by Stephanie Rabinowotz | Oct 21, 2015 | Big Data, Save With Data
With today being “Future Day” due to October 21, 2015 being “the future” in the movie Back To The Future directed by Robert Zemeckis, it has got me thinking about Big Data and the future.
Without Big Data would we have achieved many of the predictions of the future made in the Back To The Future movie?
Where will Big Data take us by 2030?
With Big Data infiltrating most industries these days, we can bet that it will greatly effect the way we live by 2030. Healthcare is being revolutionized with better patient records, communication between hospitals and the CDC to control the spreading of epidemics, and even the race to cure cancer s being aided by big data. Transportation is evolving with better taxi apps and the beginning of self driving cars. Our own homes are even being upgraded by big data, with an improving real estate system and the ongoing progress in the idea of a “connected home”.
These are just a few of the different areas Big Data is making an impact, so what does this mean for our future?
What will daily life be like?
Are our jobs going to be taken over by computers?
My predictions for 2030 are as follows,
- Doctors will have access to thousands of patient records with the touch of their finger. There will be a digital library that can analyze symptoms and present multiple similar cases, the steps taken and the percentage of positive outcome. This will decrease the amount of tests needed to diagnose symptoms and ultimately save the patient a lot of money and the doctor a lot of time.
- Houses will be equipped with smart devices that communicate with one another to save energy and control routine actions such as turning a light on or off automatically. Houses will be able to tell when the owner is about to be home and automatically kick on the heat or air, turn on the lights, unlock the doors and who knows they may even make you cup of coffee!
- Transportation will be drastically improved with self driving cars and more efficient taxi services such as Uber. Uber will be able to track a frequent customer’s debit transactions to know when they close out a tab close to closing time in a bar. This will allow the Uber driver to be outside of the bar and ready for when the customer stumbles out.
- Shopping will be more than likely completely different. With the way large stores track consumer habits I wouldn’t be surprised if the stores could communicate with our smart homes to determine which foods or household products we are low on and automatically deliver them to our front door by most likely a drone.
Does this mean we are going to start losing our jobs to automated technology? Yes and no. Sure some jobs will be taken over by computers because it makes the most sense, it is most efficient. That doesn’t mean everyone is going to be out of work. All of these technologies and programs must be monitored and serviced. I think in the next fifteen years we are going to see a major shift in our educational departments towards more technological training. We will need to have more people who are proficient in the new technologies to keep up with any of them. There will be a lot of jobs created to both monitor and service these technological advances.
Will any of this happen by 2030? Will it happen at all?
Who knows, but humans have never ceased to amaze me with their ability to create new and exciting things.
Let us know what you think, where will our world be in 2030?
Happy Future Day!!!
by Stephanie Rabinowotz | Oct 20, 2015 | Measuring Results
Big Data has become very popular and therefore is being made into a “hype”. The Big Data hype gives the impression that Big Data is a magical tool that can be implemented into any company and voila!- instant solutions to all problems. Society has started to place unrealistic expectations on Big Data and the results it can produce.
It is human nature to project our interests onto others. Like the football dad who never made it to the big leagues, companies also place their desires into their data. This is why studies and surveys are supposed to be objective, because as soon as someone biases the data, the results are compromised. Big Data takes a long time to be analyzed properly to find relationships that can be translated into valuable information. When companies jump at the first correlation because it is in line with their desired result is when Big Data becomes biased.
It is extremely important to ensure your data is clean, or else it is going to lead to a lot of wasted money. Companies buy into the Big Data hype, purchase expensive big data technologies and hope that it is going to magically reaffirm all of their dreams and provide a golden staircase to them. This is not how it works.
Big Data is essentially a lot of information. When looking at a lot of information you cannot form a conclusion by simply looking at a portion of it. You must consider every piece of information and how it fits together. This is where Data science comes in. Many are moving away from the term “Big Data” because of the hype surrounding it and are embracing data science. Data science speaks more to the volume of analytical scrutiny big data must go through to provide any useful and unbiased results.
This is why we have data scientists, they are the geniuses who can look through mass amounts of data and find correlations without biasing them. If a company is going to spend the money on implementing big data they must also hire data scientists if they hope to profit from their investment.
The take home point to this would be that companies must allow the data to create new ideas and not try to drive the data with their own thinking.
by Stephanie Rabinowotz | Oct 16, 2015 | Business Transformation
The student loan debt in the United States has reached $1.2 trillion and the average graduate is released into the real world $24,000 in debt. Does it sound like student loans are becoming a problem? I think it is safe to say, YES!!!!
As a somewhat recent graduate it is sad to see my generation accepting student debt as just a way of life. People constantly say, ” It”s good debt”. What?! There is nothing good about owing banks thousands of dollars. Not to mention that many undergraduate degrees these days do very little to actually land recent grads career worthy, well paid jobs. Many degrees don’t get graduates anywhere unless they go to grad school as well. It is difficult enough for a new graduate to learn how to live on their own, paying rent and bills, only to tell them they need to pay off their student loans quickly or else interest will bury them alive. Oh and this is all while making about a $30,000/ year salary.
