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6 Mind-Blowing Facts on Big Data Everyone Must Know

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6 Mind-Blowing Facts on Big Data Everyone Must Know


The hot topic in today’s business world is Big Data. The ability to access and analyze the massive amount of data generated every second is crucial for the growth of a business. In this blog, we highlight the rapid growth in data and its significance in business decisions through some incredible statistics.

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  1. The amount of information man created from the dawn of civilization until 2003 is currently created every two days!

    And the instant messages, tweets and pictures you exchange every second contributes to this data. Ex CEO of Google, Eric Schmidt wonders if the world is ready for the big data-driven technological revolution that is imminent.

  1. 5 quintillion bytes of data are generated by internet users on a daily basis.

    If the data generated in a day was burned onto DVDs, the number would be so massive that it could be piled on top of each other to reach the moon twice!

  1. Out of all the data we create, only about 0.5% is analyzed and put to use.

There’s a huge amount of data that remains untapped. For all the Silicon Valley big shots, like Google, LinkedIn and Facebook, the aim is to link big data with personal data and create products that are highly personalized.

  1. 40,000 search queries are processed by Google every second and these add up to 3.5 billion searches each day and 1.2 trillion searches every year globally.

Google was founded in 1998 and back then it was catering to only ten thousand search queries every day. However, you shall be astonished after knowing that since 2006 more than ten thousand search queries have been performed through Google per second!

  1. Big data has the potential to create 6 million jobs in the U.S.

    LinkedIn’s head of data recruiting, Sherry Shah, described the big data job market as being ‘’very hot right now’’.

  1. A 10% increase in the data accessibility for Fortune 1000 companies is likely to increase their income by $65 million!

    And this is exactly why companies care so much about big data.

The job market for big data and analytics looks promising. Especially if you are fresher skilled in big data Hadoop then there’s a lot of scope for you. Compared to the current demand for professionals with Hadoop training, the number of available candidates is low. So, what are you waiting for? Enroll for Hadoop training in Gurgaon and grab amazing discounts on big data certifications.

 

This article has been sourced from:

ediscovery.co/ediscoverydaily/electronic-discovery/date-fun-facts-big-data-ediscovery-trends

 

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Refugee Migration: How Predictive Analytics Coupled with Big Data is Developing Urgent Solutions for Countless Refugees?

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Refugee Migration: How Predictive Analytics Coupled with Big Data is Developing Urgent Solutions for Countless Refugees?

In total, 65 million people are currently displaced or live refugees – owing to the Syrian Civil War. Each day, thousands of refugees are fleeing their homes and seeking asylums in foreign countries. Many countries have opened their borders, countless UN agencies have come forward to help and handle the ongoing global crisis – but how bad is the current situation? What are the chances of working out a satisfactory solution?

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Predictive Analytics is the key. It’s a raw form of statistical science that mines through available data for future prediction of outcomes. Though we agree to the potentials of predictive analytics, we can’t turn a blind eye to the political and financial roadblocks it poses in front of us, which keeps us from addressing the current crisis with same gusto.

For excellent SAS predictive modeling training, DexLab Analytics is your go-to learning destination in Gurgaon. 

The Power of Prediction

Past data helps! They help the algorithms to anticipate the challenges even before they arise. Also migration data… gathered from a plethora of sources, including World Bank data, population censuses, sample surveys, population registers, and other administrative sources. Such treasure troves of data could be groundbreaking, especially for representatives working on forefront of the ongoing crisis. Armed with meaningful data, officials using advanced analytics could chart out most likely locations, where the refugees are about to head next. Spotting the possible signs of influx, government and respective policymakers might reroute the refugees to different locations, where better assistance is possible and expected. This kind of real-time data helps respectable authorities to transfer money and goods to locales that need them the most.

Nevertheless, predictions are not always on-point or don’t lead to the best guesses, all the time, yet in many cases, refugees could benefit – remember refugee crisis is not only a serious humanitarian crisis but also a development issue for countries that accept the asylum seekers. Thus, the authorities should refrain from bottling up hundreds and thousands of refugees from bottling them up in overcrowded camps, without food, water and other basic amenities. And for that, they need adequate data, which could help them make the best possible decision in such situations of distress.

A Hope in Sight

Technical challenges are soaring; if the world is resilient to solve the ongoing international crisis, predictive analytics has to be embraced, but make sure you give adequate importance to data security. Accidental data breaches and releases are happening all around, which could result in triggering targeted violence in specific, highly-populated, vulnerable areas.

