Tula N.B., senior teacher,
Tashkent Branch of REU after G.V. Plekhanov
Big data as a kind of innovative ideas and technologies
Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
The understanding and targeting customers is one of the biggest and most publicized areas of big data use today. Here, big data is used to better understand customers and their behaviors and preferences. Companies are keen to expand their traditional data sets with social media data, browser logs as well as text analytics and sensor data to get a more complete picture of their customers. The big objective, in many cases, is to create predictive models.
Ski resorts are even using data to understand and target their patrons. RFID tags ins erted in to lift tickets can cut back on fraud and wait times at the lifts, as well as help ski resorts understand traffic patterns, which lifts and runs are most popular at which times of day, and even help track the movements of an individual skier if he were to become lost.
Imagine being an avid skier and receiving customized invitations fr om your favorite resort when there’s fresh powder on your favorite run, or text alerts letting you know when the lift lines are shortest. They’ve also taken the data to the people, providing websites and apps that will display your day’s stats, fr om how many runs you slalomed to how many vertical feet you traversed, which you can then share on social media or use to compete with family and friends.
Even government election campaigns can be optimised using big data analytics. Some believe Obama’s win after the 2012 presidential election campaign was due to his team’s superior ability to use big data analytics.
Big data is also increasingly used to optimise business processes. Retailers are able to optimise their stock based on predictions generated fr om social media data, web search trends and weather forecasts.
One particular business process that is seeing a lot of big data analytics is supply chain or delivery route optimisation. Here, geographic positioning and radio frequency identification sensors are used to track goods or delivery vehicles and optimise routes by integrating live traffic data, etc. HR business processes are also being improved using big data analytics.
This includes the optimisation of talent acquisition – Moneyball style – as well as the measurement of company culture and staff engagement using big data tools. For example, one company, Sociometric Solutions, puts sensors into employee name badges that can detect social dynamics in the workplace. The sensors report on how employees move around the workplace, with whom they speak, and even the tone of voice they use when communicating.
One of the company’s clients, Bank of America, noticed that its top performing employees at call centers were those who took breaks together. They instituted group break policies and performance improved 23 percent.
You may have seen the RFID tags you can attach to things like your phone, your keys, or your glasses, which can then help you locate those things when they inevitably get lost. But suppose you could take that technology to the next level and create smart labels that could stick on practically anything. Plus, they can tell you a lot more than just wh ere a thing is; they can tell you its temperature, the moisture level, whether or not it’s moving, and more.
Suddenly, this unlocks a whole new realm of “small data;” if big data is looking at vast quantities of information and analysing it for patterns, then small data is about looking at the data for an individual product – say, a container of yogurt in a shipment – and being able to know if it’s likely to go off before it reaches the store.
This part of the Internet of Things holds incredible promise for improving everything fr om logistics to health care, and I believe we’re still just on the cusp of understanding what this incredible technology can do – as when electricity was only used to power light bulbs.
Big data is not just for companies and governments but also for all of us individually. We can now benefit from the data generated from wearable devices such as smart watches or smart bracelets. Take the Up band from Jawbone as an example: the armband collects data on our calorie consumption, activity levels, and our sleep patterns. While it gives individuals rich insights, the real value is in analysing the collective data.
In Jawbone’s case, the company now collects 60 years worth of sleep data every night. Analysing such volumes of data will bring entirely new insights that it can feed back to individual users.
The other area wh ere we benefit from big data analytics is finding love – online this is. Most online dating sites apply big data tools and algorithms to find us the most appropriate matches.
The computing power of big data analytics enables us to decode entire DNA strings in minutes and will allow us to find new cures and better understand and predict disease patterns. Just think of what happens when all the individual data from smart watches and wearable devices can be used to apply it to millions of people and their various diseases. The clinical trials of the future won’t be limited by small sample sizes but could potentially include everyone!
Big data techniques are already being used to monitor babies in a specialist premature and sick baby unit. By recording and analysing every heartbeat and breathing pattern of every baby, the unit was able to develop algorithms that can now predict infections 24 hours before any physical symptoms appear. That way, the team can intervene early and save fragile babies in an environment wh ere every hour counts.
What’s more, big data analytics allow us to monitor and predict the developments of epidemics and disease outbreaks. Integrating data from medical records with social media analytics enables us to monitor flu outbreaks in real-time, simply by listening to what people are saying, i.e. “Feeling rubbish today – in bed with a cold”.
Of course, while much has been made in the past of Google’s ability to predict flu outbreaks based on search traffic, their model didn’t work in 2014. Google itself admits that just because you search for “flu symptoms,” it doesn’t mean you’re sick.
Most elite sports have now embraced big data analytics. We have the IBM SlamTracker tool for tennis tournaments; we use video analytics that track the performance of every player in a football or baseball game, and sensor technology in sports equipment such as basket balls or golf clubs allows us to get feedback (via smart phones and cloud servers) on our game and how to improve it. Many elite sports teams also track athletes outside of the sporting environment – using smart technology to track nutrition and sleep, as well as social media conversations to monitor emotional wellbeing.
Science and research is currently being transformed by the new possibilities big data brings. The computing power of big data could also be applied to any set of data, opening up new sources to scientists. Census data and other government collected data can more easily be accessed and analysed by researchers to create bigger and better pictures of our health and social sciences.
My final category of big data application comes from financial trading. High-Frequency Trading (HFT) is an area wh ere big data finds a lot of use today. Here, big data algorithms are used to make trading decisions. Today, the majority of equity trading now takes place via data algorithms that increasingly take into account signals from social media networks and news websites to make, buy and sell decisions in split seconds.
Computers are programmed with complex algorithms that scan markets for a set of customisable conditions and search for trading opportunities. The programs can be designed to work with no human interaction or with human interaction, depending on the needs and desires of the client.
The most sophisticated of these programs are now also designed to change as markets change, rather than being hardcoded.
