According to a Statista report, the global sports industry has crossed US$ 145.34 billion mark in 2015 in terms of revenue, thus making it one of the most valuable sectors. As professional sports are gaining more popularity, the leaders of the industry are feeling the need to improve its quality to ensure that the fans are delighted and at the same time, the future of the game can be sustained.
Here are different ways how Big Data is used for improving the quality of the game and the overall fan experience.
An injury is the biggest threat for a professional sportsman’s career. Several wearable products have been developed for collecting real time data from the athlete’s body. These devices can analyse these datasets to figure out the chances of injury that the athlete is exposed to, which can be used to take appropriate measures that may save the career of the star player.
Better fan experience
Fans are the single most important factor that decides whether any tournament will be successful or not. Fans are the lifeline of any professional sport and in order to enhance the sustainability of the sport, fans must be provided with additional benefits. Several technology firms are working together to initiate measures needed to improve the experience of sports fans while they are watching a live game in the stadium by allowing them to order foods from the stands, and detecting empty parking spots when they are entering the stadium.
Better on-field decisions
Sports Vision, a USA based sports technology company, has been using the latest innovations to make things easier for the match officials of different sports to take accurate decisions, which in turn may contribute largely to promote the quality of the game. The company has recently installed the ‘Pitch f/x’ technology across 30 stadiums that host games in the Major League Baseball in USA. According to the company, this particular technology would help the umpires to make judgements based on the real-time data gathered by the same.
Sports Vision operates across a wide array of different sports other than baseball.
Let us look into the biggest mistakes committed by the match officials during a live game. These unintended mistakes later proved to be the decider of the match result.
Case study: The biggest mistakes in sports
Every sport lover wants a game to be error free and the decisions taken by the officials must be flawless. But in many cases, the situation has not been desirable. One such controversy was seen during India’s tour of Australia in 2008, when these two teams locked horns on Day 5 of the second test at Sydney on January 6, 2008.
Former Indian skipper Sourav Ganguly had just scored his 2nd consecutive half century in the match and was looking dangerous for the mighty Australians. India needed 200 more runs to win the test, which seemed possible with Ganguly and Rahul (Dravid) on the crease. Brett Lee was in charge with the ball and he was running fiercely towards the in-form batsman. He delivered at nearly 145 kilometres per hour, which kissed the edge of Ganguly’s blade, only to reach the second slip. The Australian vice-captain Michael Clarke was fielding in the slip position who made an excellent effort to reach towards the ball and complete the catch.
The umpire was not sure whether the batsman was out or not, so he asked Ricky Ponting- the Australian captain at that time, about the result. Ponting confirmed that the ball carried to the slip and Clarke did no mistake to take the catch. As a result, the umpire Mark Benson declared Ganguly out. Nevertheless, India’s most successful test captain was sure that he was not out and later the TV replay showed that the ball actually dropped on the ground before Clarke caught it. Later in that innings, three of the other Indian batsmen including Dravid, Tendulkar and VVS Laxman, also fell prey to the wrong judgements by the umpires. India was finally all out for 210 and lost the match by 122 runs.
The hand of God
Wrong decisions are not familiar in only cricket, but are also witnessed in other sports as well. Can you recall the event when the Argentinean football legend Diego Maradona scored a goal using his hand? The referees were unable to detect the handball even when the TV camera captured the event. This goal destroyed England’s World Cup dreams of 1986 and allowed Argentina to proceed to the next round. Later that week, Argentina went on to lift the FIFA Football World Cup for the second time.
Technology at the rescue
Thankfully, we are living in an era when technology is more advanced than ever. The advent of big data certification courses have proved to be an important event for various sectors, as more professionals are entering the field of business intelligence in order to make impactful decisions that can change the world. More companies are coming forward to contribute to the welfare of the world of sports. We hope that innovations are made in order to transform each professional sport into an excellent campaign.
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If you have ever used a spreadsheet program then you are well-acquainted with the frustrations produced by entering one thing and having it auto formatted into something else. If such formatting errors go unnoticed then it will definitely lead to serious repercussions when it comes to company finances in the commercial scenario, but when it comes to the serious sciences like genetics their outcomes will be catastrophic.