Addressing the growing concern, hefty financial investment is the best play. Several private players and multinational organizations, including UN till now have given undue attention but devoted limited resources to tackle the challenge. That needs to be changed now. And fortunately, change is in motion; recently two key players in the humanitarian aid and development area of work signed a partnership to formulate innovative solutions for refugee crisis using far-reaching claws of big data and technology. The striking partnership between the World Bank and United Nations Refugee Agency is the first stepping stone towards improving the quality of data about refugees, prompting an improved smarter assistance for refugees across the globe.

No longer are such initiatives a distant concept; the phenomenal rise of big data hadoop and predictive analytics technology has stepped up the quality and speed of data resulting in tailor-made sophisticated assistance, perfect for refugee crisis. In a nutshell, the new dimension is going to make a lot of difference, and technology is going to be a game-changer in this.

For more interesting blogs and data-related stuffs, follow us on DexLab Analytics. We are a leading SAS Predictive Modelling training institute in Delhi offering high-in demand certification courses. Reach us today!

 

The blog has been sourced from —

sisense.com/blog/refugee-migration-where-are-people-fleeing-from-and-where-are-they-going

mashable.com/2018/04/24/big-data-refugees/#8md_gh7p2iqr

theconversation.com/millions-of-refugees-could-benefit-from-big-data-but-were-not-using-it-86286

 

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How Can Big Data Tools Complement a Data Warehouse?

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How Can Big Data Tools Complement a Data Warehouse?

Every person believes that he/she is above average. Businesses feel the same way about their best asset— data. They want to believe that their big data is above average and perfect for implementing advanced big data tools. But, that’s not the case always.

Do you really need big data tools?

In the data world, big data tools like Hadoop Spark and NoSQL are like freight trains delivering goods. Freight trains are powerful, but they’ve limited routes and a slow start. They are great for delivering goods in bulk regularly. However, if you need a swift delivery, freight train might not be the best choice.

So firs of all, it is important to understand if there’s a big data scenario in your business or not.

A 100 times increase in data velocity, volume or variety indicates that you have a big data situation at hand. For example, if data velocity increases to hundreds of thousands of transactions per hour from thousands of transactions, or if the data sources shoot up from dozens to hundreds, you can safely conclude that your business is dealing with big data.

In such scenarios, you are likely to get frustrated with traditional SQL tools. A complete revamp or moderate tuning of existing big data tools is needed to effectively handle such massive data sets.

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What tools to use?

The tool to be used depends on the task at hand. For main business outcomes like sales, payments, etc., traditional reporting tools employed within the data warehouse architecture are suitable. For secondary business outcomes like following the customer journey in detail, tracking browsing history and monitoring device activity, big data tools within data warehouse are necessary. In a data warehouse these events are aggregated into models that show the summarized business processes.

Incorporating Big Data Tools in Data Warehouse

Consider an alarm company with sensors that are connected though the internet across an entire country. Storing the response of individual sensors in a SQL data warehouse would incur huge expenses, but no value. An alternative storage solution is retaining this information in data lake environments that are cheaper and later aggregating them in a data warehouse. For example, the company could define sensor events that constitute a person locking up a house. A fact table recording departures and arrivals could be stoked up in a data warehouse as an aggregate event.

There are many other use cases. Some are given below:

Sum up and filter IoT data:  A leading bed manufacturing company uses biometric sensors in their range of luxury mattresses. Apache Hadoop could be used to store individual sensor readings and Apache Spark can be employed to amass and filter signals. The aggregated data in data warehouses can be used to create time-trended reports once the boundary metrics are surpassed.

Merge real-time data with past data: Financial institutes need live access to market data. However, they also need to store that data and use it for identifying historical trends in future. Merging these two types of data with tools like Apache Kafka or Amazon Kinesis is important because, with these tools the data can be directly streamed to visualization tools and there’s hardly any delay.

The ultimate goal is to form a balance between the two sides of the data pipeline. While it is important to collect as much raw data about customers as possible, it is equally important to use the right tool for the right job.

To read more blogs on the latest developments in the field of big data, follow DexLab Analytics. We are a premier Hadoop training institute in Gurgaon. To aid your big data dreams, we have started a new admission drive #BigDataIngestion where we offer flat 10% discount to all students interested in our big data Hadoop courses. Enroll now!

