With resonating hits, like Bird Box and Bandersnatch, Netflix is revolutionizing the entertainment industry – all with the power of big data and predictive analytics.
Big Data Analytics is the heart and soul of Netflix, says the Wall Street Journal. Not only the company relies on big data to optimize its video streaming quality but also to tap into customer entertainment preferences and content viewing pattern. This eventually helps Netflix target its subscribers with content and offers on shows they prefer watching.
Committed to Data, Particularly User Data
With nearly 130 million subscribers, Netflix needs to collect, manage and analyze colossal amounts of data – all for the sole purpose of enhancing user experience. Since its inception days of being a mere DVD distributor, Netflix has always been obsessed about user data. Even then, the company had an adequate reservoir of user data and a robust recommendation system. However, it was only after the launch of its incredible streaming service that Netflix took the game of data analytics to an altogether different level.
In fact, Netflix invested $1 million in a cutting-edge developer company for coming up with an algorithm that increased the accuracy of their already-existing recommendation engine by almost 10%. For this, Netflix can now save $1 billion annually from customer retention.
Netflix Already Knows What You Going to Watch Next
Yes, Netflix is a powerhouse of user behavior information. The content streaming giant knows your viewing habits better than you – courtesy pure statistics, preferably predictive analytics. This is one of the major strengths of Netflix – the way it analyzes data, adjusts algorithms and optimizes video streaming experience is simply incredible.
However, nothing great comes easy. Close monitoring of user viewing habits is essential. Right from how much time each user spends on picking movies to the number of times he/she watches a particular show, each and every data is extremely important. Moreover, conventional calculus helps Netflix in understanding its user behavior trends and necessarily provides them with appropriate customized content.
As closing thoughts, Netflix is a clear-cut answer to how technological advancement has influenced human creativity beyond levels. Powered by big data and predictive analytics, Netflix has surely debunked several lame theories on content preference and customer viewing habits. So, if you are interested in big data Hadoop training in Delhi, this is the time to act upon. With DexLab Analytics by your side, you can definitely give wings to your dreams, specifically data dreams.
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Big data is the backbone of modern businesses. All their decisions are data driven. Firstly, information is aggregated from various sources, like customer viewing pattern and purchasing behavior. After this, the data is analyzed and then actionable insights are generated. Nowadays, most companies rely on some type of business intelligence tool. All in all, information collection is increasing exponentially.
However, in many cases the desire for information has gone too far. The recent scandal involving Facebook and Cambridge Analytica stands as an example. It has left people very insecure about their online activities. Fears regarding violation of privacy are rising; people are worried that their data is being monitored constantly and even used without their awareness. Naturally, everyone is pushing for improved data protection. And we’re seeing the results too – General Data Protection Regulation (GDPR) in EU and the toughening of US Data Regulations is only the beginning.
Although data organization and compliance have always been the foundation of IT’s sphere of activity, still businesses are lagging behind in utilizing big data in remote IT support. They have started using big data to enhance their services only very recently.
Advantages of data-directed remote IT support
The IT landscape has undergone a drastic change owing to the rapid advancement of technology. The rate at which devices and software packages are multiplying, Desktop Management is turning out to be a nightmarish task. Big data can help IT departments manage this situation better.
Managing complexity and IT compliance
The key reasons behind maximum number of data breaches are user errors and missing patches. Big data is very useful in verifying if endpoints are on conformity with IT policies, which in turn can help prevent such vulnerabilities and keep a check on networks.
Troubleshooting and minimizing time-to-resolution
Data can be utilized to develop a holistic picture of network endpoints, ensuring the helpdesk process is more competent. By offering deeper insight into networks, big data allows technicians to locate root causes behind ongoing issues instead of focusing on recurring symptoms. The direct effect of this is an increase in first-call-resolution. It also helps technicians to better diagnose user problems.
Better end-user experience
Having in-depth knowledge about all the devices of a network means that technicians don’t have to control an end-user’s system to solve the issue. Also, this enables the user to continue working uninterrupted while the technician takes care of the problem from behind-the-scene. Thus, IT can offer a remedy even before the user recognizes there’s a problem. For example, a team engaged in collection of network data may notice that few devices need to be updated, which they can perform remotely.
Better personalization without damaging control
IT teams have always found it difficult to manage provisioning models, like BYOD (bring your own device) and COPE (corporate owned, personally enabled). But with the help of big data, IT teams can divide end users based on their job roles and also support the various provisioning models without compromising with control. Moreover, they constantly receive feedback, allowing them keep to a check on any form of abuse, unwanted activities and any changes in the configuration of a system.
