How did Big Data help Gareth Bale become the most expensive footballer ever?!

The English Premier League is by the far the most well known and richest league in the world. For participating teams, everything is at stake. EPL attracts the best talent from all over the globe and clubs spend astronomical sum to hire players in hopes to improve or maintain their ranking and many a times only to avoid being at the bottom – which would mean relegation to the lower ranked league. Clubs spend big money to boost their team-sheets and many spend close to 94% of their total earnings disbursing players’ salaries.

Spotting a great talent is a huge undertaking for every club. All clubs employ talent spotters who travel around the globe to find the right players at best price. Until recently their technique was pretty old fashioned – spend hours watching lower league matches in hope of finding a rising star that would change their clubs’ fortune. But now these talent spotters are facing a tough competition from new age data analytics firms, such as Prozone and Opta, which collect realms of (massive) data and sell that to Football clubs for a fee. These Performance analysis systems, as they are known in the industry, use digital cameras to track every movement of every player, and this information is then used to produce the most precise player tracking systems available and they log every tackle, pass and goal, typically collecting 2000 or so events per match.

The objective information captured by these tools offer a completely new perspective on a player's performance. Take Gareth Bale, last season’s outstanding EPL player. Many of his talents are obvious and easy to spot: he is strong, fast with the ball, and scores lots of long distance shots. But crunching data suggests other attributes that are equally important. Gareth regularly intercepts opponents’ passes and makes many successful ones himself. The fact that he is good at getting and keeping the ball contributes a lot to his team’s defence. And this helps Tottenham Hotspur, his club, as much as his actual goals do.

Tottenham isn’t the only big club taking making the use of big data to analyze team’s performance. Chelsea, another top EPL club, has data for all the players playing in top 15 leagues across the world. Liverpool has also recruited a data scientist in the last year. Similarly Real Madrid, a top Spanish football club, has access to performance data form all major leagues. And it was only after analyzing Gareth's performance via one of these data analytics tool, real Madrid decided to offer a cool $133 million and add another shining star in their galacticos ensemble.

While big data and data analytics tools will not replace scouts entirely, one thing is for sure – data analytics, and big data in particular, will increasingly play an ever crucial role in identifying raw and hidden talent and will help many clubs save their place in their respective football leagues!!

Are you studying medicine, or are a doctor or physician? Now, you can use Big Data to help you save lives.

Most of the time the focus is on cost-savings by using Big Data. But Big Data is much more than that, it can now help save lives along with money. This idea could revolutionise the healthcare business by offering more relevant information to the healthcare network.

Imagine making real time data available of various forms of tests, or of patient history so that by the time you, an emergency case, arrives at a hospital, all of your medical records are right there and and there are no delays in your physical examination. In so many parts of the world, this could be a real life-saver.

Harnessing big data could help achieve three critical objectives in healthcare:

• Build sustainable healthcare systems
• Collaborate to improve care and outcomes
• Increase access to healthcare

With a growing need for efficient and accessible healthcare, companies and healthcare organisations are starting to invest in applications and analytical tools that help healthcare stakeholders identify value and opportunities. Dell currently places an emphasis on healthcare, they have been working with big data for a while now in areas such as genomics and personalized medicine. Dell's main goal is to help doctors in making the most accurate diagnose as possible in order to perform the best treatment to ill patient. As it can be seen in the following process image, here is an example on how big data comes to play an important role in the near future.

However, do note that this is contingent on the accuracy of data provided to the physicians. This is critical. Any form of inaccuracy can be fatal. While looking for examples on how data can fail healthcare facilities, I found this:
"My younger brother was born under circumstances that caused him to be transferred immediately to a neonatal ICU. In the same unit, another infant had been born with all of his organs outside his body. He had survived surgery and his vital signs were being monitored with a pressure mat in his incubator. On one visit, his father lifted him out of the incubator and noticed the monitor was still registering the baby. He gave the child to the mother, placed a stuffed animal in the incubator, and covered it with the blanket. You can imagine what happened when the father called the nurse to check on the baby and she discovered a stuffed tiger instead."
(Source: Making Data Meaningful, Lucrum, Dec 10 2012)
Imagine if the data a physician was meant to use was completely non-existent in real life, and imagine the consequences!