 

Reference: https://tdwi.org/articles/2018/07/20/arch-all-5-use-cases-integrating-big-data-tools-with-data-warehouse.aspx

 

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5 Trends Shaping the Future of Data Analytics

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5 Trends Shaping the Future of Data Analytics

Data Analytics is popular. The future of data science and analytics is bright and happening. Terms like ‘artificial intelligence’ and ‘machine learning’ are taking the world by storm.

Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020, says Forbes, a leading business magazine.

 

Last year, at the DataHack Summit Kirk Borne, Principal Data Scientist and Executive Advisor at Booz Allen Hamilton shared some slivers of knowledge in the illuminating field of data science. He believes that the following trends will shape up the world of data analytics, and we can’t agree more.

Dive down to pore over a definitive list – thank us later!

Internet of Things (IoT)

Does IoT ring any bell? Yes, it does, because it’s nothing but evolved wireless networks. The market of this fascinating new breed of tech is expected to grow from $170.57 billion in 2017 to $561.04 billion by 2022 – reasons being advanced analytics and superior data processing techniques.

Artificial Intelligence

An improved version of AI is Augmented Intelligence – instead of replacing human intelligence, this new sophisticated AI program largely focuses on AI’s assistive characteristic, enhancing human intelligence. The word ‘Augmented’ stands for ‘to improve’ and together it reinforces the idea of amalgamating machine intelligence with human conscience to tackle challenges and form relationships.

Augmented Reality

Look forward to better performances and successful models? Data is the weapon of all battles. Augmented Reality is indeed a reality now. The recent launch of Apple ARkit is a pivotal development in bulk manufacturing of AR apps. The power of AR is now in the fingertips of all iPhone users, and the development of Google’s Tango is an added thrust.

Hyper Personalization

#KnowYourCustomer, it has become an indispensable part of today’s retail marketing; the better you know your customers, the higher are the chances of selling a product. Yes, you heard that right. And Google Home and Amazon Echo is boosting the ongoing operations.

Graph Analytics

Mapping relationships across wide volumes of well connected critical data is the essence of graph analytics. It’s an intricate set of analytics tools used for unlocking insightful questions and delivering more accurate results. A few use cases of graph analytics is as follows:

  • Optimizing airline and logistic routes
  • Extensive life science researches
  • Influencer analysis for social network communities
  • Crime detection, including money laundering

 
Advice: Be at the edge of data accumulation – because data is power, and data analytics is the power-device.

Calling all data enthusiasts… DexLab Analytics offers state of the art data analytics training in Gurgaon within affordable budget. Apply now and grab amazing discounts and offers on data analyst course.

 

The article has been sourced from – yourstory.com/2017/12/data-analytics-future-trends

 

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The Future of Humanity Lies in Big Data

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The Future of Humanity Lies in Big Data

The World Economic Forum Annual Meeting 2018 was held in Davos, Switzerland. Here politicians, decision-makers from the world’s largest companies, and thought leaders come together to discuss about pressing global challenges. In this important platform, the opening words of historian, professors and famous author Yuval Harari were these— ‘’ we are probably the last generations of Homo sapiens.’’

He went on to explain that the new entities that humans will eventually evolve into will differ a lot more from the modern man than we did from our predecessors, the Neanderthals. However, the new species won’t be products of natural evolution of human genes, rather the result of humans engineering bodies and brains.

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Harari said that in future, the power will lie in the hands of those who control data. Data is the most important asset in the world and has redefined the prerequisites of power and dominance. Earlier, the ownership of land and subsequently machinery separated humans into aristocrats and commoners, capitalists and workers. However, in the modern age data is the determining asset. This is reflected in the biggest companies of the world. Out of ten of the leading companies in the world, six are tech firms that deal with enormous amounts of data, namely Apple, Microsoft, Amazon, Alphabet, Tencent and Facebook. The fact that these companies are only around two decades old suggests the role big data played in their growth.

Technology has advanced to the extent that data can be used to hack not just computers but also human beings. It takes only two things- data and computing power. Computing power is advancing with enormous speed. Today, the processing powers of mobile phones we use are greater than the best computers from a few decades ago. At the same time, digital information is ever increasing. Humans generate an average of 2.5 million terabytes of data in a day!

The data humans generate is mostly in unstructured form, especially the data that comes from online surveys and social media platforms. However, if analyzed, this data can reveal a lot about the personality of the person generating the data. It is layered with meaning and very open to interpretation. Understandably, analysts are focusing more and more on making sense of this unstructured data.