Concluding:
In short, the organization as a whole benefits from data-directed remote support. IT departments can improve on their delivery service as well as enhance end-user experience. It gives users more flexibility, but doesn’t hamper security of IT teams. Hence, in this age of digital revolution, data-driven remote support can be a powerful weapon to improve a company’s performance.
Knowing how to handle big data is the key to success in all fields of work. That being said, candidates seeking excellent Big Data Hadoop training in Gurgaon should get in touch with DexLab Analytics right away! This big data training center in Delhi NCR offer courses with comprehensive syllabus focused on practical training and delivered by professionals with excellent domain experience.
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Data is indispensable, especially for modern business houses. Every day, more and more businesses are embracing digital technology and producing massive piles of data within their supply chain networks. But of course, data without the proper tools is useless; the emergence of big data revolution has made it essential for business honchos to invest in robust technologies that facilitate big data analytics, and for good reasons.
Quality Vs Quantity
The overwhelming volumes of data exceed the ability to analyze that data in a majority of organizations. This is why many supply chains find it difficult to gather and make sense of the voluptuous amount of information available across multiple sources, processes and siloed systems. As a result, they struggle with reduced visibility into the processes and enhanced exposure to cost disruptions and risk.
To tackle such a situation, supply chains need to adopt comprehensive advanced analytics, employing cognitive technologies, which ensure improved visibility throughout their enterprises. An initiative like this will win these enterprises a competitive edge over those who don’t.
Predictive Analytics
A striking combination of AI, location intelligence and machine learning is wreaking havoc in the data analytics industry. It is helping organizations collect, store and analyze huge volumes of data and run cutting edge analytics programs. One of the finest examples is found in drone imagery across seagrass sites.
Thanks to predictive analytics and spatial analysis, professionals can now realize their expected revenue goals and costs from a retail location that is yet to come up. Subject to their business objectives, consultants can even observe and compare numerous potential retail sites, decrypting their expected sales and ascertain the best possible location. Also, location intelligence helps evaluate data, regarding demographics, proximity to other identical stores, traffic patterns and more, and determine the best location of the new proposed site.
The Future of Supply Chain
Talking from a logistic point of view, AI tools are phenomenal – IoT sensors are being ingested with raw data with their aid and then these sensors are combined with location intelligence to formulate new types of services that actually help meet increasing customer demands and expectations. To prove this, we have a whip-smart AI program, which can easily pinpoint the impassable roads by using hundreds and thousands of GPS points traceable from an organization’s pool of delivery vans. As soon as this data is updated, route planners along with the drivers can definitely avoid the immoderate missteps leading to better efficiency and performance of the company.
Moreover, many logistics companies are today better equipped to develop interesting 3D Models highlighting their assets and operations to run better simulations and carry a 360-degree analysis. These kinds of models are of high importance in the domain of supply chains. After all, it is here that you have to deal with the intricate interplay of processes and assets.
Conclusion
Since the advent of digital transformation, organizations face the growing urge to derive even more from their big data. As a result, they end up investing more on advanced analytics, local intelligence and AI across several supply chain verticals. They make such strategic investments to deliver efficient service across the supply chains, triggering higher productivity and better customer experience.
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Almost 44 million people across the globe suffer from Alzheimer’s disease. The cost of the treatment amounts to approximately one percent of the global GDP. Despite cutting-edge developments in medicine and robust technology upgrades, prior detection of neurodegenerative disorder, such as Alzheimer’s disease remains an upfront challenge. However, a breed of Indian researchers has assayed to apply big data analytics to look for early signs of the Alzheimer’s in the patients.
The whip-smart researchers from the NBRC (National Brain Research Centre), Manesar have come up with a fierce big data analytics framework that will implement non-invasive imaging and other test data to detect diagnostic biomarkers in the early stages of Alzheimer’s.
The Hadoop-powered data framework integrates data from brain scans in the format of non-invasive tests – magnetic resonance spectroscopy (MRS), magnetic resonance imaging (MRI) and neuropsychological test results – by employing machine learning, data mining and statistical modeling algorithms, respectively.
The framework is designed to address the big three Vs – Variety, Volume and Velocity. The brain scans conducted using MRS or MRI yields vast amounts of data that is impossible to study manually or analyze data of multiple patients to determine if any pattern is emerging. As a result, machine learning is the key. It boosts the process, says Dr Pravat Kumar Mandal, a chief scientist of the research team.