Recently a study published in the journal of Genome Biology stated that 19.6 percent i.e. roughly 1 in 5 of all genetics papers published that contained spreadsheets had such errors. The main reason behind the problem is the way how genomic names are written. To consider an example, the gene called – “Membrane-Associated Ring Finger (C3HC4) 1, E3 Ubiquitin Protein Ligase” is written as MARCH1 in shorthand. But in Excel due to its default settings, it is automatically converted into 1-Mar or some other calendar date format.
Similarly when scientists enter genetic ID numbers they get converted into floating point numbers like – 2310009E13 became 2.31E+13. And as per Popular Mechanics, it is not possible for the scientists to completely reformat their excel files. So, instead they choose to reformat a blank document and then re-enter their data cell by cell. However, fortunately these errors do not have any effects on the paper’s original findings, but they may pose a serious problem for scientists who would want to replicate the study.
Here is some more insight on the issue:
Recently in a convention for geneticists, the speaker began with the snarky statement that – if you thought autocorrect was a bummer for your Whatsapp texts then you should talk to a geneticist instead. While automated features and productivity tweaks are supposed to save people time and work, they are often times counterproductive as they insert errors into our substantial pieces of work. And sadly, that is the case for an alarming number of academic papers in genetics.
Many academic papers come with supplemental files complete with charts, table and other tabular data and ideally such files are there to support the data and other aspects of the research. They are also useful for fellow researchers to take the work further. While all things remain fine in an ideal scenario, but some automated features in Excel some important data such as scientific names, floating point numbers and dates ends up getting reformatted and causes much too trouble for the scientific community causing havoc confusion. This problem with automatic reformatting occurs all the time and scientists had found the issue back in 2004 and the problem has still persisted since then.
An extensive research has been conducted by Mark Ziemann, Yotam Eren and Assam El-Osta on gene name conversion revealed that about 20 percent of all papers with supplemental spreadsheets have such errors appearing in them. The researchers took a note of more than 35,000 supplemental Excel files attached to such research documents related to genetic studies. They employed automated software to search and filter anything that resembled lists of genes and narrowed the field to about 3597 papers with several supplemental files. Then they went on to screen for the 10 most common false positive cases and discovered them in files attached to some 704 publishing houses who have published such papers. That is 19.6 percent of all the research papers they screened.
So, while many of us have been the victim of autocorrect changing the meaning of our text messages, some with hilarious results but in the case of genetic studies this matter is of no big laughs. These papers are assets for the scientific community and are often used by new generations of researchers to further study the matters. But having such massive errors on the papers can definitely slow things down and create problems for science to advance.
Science has already seen several wasted years in the world due to human intervention with obstacles to free thinking and questioning in the past riding on government or authoritarian censorship eating away people’s ideas of genius.
To further worsen the situation, there is no way we can turn off this autocorrect feature in Microsoft Excel permanently. Fortunately, researchers have discovered that Google Sheets does not perform such automated correctional functions, and if people copied such content from Google Sheets into other forms of spreadsheet programs then the formatting of these data were preserved.
So, until the prominent spreadsheet software manufacturers can figure out a way to offer people with the feature to switch off such autocorrect functions, it will probably fall in the hands of some young, unfortunate research assistant to double check this massive amount of data and correct the lists of gene names.
To learn more about common application of MS Excel and some nifty tricks for spreadsheet software take up an advanced Excel course in Gurgaon from DexLab Analytics, the premiere analytics training institute in India.
Even if you are not a data scientist yet, but there is still data surrounding you and engulfing you in a cloud of structured, specified and targeted data. Data that you use every day on a regular basis and data that actually shapes up your daily routines of commute to work, gym or entertainment. It is like the omnipresent atmosphere that we often take for granted. Why do we say that?
Here is an extract from the life of a non-technical executive of our team, after reading this many of you may feel that this somewhat similar to your story as well.