Now that my rant is finished we can discuss if there is a possible light at the end of the tunnel for this crisis. Thankfully there are some new Startups that are helping re think the way student loans work. Traditionally student loans are granted based on credit score and have a very strict repayment schedule. New companies like Earnest are going against the lending rules and not considering credit scores when accepting borrowers. Instead, the new company looks at a borrower’s bank records, credit cards and even social media profiles to decide if they are a trustworthy person.
You may be wondering how a company can decide what makes someone a trustworthy person? Well, Earnest uses a big data approach, looking at many different parts of an individual rather than simply one credit score that may not fairly represent that person. Earnest takes the data a potential borrower provides and puts it into their underwriting program to find what they call the “highest quality clients”. Earnest’s philosophy is not over charging financially responsible individuals with high interest rates just because they wanted an education. Earnest also allows borrowers to pay in a much more flexible fashion, choosing the length and frequency of payments as well as the option of fixed or variable interest rates.
The reason Earnest can offer lower interest rates and flexible payment schedules is because of big data. The company uses big data to make quicker, more informed decisions on who to lend to, ultimately saving the client money by skipping and extra labor or middle men. It never ceases to amaze me how many different areas big data can be used in to create efficiency and reel in savings. Maybe if other lending companies adopted the big data approach to determine which of their clients are financially responsible then they could charge them less interest. This would be a great incentive for people to pay their loans on time every month!
Let us know what you think! Student loans are a heavily debated topic, we know you have an opinion, and odds are you have student debt as well!
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by Stephanie Rabinowotz | Oct 14, 2015 | Internet of Things, Save With Data
Everyone wants to save money. I have never heard someone say they would like to pay more for something. What is one great way to save money? Lowering your energy bill of course. Everyone has got one- wouldn’t it be nice to have a lower energy bill and use your extra money on things you actually want to pay for? Better yet, wouldn’t it be awesome to save money on your energy bill AND help save the planet?! You would feel like a great person- a great person with a little extra dough in your pocket.
How can you feel this great?
BIG DATA
Big what? How does that work?
By using big data devices to monitor your energy and also receive stats on your energy usage
you can see where you can use less and conserve more!
We all like to think that we are doing all we can to help conserve energy and save the planet, but there is a lot of wasted energy we do not see. Through the use of Big Data analytics, energy companies can receive information on the amount of energy each household is using and send the consumer a usage report. This report can highlight wasted energy and inform the consumer of better ways to conserve their energy. By informing the consumer of their wasted energy, they will be able to adapt their habits to reduce their energy use and also save some money on their bill.
Another way to reduce unnecessary energy use in the household is to install a thermostat that is connected to the internet such as the Nest. Nest is a “smart” thermostat that learns the habits of the household and adjusts the temperature to ensure optimal efficiency. For example, a family may like their house to be sixty-five degrees in the summer while they are home, but when everyone leaves for work and school for the day, does the air conditioning need to keep the house at sixty-five degrees? No, unless you think your couch will get too warm without it. This is where Nest would turn off the air conditioning for the day, saving energy and money, and then return the house to your preferred temperature shortly before you return home.
These big data devices like Nest are a great way to ensure you are conserving energy without having to do the work yourself. I’m sure it also does a great deal to lower your bills as well, only using energy sucking devices when they are most needed. America has a long ways to go and much to change before being recognized as an energy efficient country, but these are some simple changes the energy companies and average families can do to help the cause!
by Stephanie Rabinowotz | Oct 12, 2015 | Measuring Results
With the Chicago Cubs and the St. Louis Cardinals playing in Game 3 of the MLB playoffs tonight, fans are eager to see who breaks the 1:1 tied score. Whoever wins tonight’s game will be one step closer to playing in the 2015 World Series. With this kind of pressure how do coaches and managers decide line ups and game time decisions?
Will Matheny and Maddon be using Big Data tonight?
Big Data has been slowly infiltrating the MLB world and is now a heavily relied upon tool for strategic decisions. Systems such as Hitf/x, Fieldf/x, and Pitchf/x capture data from hitters, fielders and pitchers and relay it to the coaches and managers in real time. This allows coaches to not only see the outcome of plays but to get a better idea of what may happen before the play is even done.
The Pitchf/x system records 20 different measurements of every pitch including the angle of the pitchers arm, the speed, location and three dimensional movement of the ball. These types of measurements are what allow the broadcasters to display the strike box in the corner of your screen, showing you real time reports of where pitches were thrown.
Big Data and statistics are not limited to just off season training, but actually being used during the games. Because of these new programs MLB teams can study pitcher/hitter relationships and strategically set their line up and shift their fielders accordingly. The data that is collected and analyzed is also what influences which signals coaches give their hitters.
Thanks to Big Data, managers can more strategically coach players and make better informed trades with more accurate stats, saving time and money. Big Data has also helped capture more fans with better screen graphics, teaching fans about the underlying strategies within baseball.
No matter who wins tonight it is safe to say both teams will be implementing Big Data into their game plan. Hopefully Big Data will continue to grow in the MLB as it has with the NFL.
To learn more about how Big Data is used in the NFL check out this article!
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