Despite the various concerns with using Big Data, it is clear about the extremely important role it will play in Healthcare in the near future. If used correctly, it will help physicians and the healthcare sector to reduce uncertainty, hence improving the quality of life patients.

"I don't expect to get yesterdays medicine. If I can help it, I'd like to get tomorrow's medicine". (Elizabeth Edwards)
We strongly believe that big data is tomorrow's medicine.

Big Data in the Retail Industry

Imagine as a business being able to create competitive advantage by using Big Data. Imagine being able to predict the hottest trends for the season in the busiest time of the year. Big data together with prediction models trends can be a very powerful tool to use. Business will be able to predict demand and target customers in a smart way. With this information, business can create value by adding insights for price optimization, having real time inventory data and other key aspects that are relevant to the business.

Big data is here to stay and to help businesses perform deeper analysis on all types of data. It helps finding meaningful patterns and insights in order to create competitive advantage, by being able to provide a smart shopping experience to customers.

Take a quick look to the video on how retailers could use big data in order to create competitive advantage.

http://www.youtube.com/watch?v=aZXVWOAcXjA

Is this plain creepy stalking or a big opportunity that’s still untapped? You decide.

Big Data is everywhere, Big Data is useful, Big Data is awesome. Yeah, yeah we hear you.
Watch this video, and you might just change your mind.

The video caters to any service provider who could potentially use Big Data to assess the effectiveness of his marketing campaign. For example, a cafè has just released a promotion of some sort, or opened a new store at a particular location. Or done both of them together and wants to find out if it has really worked or not.

In comes in the wonderful communications provider who provides all sorts of data to the cafè’s marketing department so that the cafè can examine the effectiveness of their campaign.

Data like – which route does John, your customer, who uses my communications service to tweet about your cafè takes to work? Does he stop by your cafè on the way or is it close to somewhere like a cinema that he had visited? And how many likes, reposts or other social platforms has he used to communicate this?

Wow, I think I’d be quite worried if a random cafè I was a patron of had this much information about me. The communications provider gives the cafè data about the route I take to their cafè? Is that sort of detail necessary? Personally, that comes as close to privacy invasion as anything can.

Then again, we are probably being too skeptical about this too quickly. Perhaps we need to be aware but not worried.
I’d like to quote a fellow, random internet user to conclude:
“I am an optimist, a humanist, and a futurist so I welcome our new digital age and all the good it can do for society. Technology is not the problem. The problem is how we use it.”

Can you trust the data you use? We tell you the truth.

Thus far, we have gone over how Big Data is extremely useful in analytical decision making and the cases in which it can have unwanted consequences.

Surely though, Big Data is not the panacea to all our problems. It comes with its own set of problems, one of the biggest ones being reliability of the data you use to make your decisions. Clearly, if you use the wrong set of data, the outcome is going to be an undesirable one. We will take you through the factors you need to go over before using Big Data for decision making.

Big Data's characteristics are usually described by using the 3Vs:

The above is a popular, ubiquitous 3V framework that is usually used to show the capacity and potential that Big Data can hold in terms of its speed, volume and variety for our decision making processes.

However, what may be the shortcomings of this process?

The decisions we make must be based on truthful data otherwise we will end up wasting our time, money and may have dire consequences. Hence, we put in the next V you need to consider VERACITY.

Example, you may have found the data on social networking sites possibly useful to decide which promotions to use to market your product. But social media data can be quite uncertain and unreliable, one may find it doubtful to project his/her sales based on the data from social media.

We also need to consider VARIABILITY of data. Do not confuse it with Variety, they are completely different. For example, the chocolate cake you order from the cake shop near you will be the same chocolate you order over the next 3 days. However, the chocolate cake may taste different on each of the 3 days. This is variability. In technology terms, it means the data is possibly undstrcutured or keeps changing rapidly. This can be a very worrying proposition for someone who has to make a decision on this sort of data.

The other V of data you want to consider is VALUE. Self-explanatory you might think but people forget this very often. You want to ensure that the data you use creates value for you and you need to ensure this by using data that correlates and is applicable to the business process and business decision you want to make.

Hence, all the data you use may not be 100% accurate. Some amount of it is bound to have some 'noise' because you want the widest sample size possible before you sort them. However, the truth is that you can only trust the data if you pick the right ones and understand completely to what purpose and how you will be analyzing and using the data. We need to able to understand the trade-offs of using certain vs uncertain data.