On an ordinary day, our aforesaid employee gets up in the morning at the ring of his alarm and remembers that his flight will leave at 5 o’ clock that morning. Then he looks at his smart phone and checks the updates on his flight. The flight is on time and the security checks are moving unperturbed. Then he swipes around some more on his smart phone to see if the traffic situation is on his side on this day. He soon finds the traffic is light unlike most other days and decides to cut his commute time very tight expecting himself to reach the airport within 15 minutes. So, he concludes there is ample time for him to leave for the airport at 4:00 am and feeling a sense of confidence about his decision as he made an informed choice so, the chances of things going wrong are low.
Then after his daily ablutions he prepares to set out and opens his Ola/Uber app on phone to call a cab. The app immediately responds with the information that the driver is 2 minutes away. Almost instantaneously the cabbie calls him to understand the precise location of his house and concurs that he will be couple of minutes to get there.
Soon after boarding the cab, our friend opens his health app and connects it via Bluetooth to his smart watch. He notes with a scorn that he is not getting enough exercise and that he only slept 5 hours of deep sleep last night. Then while sitting around being bored in the cab he opens the new Microsoft app that uses your phone camera to look at the picture and guess the age of the face. With further disappointment and in an uncomplimentary way the app gives him a number that is 7 years more than his actual age! But still our executive friend here feels happy as this is a good start of a day. Firstly, because he had the power of data to make educated decisions about some very simple yet troublesome things and two because he got a cab fairly fast.
Now this story may seem like a pretentious rant of pseudo-first world problems, but our point is completely different than the luxurious facilities available to modern urban smart phone owning working class. Our point is to emphasize how almost unknowingly we have let in data into our lives, the myth (and/or fact) of choice is real and we are using it unknowingly while adding and accessing the omnipresent phenomenon of – Big Data.
Yes, Big Data did not just come to office one day and sat in a cabin labelled as “Big Data at work”. This is like electricity a utility that changes our life and influences our decision making ability. Still unconvinced? Then we ask you to conduct a simple survey among your friends. Ask around to know how many people you know buy over-expensing, sub-par quality products without going through the ratings or reviews. If you hadn’t realized it yet, this is what you would like to label as “Big Data at work”.
Thus, in closing thoughts Big Data analytics is the fundamental ability that enables capabilities to people which will effect and transform our daily lives forevermore and what we see today is evidently just the tip of the ice-berg.
So, start your Big Data certification in Pune today, with DexLab Analytics.
Dexlab Analytics presents a handy installation guide to all aspiring data analysts to test their hands on Hadoop ecosystems. It only works in a Linux environment and hence, can be tricky to handle. This step-by-step guide will help you through to get this useful software installed in your computer and to start making sense of all the chaos surrounding data.
In India the hottest job locations for a data analyst position according to our pay-scale and job scenario survey are – Gurgaon, Mumbai and Bangalore. For more details on payment packages on offer for various data analysis positions view our infographic with numbers based on industry-based survey.
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Huge amounts of data are being generated daily. However traditional systems of managing
data fall short when it comes to analyze such huge datasets. The answer to this riddle lies in Big Data. According to Data Scientists working for IBM, Big Data may be broken up into four dimensions and this presentation brings forth to you some startling and astonishing figures concerning each.
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NewVantage Partners have over the course of the last four years which has found that Big Data has quickly become the part and parcel of the day to day activities of most of Fortune 1000 companies.
The opinion of the crucial decision makers within organizations was taken into account. Financial led the way in investing in Big Date infrastructure. Also of note is the fact in the field of life sciences too usage of big data is increasing by the day.
Other findings of the survey are:
- In 2012 just a small group of 5% of firms had in place a system of Big Data analytics which in 2015 rose to 63%.
- In 2012 only 24% of firms claimed to have expectations to invest $10 million or more in Big Data by the year 2017 which rose to 63% in the year 2015.
- At present a majority of 54% of companies have in place a Chief Data Officer which rose from 12% in the year 2012.
- In 2012 only 21% of firms reported that their firms held Big Data to be critically important which rose to 70% in the last year.
During the starting year of the survey the executives of organizations were struggling to come in terms to properly perceive the impact and opportunity that Big Data might potentially hold. But today it has become the standard norm for corporate organizations and the focus is rapidly shifting to results produces and the business capabilities enabled by the same.
