complicated, yet create a principled approach towards how we should view reporting. His tips continue on to remind us the real reason that we are writing a story in the first place - to drive informed business decisions. It's easy to allow our findings to carry us in a direction but as the captain of the ship, it's important to remind ourselves the whys.

Before concluding, the article gives us a concise example of how to keep our points, logically backed by information, relevant and effective. After all, this should always be the goal.

Reporting is a craft that takes a lifetime to master, but articles like Poynters are extremely helpful to young marketers and researchers such as myself. 

The article is available at: https://www.visioncritical.com/storytelling-with-data/

Data In the Autonomous Car Industry

            For decades, little change had been made in the Automotive industry. When a forward thinking cast including Elon musk founded tesla, the foundation had been had been set to disrupt the industry - but in more ways than immediately seem apparent. Yes, Tesla was an electric car company, many of which had been founded to cease existence in just a few short years, but it only took about a decade for the company to begin developing self-driving capabilities. Although Tesla is not the first company to venture into this endeavor, they are the first car manufacturer to streamline the process. That said, the other companies such as google, that are working on the same venture, don't manufacture vehicles as well.

Creating electric cars is a praiseworthy feat itself, but the self-driving capabilities may be the more revolutionary field. Although we all like to believe we are the best drivers on the road, being human does limit our reaction times, but we need to make quick decisions under moments of high pressure and stress, for example moments before a potential accident. A computer making decisions in this moment is not subject to err in the same way. Besides this, drivers are becoming distracted more every day- in the ways that computers are not.

Self-driving technologies are driven by data. Tesla vehicles are equipped with cameras that instantly collect data that is analyzed to make decisions, specifically when in auto-pilot mode. These computerized decisions are significantly better drivers than humans. They take into account things such as vehicles and their proximity, the speed limit in any given area, the lines on both sides of the road, any curves in the road, and so on. This allows the driver to sit back (though hands are required to be on the wheels periodically) and allow the car to drive itself.

In just a few years, human-driven cars may be a thing of the past. Due to a number of companies desires to enrich the human experience – essentially finding a way to turn information about the surroundings of a car at an instantaneous rate into information – an industry is being revolutionized. This is something truly phenomenon; using data, simply capturing information from environment around to safely drive a vehicle. There have already been videos of the cars braking seconds before any human driver would have and prevented accidents because of this. In addition to improving the technology, the next step is making it more accessible. With the launch of the Tesla Model 3 in 2018, a car with this technology will be much more available since the car will cost around 30k. To this point, all of Tesla’s vehicles will cost a buyer close to six figures, so this is clearly a substantial difference. As Tesla then leads the technology curve, it is clear that other automotive companies will quickly follow in integrating fully-autonomous software into their newest vehicles. Though it sounds ambitious, the change can overtake the industry faster than we may imagine.

Beyond the luxury of being able to relax as the car drives itself in front of you, as earlier stated, this can truly enhance the human experience. It seems like every day we drive by a car accident on our commutes to work – which incur injuries and costs that are never convenient. Although this technology will never be able to full prevent that, it will surely decrease the number of accidents, making driving a safer activity overall. As an activity that we all take part in on a daily basis, being able to drive safer will do more than just have safer roads - it will eventually be able to expand lifespans of all of us. Though these are bold predictions, the technology and people backing them will be working diligently until they become a reality.

https://www.wired.com/2017/01/teslas-new-autopilot-may-seem-lame-critical-reboot/

Using Data to Improve Our Sleep

If you’re like me – and the rest of the country – you wake up tired in the mornings. One thing that we all have in common is that we sleep every night, just some of us not as much as we should. Though this sleeping phenomenon has been happening since the dawn of man, tracking and understanding sleeping habits is something that has been developing much more rapidly in recent years. Commonly-owned electronics, such as Iphones have apps that are meant to track your sleep.

For a long time, sleep tracking was something that was exclusive to the wealthy, but this is clearly not the case anymore since the majority of Americans own cell phones. With an Iphone, you can set a bedtime and a wake-up time, also having you pick how long your ideal sleep period is. Before your bedtime, the phone will give you a reminder that it’s almost time to call it a night. Although there are many factors that go into having a comfortable sleep space, understanding your sleep times are vital to you being well-rested the next day. The New York Times article backs up these ideas and also states that people do not accurately recall how many times they woke up in the night or recall any disturbances through their hours of sleep. If we can find it in ourselves to allow technology to make up for our deficiencies, we can improve our nightly sleep habits. 

Introduction: Use of Data In Our Lives

From the beginning of time, the way that information has been collected, organized and analyzed has developed. I'm the past century, our methods for all of these things have developed immensely and now drive our daily lives - I challenge you to find five activities you do daily that haven't been improved by use of data. From unbeatable chess engines to IBMs Watson and self-driving cars, the world is growing quickly in these types of areas.

Business school has taught me uses for information that I had never dreamed of. As I complete the last few classes of my MBA, the way I view virtually all decisions, work and personal comes down to data analysis. That being said, I think it will be an enjoyable to analyze how data affects the layman a daily decision making. Many of the topics I write about will be regarding activities that the average American does daily but may not realize has become data-driven. After all, the collection, organization and analysis of data all is geared towards allowing us to make better decisions, therefore seeing how these concepts relates to the average person can teach us something new.