Hacking the human mind with algorithms

Through machine learning, smart artificial intelligence and deep learning, it is now possible to mine volumes of data and find patterns that earlier went unnoticed to human minds, which are ‘biologically limited’. Right kind of data and the power of computers can be utilized to develop algorithms that know more about people than they do themselves. After all, humans are just biochemical algorithms and the amalgamation of neuroscience and artificial intelligence has enabled the creation of algorithms that help understand the mechanics of human mind better than ever before.

In the words of Harari— ‘’As you surf the internet, as you watch videos or check your social feed, the algorithms will be monitoring your eye movements, your blood pressure, your brain activity, and they will know.’’

To read more blogs on big data, analytics and all the latest trends in these fields, follow DexLab Analytics. We are a leading institute providing Hadoop training in Gurgaon. Do take a look at our big data Hadoop certifications— we are offering flat 10% discount in these courses.

 

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The 8 Leading Big Data Analytics Influencers for 2018

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The 8 Leading Big Data Analytics Influencers for 2018

Big data is one of the most talked about technology topics of the last few years. As big data and analytics keep evolving, it is important for people associated with it to keep themselves updated about the latest developments in this field. However, many find it difficult to be up to date with the latest news and publications.

If you are a big data enthusiast looking for ways to get your hands on the latest data news, then this blog is the ideal read for you. In this article, we list the top 8 big data influencers of 2018. Following these people and their blogs and websites shall keep you informed about all the trending things in big data.

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Kirk Borne

Known as the kirk in the field of analytics, his popularity has been growing over the last couple of years.  From 2016 to 2017, the number of people following him grew by 30 thousand. Currently he’s the principal data scientist at Booz Allen; previously he has worked with NASA for a decade. Kirk was also appointed by the US president to share his knowledge on Data Mining and how to protect oneself from cyber attacks. He has participated in several Ted talks. So, interested candidates should listen to those talks and follow him on Twitter.

Ronald Van Loon

He is an expert on not only big data, but also Business Intelligence and the Internet of Things, and writes articles on these topics so that readers become familiar with these technologies. Ronald writes for important organizations like Dataconomy and DataFloq. He has over hundred thousand followers on Twitter. Currently, he works as a big data educator at Simplelearn.

Hilary Manson

She is a big data professional who manages multiple roles together. Hilary is a data scientist at Accel, Vice president at Cloudera, and a speaker and writer in this field. Back in 2014, she founded a machine learning research company called Fast Forward labs. Clearly, she is a big data analytics influencer that everyone should follow.

Carla Gentry

Currently working in Samtec Inc; she has helped many big shot companies to draw insights from complicated data and increase profits. Carla is a mathematician, an economist, owner of Analytic Solution, a social media ethusiat, and a must-follow expert in this field.

Vincent Granville

Vincent Granville’s thorough understanding of topics like machine learning, BI, data mining, predictive modeling and fraud detection make him one the best influencers of 2018. Data Science Central-the popular online platform for gaining knowledge on big data analytics has been cofounded by Vincent.

Merv Adrian

Presently the Research Vice President at Gartner, he has over 30 years of experience in IT sector. His current work focuses on upcoming Hadoop technologies, data management and data security problems. By following Merv’s blogs and twitter posts, you shall be informed about important industry issues that are sometimes not covered in his Gartner research publications.

Bernard Marr

Bernard has earned a good reputation in the big data and analytics world. He publishes articles on platforms like LinkedIn, Forbes and Huffington Post on a daily basis. Besides being the major speaker and strategic advisor for top companies and the government, he is also a successful business author.

Craig Brown

With over twenty years of experience in this field, he is a renowned technology consultant and subject matter expert. The book Untapped Potential, which explains the path of self-discovery, has been written by Craig.

If you have read the entire article, then one thing is very clear-you are a big data enthusiast! So, why not make your career in the big data analytics industry?

Enroll for big data Hadoop courses in Gurgaon for a firm footing in this field. To read more interesting blogs regularly, follow Dexlab Analytics– a leading big data Hadoop training center in Delhi. Interested candidates can avail flat 10% discount on selected courses at DexLab Analytics.