There is also a need to develop the appropriate metrics. While indeed the majority of Fortune 1000 companies’ implemented capabilities in Big Data, few have demonstrated the business value derived from the investments over time. Organizations with responsible executives for data who report to a Chief Financial Officer are often more likely to have developed financial measurements that are precise.
Innovation remains a source of much promise in the field of Big Data and innovation opportunities need to be identified. There is a dearth of success stories in innovation in things that are enabled by Big Data. Till now its success has largely been limited to savings on cost of operations or being able to analyze data sets that are larger or more diverse. Innovation in Big Data led applications need to be funded more and its practitioners need to exemplify imagination as well as boldness.
Businesses and the analytics industry needs to prepare for both business and cultural change as a new generations take their place in the workplace who have grown up on tools Like R Programming and Hadoop. There is great need for organizations to realize that Big Data is more about cultural change rather than solely technical change.
If you too want a piece of the bigger pie of Big Data as a practitioner, then you should try to train yourself for a promising career through proper Big Data Courses in Delhi as conducted by a leading institute like DexLab Analytics.
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If you still are unaware of the power of data analytics even after the success of the movie Moneyball, then you might as well be living under a rock. Yes the book by Michael Lewis transformed the way sports administrators can identify low-performing players by using data analysis rather than the conventional techniques. This was the first time something as analytical and core-academic was used in the field of sports and that too for something as substantial as selecting players.
But today data analysis is used in almost every aspect of our lives than just the way we like our favorite teams in popular sports. The basic idea behind statistical analysis is to determine meaningful patterns within a given set of data. This discipline is slowly edging its way into every conceivable business avenue these days whether big or small. The main elements in this field are data (that are available easily these days) and highly powerful computer programs that are used to analyze it and draw valuable insights from it. This field has completely changed the way we perceive information and make decisions. Historically these analysis tools have helped companies take decisions on how they should position themselves in new market ventures as well as place their new products.
For instance, ecommerce start-ups that house some of the smartest minds in the business are using expert statisticians to analyze Big Data with state-of-the-art data analysis tools to generate useful deductions from otherwise dull numbers and decimal points. Whether it is the heavy shifters in the logistics market or simply consumer durables and other fast selling products, companies are using data analysis to predict trends, deduce pricing patterns and are leveraging themselves with a revolutionary use of data analysis tools. Also in the sports world data analysis is behind big decisions like who to draft in a team to what should be the in-game strategy to marketing budget. Movie studios also employ analysis tools to calculate box-office generations and to even predict which movies will be a hit and which ones will be a flop.
Therefore, it is highly unlikely that data analytics would not make its way into the field that generates the highest number of data i.e. finances. The worlds of finances are all about numbers and so data analysis is an integral tool that has been used as an operational instrument in this field for a long-time now. While data analysis has been used in the financial world for several years globally, it is still fairly new to the Indian markets. Until now Indian markets only used data analysis tools in brokerage firms for slicing orders and for minimizing the impact cost of sales and purchases. But data analysis can also be used for mitigating frauds in money laundering, risk analysis and management and for rogue trading.
But currently the stock market is also on the verge of boarding the Big Data analysis train which is known as ‘Quants’ amongst the industry insiders. The basis of decision making in the stock trading market holding the hands of Data Analysis is to choose on the basis of history repeats itself theory. So the analysis tools are used more for technical analysis. The trend of analyzing data on the verge of the result season is slowly transforming as realizing valuable insights and patterns in data has somewhat a knee-jerk effect for the analysts and experts associated with the field. Hence, it is understandable that data analysis is making its way into the core practices of how we trade finances. So, it is understandable that the new key player in the market is data analysis or in other words also known as power computing. As the market experts have realized that using data analysis tools with precision will only increase their trading capabilities while enriching their funds in the process, so it is highly recommended for market participants to join the coveted team of data analysts to harness the true potentials of data.
In India the leading data analyst training institute in Delhi, NCR is DexLab Analytics which offers industry-oriented training courses in data analysis, Big Data Hadoop and other statistical analysis tools used in the market currently.
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The best way to begin speaking about how interesting the past year was for Big Data is to say that – there will be more.
By more we mean that there will be more data, thus, there will be more data scientists, data analysts, more cloud analytics, more mobile analytics and data discovery. So, it is evident that Big Data will only get Bigger this New Year on 2016 with more data to visualize.