The researchers are found using data about diverse aspects of the brain – neurochemical, structural and behavioural – accumulated through MRS, MRI and neuropsychological mediums. These attributes are ascertained and classified into collectives for clear diagnosis by doctors and pathologists. The latest framework is regarded as a multi-modalities-based decision framework for early detection of Alzheimer’s, clinicians have noted in their research paper published in journal Frontiers in Neurology. The project has been termed BHARAT and has been dealing with the brain scans of Indians.
The new framework integrates unstructured and structured data, processing, storage, and possesses the ability to analyze volumes and volumes of complex data. For that, it leverages the skills of parallel computing, data organization, scalable data processing and distributed storage techniques, besides machine learning. Its multi-modal nature helps in classifying between healthy old patients with mild cognitive impairment and those suffering from Alzheimer’s.
Other such big data tools for early diagnostics are only based on MRI images of patients. Our model incorporates neurochemical-like antioxidant glutathione depletion analysis from brain hippocampal regions. This data is extremely sensitive and specific. This makes our framework close to the disease process and presents a realistic approach,” says Dr Mandal.
As endnotes, the research team comprises of Dr Mandal, Dr Deepika Shukla, Ankita Sharma and Tripti Goel, and the research is supported by the adept Ministry of Department of Science and Technology. The forecast predicts the number of patients diagnosed with Alzheimer is expected to cross 115 million-mark by 2050. Soon, this degenerative neurological disease will pose a huge burden on the economies of various countries; hence it’s of paramount importance to address the issue now and in the best way possible.
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Highly scalable general-purpose programming language, Scala is a wonder tool for newbie developers as well as seasoned professionals. It provides both object-oriented and functional programming assistance. The key to such wide level popularity of Scala lies in the sudden explosive growth of Apache Spark, which is actually written in Scala – thus making later a powerful programming language best suited for machine learning, data processing and streaming analytics.
Below, we have enumerated top three reasons why you should learn Scala and tame the tides of success:
For Better Coding
The best part is that you would be able to leverage varied functional programming techniques that will stabilize your applications and mitigate challenges that might arise due to unforeseen side effects. Just by shifting from mutable data structures to immutable ones, and from traditional methods to purely functional strategies that have zero effect on their environment, you can stay rest assured that your codes would be more stable, safer and easy to comprehend.
Inarguably, your code would be simple and expressive. If you are already working on languages, such as JavaScript, Python or Ruby, you already know about the power of a simple, short and expressive syntax. Hence, use Scala to shed unnecessary punctuations, explicit types and boilerplate code.
What’s more, your code would support multiple inheritance and myriad capabilities, and would be strongly-typed. Also, in case of any incompatibilities, it would be soon caught even before the code runs. So, developers in both dynamic and statically typed languages should embrace Scala programming language – it ensures safety with performance along with staying as expressive as possible.
To Become a Better Engineer
He who can write short but expressive codes as well as ensure a type-safe and robust-performance application is the man for us! This breed of engineers and developers are considered immensely valuable, they impress us to the core. We suggest take up advanced Scala classes in Delhi NCR and take full advantage of its high-grade functional abilities. Not just learn how to deliver expressive codes but also be productive for your organization and yourself than ever before.
Mastering a new programming language or upgrading skills is always appreciable. And, when it comes to learning a new language, we can’t stop recommending Scala – it will not only shape your viewpoint regarding concepts, like data mutability, higher-order functions and their potential side effects, but also will brush your coding and designing skills.
It Enhances Your Code Proficiency
It’s true, Scala specialization improves your coding abilities by helping you read better, debug better and run codes pretty faster. All this even makes you write codes in no time – thus making you proficient, and happy.
Now, that you are into all-things-coding, it’s imperative to make it interesting and fun. Scala fits the bill perfectly. If you are still wondering whether to imbibe the new-age skill, take a look at our itinerary on advanced Scala Training in Delhi displayed on the website and decide for yourself. The world of data science is evolving at a steadfast rate, and it’s high time you learn this powerful productive language to be on the edge.
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The excitement over big data is beginning to tone down. Technologies like Hadoop, cloud and their variants have brought about some incredible developments in the field of big data, but a blind pursuit of ‘big’ might not be the solution anymore. A lot of money is still being invested to come up with improved infrastructure to process and organize gigantic databases. But the costs sustained in human resources and infrastructure from trying to boost big data activities can actually be avoided for good – because the time has come to shift focus from ‘big data’ to ‘deep data’. It is about time we become more thoughtful and judicious with data collection. Instead of chasing quantity and volume, we need to seek out quality and variety. This will actually yield several long-term benefits.