 

Reference: www.analyticsinsight.net/top-12-big-data-analytics-and-data-science-influencers-in-2018

 

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Top 5 Up-And-Coming Big Data Trends for 2018

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Top 5 Up-And-Coming Big Data Trends for 2018

The big data market is constantly growing and evolving. It is predicted that by 2020 there will be over 400,000 big data jobs in the US alone, but only around 300,000 skilled professionals in the field. The constant evolution of the big data industry makes it quite difficult to predict trends. However, below are some of the trends that are likely to take shape in 2018.

Open source frameworks:

Open source frameworks like Hadoop and Spark are dominating the big data realm for quite some time now and this trend will continue in 2018. The use of Hadoop is increasing by 32.9% every year- according to Forrester forecast reports. Experts say that 2018 will see an increase in the usage of Hadoop and Spark frameworks for better data processing by organizations. As per TDWI Best Practices report, 60% of enterprises aim to have Hadoop clusters functioning in production by end of 2018.

As Hadoop frameworks are becoming more popular, companies are looking for professionals skilled in Hadoop and similar techs so that they can draw valuable insights from real-time data. Owing to these reasons, more and more candidates interested to make a career in this field are going for big data Hadoop training.

Visualization Models:

A survey was conducted with 2800 BI experts in 2017 where they highlighted the importance of data discovery and data visualization. Data discovery isn’t just about understanding, analyzing and discovering patterns in the data, but also about presenting the analysis in a manner that easily conveys the core business insights. Humans find it simpler to process visual patterns. Hence, one of the significant trends of 2018 is development of compelling visualization models for processing big data.

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Streaming success:

Every organization is looking to master streaming analytics- a process where data sets are analyzed while they are still in the path of creation. This removes the problem of having to replicate datasets and provides insights that are up-to-the-second. Some of the limitations of streaming analytics are restricted sizes of datasets and having to deal with delays. However, organizations are working to overcome these limitations by end of 2018.

Dark data challenge

Dark data refers to any kind of data that is yet to be utilized and mainly includes non-digital data recording formats such as paper files, historical records, etc. the volume of data that we generate everyday may be increasing, but most of these data records are in analog form or un-digitized form and aren’t exploited through analytics. However, 2018 will see this dark data enter cloud. Enterprises are coming up with big data solutions that enable the transfer of data from dark environments like mainframes into Hadoop.

Enhanced efficiency of AI and ML:

Artificial intelligence and machine learning technologies are rapidly developing and businesses are gaining from this growth through use cases like fraud detection, pattern recognition, real-time ads and voice recognition. In 2018, machine learning algorithms will go beyond traditional rule-based algorithms. They will become speedier and more precise and enterprises will use these to make more accurate predictions.

These are some of the top big data trends predicted by industry experts. However, owing to the constantly evolving nature of big data, we should brace ourselves for a few surprises too!

Big data is shoving the tech space towards a smarter future and an increasing number of organizations are making big data their top priority. Take advantage of this data-driven age and enroll for big data Hadoop courses in Gurgaon. At DexLab Analytics, industry-experts patiently teach students all the theoretical fundamentals and give them hands-on training. Their guidance ensures that students become aptly skilled to step into the world of work. Interested students can now avail flat 10% discount on big data courses by enrolling for DexLab’s new admission drive #BigDataIngestion.

 

Reference: https://www.analyticsinsight.net/emerging-big-data-trends-2018

 

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Hadoop or Spark: Which Big Data Framework to Choose?

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Hadoop or Spark: Which Big Data Framework to Choose?

Feeling confused?

Of late, Spark has overtaken Hadoop for being the most active open source big data project. Though they have their differences, they both have many common uses.

To begin, they both are incredible big data frameworks. For some years, Hadoop has been leading the open source big data framework clusters but recently highly advanced Spark tends to have captured the market. The latter has become increasingly popular and for all the right reasons. But that is not to say, Hadoop is losing its significance entirely.

They don’t perform exactly the similar tasks. Neither are they mutually exclusive. Though it’s been heard that Spark can work 100X faster than Hadoop in some scenarios, it doesn’t come with its own distributed storage system, which is quite fundamental to big data projects. Distributed storage offers elaborate multi-petabyte dataset storage solution across almost infinite number of computer hard drives. As compared to expensive machinery customization which holds everything in one device, distributed system is cheap as well as scalable, which means as many devices can be added if the network of data set ever grows.

Moreover, Spark doesn’t have its own file system; it cannot organize files in a distributed way without help from third party. This is the reason why several companies think of installing Spark after Hadoop, so that superior analytical applications of Spark can employ data stored using HDFS.