But if you are new in the world of Big Data Hadoop courses and are only beginning to acquaint yourself with Big Data, here are a few special trends of 2015 that you must keep a close eye on, this year.
You can make magic:
The technologies we use today are nothing short of sheer magic that, we could only imagine in our wildest dreams even a few years ago. But in the field of Big Data Hadoop and other data analytics tools, nothing such amazing has been noticed yet. We only know of tools and features that get the job done with some difficulty, but nothing that gives us data magic on our very fingertips. Thus, the world of data science still requires shepherds who would oversee and micromanage each and every step of data analysis. We still need people to manage aspects like – where to find data, how it should be stored, how it should be stored and most important how to analyze it and what conclusions to draw from the analysis.
Experts suggest that all this would soon change with advanced technologies being incorporated into the world of analysis to further automate the process. Things like machine learning with other advanced analytics tools are being applied to data science to further upgrade the process.
Insiders now want advanced tools that help them take care of the trivialities fast and get to analysis sooner with facilities like – simply pointing them at the data and allowing the algorithms to figure out things like how to join the data, propose complementary data, and cleanse it and to optimize it to determine it should be stored. Thus, 2016 will see more developments in this aspect of advanced automation.
Make way for multi-polar analytics:
Nowadays the layer cake approach of model analytics is slowly becoming obsolete and a practice that was thought of as the best – with external data feeding data marts and warehousing data with Bi features, as the final touch is fast being replaced with a multilayered or multi-polar approach. So, this New Year we will gain a better idea of how we can get better results from realistic yet complex data analysis centres.
Data privacy laws:
Currently there is a huge room for development in the world of data science contrary to the rate of development in the technologies. Already there have been reports of serious abuses with important data and we expect that 2k16 will see some strict actions in encryption practices in data.
In conclusion, 2016 will be a wonderful year for data analytics with increased adaptability.
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Enterprises look forward to this analysis as a source of valuable insights the operations related to their business as well as finding correlations within revenue and the activity conducted in the field of sales and marketing.
Frameworks like Hadoop are not only open source but also make data storage more effective both in terms of analytics tools as well as costs associated with the storage of data.
Situation Set To Change
But the prevailing situation is in for some serious change as large data set analysis undergoes a paradigm shift from what happened or is happening to what will happen. Machine learning, the concept of cloud computing as well as technologies that work in-memory all are contributing to this shift.
This shift is termed predictive analysis and promises to be the next big thing in the exciting world of Big Data. With it, Enterprises shall no more be confined to insights gained through analysis of data and react according to it but will be able make pretty accurate forecast of the state of their business a month, week or hours down the line through an effective combination of historical, third party and real time data.
This in turn will allow prompt action on potential problems like failure of machinery or depletion of stock and also to cash in on euphoric or depressed moods common after sporting events. However the focus should be on action than mere forecasts.
The Impact on CRM
CRM or customer relationship management software is a key area where predictive analytics can prove to be of invaluable help. Here predictive analysis will help marketers and sales people to be aware of the impact that their activity will potentially have and also to be able to provide content and pitches that are more personalized. This option is far better than simply to rely on the historical data about interactions made previously.
Inside Sales is a IT specialist in analytics and machine-learning and is one of the companies that is making use of this concept by not limiting itself to analytics but extending to the sphere of predictive analytics with a service based in the cloud.
Their system takes into account data from CRM platforms like Microsoft Dynamics and Salesforce and then proceeds to analyse the same against sales interactions that are suitably anonymized and are in tune of over 100 billion in number from the breadth of its worldwide customer base.
Why It Is Superior
This results in the predictive analytics that is both more effectively enhanced and expanded. This is due to the fact that the data possessed by the company is compared to an aggregated data mass.
When put into practice a company is able to figure out its performance vis-à-vis global and regional trends in sales in this particular sector and to find out key influences like economic factors, current affairs and maybe even the weather on its sales.
You have to know clients as people if you need to win them and then you’ll make more intelligent choices about their needs and practices.
Big Data training is essential for creating a workforce that can suitably match the demands of Big Data and its associated technologies and processes.
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