So, what makes Spark win over Hadoop? It’s the SPEED. Spark is a champion of handling a large chunk of its operations ‘in memory’- this reduces a lot of time and effort, indeed. Thanks to MapReduce!

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MapReduce writes of the data right to its physical storage medium after each activity. The main purpose of this was to ensure a fully recovery if something goes wrong – nevertheless, Spark organizes data in Resilient Distributed Datasets, where data can be easily recovered following failure or any kind of mishap.

The main driving factor behind growth of Spark lies in its adept functionality for tackling advanced data processing tasks, including machine learning and real-time stream processing. Real-time processing stands for feeding data into analytical applications the moment it’s seized, and insights are right away directed back to the users through a dashboard to inspire action. This kind of processing is nowadays very much used in big data, thus making Spark enjoy an upper hand against its Hadoop counterpart.

The technology of machine learning is right at the kernel of digital revolution – artificial intelligence and creating far-fetched algorithms is an area of analytics Spark excels at. Its speed and the sound capability to tackle streaming data are the reasons behind. Spark has its own machine learning libraries, known as MLib, while Hadoop needs to collaborate with third-party machine learning library, for example Apache Mahout.

As closing thoughts, though it appears that the two big data frameworks are stiff competitors of each other, yet this is really not the case in the reality. The corporate uses offers both the application services, letting the buyer decide which one they prefer to pick, subject to their functionality and need.

DexLab Analytics Presents #BigDataIngestion

DexLab Analytics Presents #BigDataIngestion

 

The good news is that DexLab offers both Hadoop and Apache Spark Certification Training. What’s more, a recent admission drive is ongoing #BigDataIngestion. Enroll now and enjoy 10% discount on big data certification training courses.

 

The blog originally was published on – www.forbes.com/sites/bernardmarr/2015/06/22/spark-or-hadoop-which-is-the-best-big-data-framework/2/#714061d161d6

 

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Predicting World Cup Winner 2018 with Big Data

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Predicting World Cup Winner 2018 with Big Data

Is there any way to predict who will win World Cup 2018?

Could big data be used to decipher the internal mechanisms of this beautiful game?

How to collect meaningful insights about a team before supporting one?

Data Points

Opta Sports and STATS help predict which teams will perform better. These are the two sports companies that have answers to all the above questions. Their objective is to collect data and interpret it for their clients, mainly sports teams, federations and of course media, always hungry for data insights.

How do they do it? Opta’s marketing manager Peter Deeley shares that for each football match, his company representatives collects as many as 2000 individual data points, mostly focused on ‘on-ball’ actions. Generally, a team of three analysts operates from the company’s data hub in Leeds; they record everything happening on the pitch and analyze the positions on the field where each interaction takes place. The clients receive live data; that’s the reason why Gary Lineker, former England player is able to share information like possession and shots on goal during half time.

The same procedure is followed at Stats.com; Paul Power, a data scientist from Stats.com explains how they don’t rely only on humans for data collection, but on latest computer vision technologies. Though computer vision can be used to log different sorts of data, yet it can never replace human beings altogether. “People are still best because of nuances that computers are not going to be able to understand,” adds Paul.

Who is going to win?

In this section, we’re going to hit the most important question of this season – which team is going to win this time? As far as STATS is concerned, it’s not too eager to publish its predictions this year. The reason being they believe is a very valuable piece of information and by spilling the beans they don’t want to upset their clients.

On the other hand, we do have a prediction from Opta. According to them, veteran World Cup champion Brazil holds the highest chance of taking home the trophy – giving them a 14.2% winning chance. What’s more, Opta also has a soft corner for Germany – thus giving them an 11.4% chance of bringing back the cup once again.

If it’s about prediction and accuracy, we can’t help but mention EA Sports. For the last 3 World Cups, it maintained a track record of predicting the eventual World Cup winner impeccably. Using the encompassing data about the players and team rankings in FIFA 2018, the company representatives ran a simulation of the tournament, in which France came out to be the winner, defeating Germany in the final. As it has already predicted right about Germany and Spain in 2014 and 2010 World Cups, consecutively, this new revelation is a good catch.

So, can big data predict the World Cup winner? We guess yes, somehow.

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The blog has been sourced from – https://www.techradar.com/news/world-cup-2018-predictions-with-big-data-who-is-going-to-win-what-and-when

 

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