Big Myths of Big Data
To understand why the transition from ‘big’ to ‘deep’ is essential, let us look into some misconceptions about big data:
All data must be collected and preserved
Better predictive models come from more data
Storing more data doesn’t incur higher cost
More data doesn’t mean higher computational costs
Now the real picture:
The enormity of data from web traffic and IoT still overrules our desire to capture all the data out there. Hence, our approach needs to be smarter. Data must be triaged based on value and some of it needs to be dropped at the point of ingestion.
Same kind of examples being repeated a hundred times doesn’t enhance the precision of a predictive model.
Additional charges related to storing more data doesn’t end with the extra dollars per terabyte of data charged by Amazon Web Services. It also includes charges associated with handling multiple data sources simultaneously and the ‘virtual weight’ of employees using that data. These charges can even be higher than computational and storage costs.
Computational resources needed by AI algorithms can easily surpass an elastic cloud infrastructure. While computational resources increase only linearly, computational needs can increase exponentially, especially if not managed with expertise.
When it comes to big data, people tend to believe ‘more is better’.
Here are 3 main problems with that notion:
Getting more of the same isn’t alwaysuseful: Variety in training examples is highly important while building ML models. This is because the model is trying to understand concept boundaries. For example, when a model is trying to define a ‘retired worker’ with the help of age and occupation, then repeated examples of 35 year old Certified Accountants does little good to the model, more so because none of these people are retired. It is way more useful if examples at the concept boundary of 60 year olds are used to indentify how retirement and occupation are dependent.
Models suffer due to noisy data: If the new data being fed has errors, it will just make the two concepts that an AI is trying to study more unclear. This poor quality data can actually diminish the accuracy of models.
Big data takes away speed: Making a model with a terabyte of data usually takes a thousand times more time than preparing the same model with a gigabyte of data, and after all the time invested the model might fail. So it’s smarter to fail fast and move forward, as data science is majorly about fast experimentation. Instead of using obscure data from faraway corners of a data lake, it’s better to build a model that’s slightly less accurate, but is nimble and valuable for businesses.
How to Improve:
There are a number of things that can be done to move towards a deep data approach:
Compromise between accuracy and execution: Building more accurate models isn’t always the end goal. One must understand the ROI expectations explicitly and achieve a balance between speed and accuracy.
Use random samples for building models: It is always advisable to first work with small samples and then go on to build the final model employing the entire dataset. Using small samples and a powerful random sampling function, you can correctly predict the accuracy of the entire model.
Drop some data: It’s natural to feel overwhelmed trying to incorporate all the data entering from IoT devices. So drop some or a lot of data as it might muddle things up in later stages.
Seek fresh data sources: Constantly search for fresh data opportunities. Large texts, video, audio and image datasets that are ordinary today were nonexistent two decades back. And these have actually enabled notable breakthroughs in AI.
What all get’s better:
Everything will be speedier
Lower infrastructure costs
Complicated problems can be solved
Happier data scientists!
Big data coupled with its technological advancements has really helped sharpen the decision making process of several companies. But what’s needed now is a deep data culture. To make best of powerful tools like AI, we need to be clearer about our data needs.
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In marketing, the analysis of data is a highly established one but the marketers nowadays have a massive amount of public and proprietary data about the preferences, usage, and behavior of a customer. The term ‘big data’ points out to this data explosion and the capability to use the data insights to make informed decisions. Understanding the potential of big data presents various technical challenges but it also needs executive talent devoted to applying the solutions of big data. Today, the marketers are widely embracing big data and are confident in their use of analytics tools and techniques. Let us learn about the ways in which Big data and analytics can improve the marketing efforts of various businesses around the around.
Locating Prospective Customers
Previously, marketers had to frequently make guesses as to which sector of population comes under their ideal market segment but this is no longer the scenario today. The companies can exactly see who is buying and even extract more details about them with the help of big data. The other details include which buttons they generally click while on a website, which websites they visit frequently, and which social media channels they utilize.
Tracking Impact and ROI
Many retailers have introduced loyalty card systems that track the purchases of a customer, but these systems can also track which promotions and incentives are most effective in encouraging a group of customers or a single customer to make another purchase.
Handling Marketing Budgets
Because big data allows companies to optimize and monitor their marketing campaigns for performance, this implies they can allocate their budget for marketing for the highest return-on-investment (ROI).
Personalizing Offers in Real-Time
Marketers can personalize their offers to customers in real time with the combination of big data and machine learning algorithms. Think about the Amazon’s “customers also bought” section or the recommended list of TV shows and movies from Netflix. The organizations can personalize what promotions and products a particular customer views, even down to sending personalized offers and coupons to the mobile phone of a customer when he walks into a physical location. The role of Personalized Merchandising in the ecommerce industry will continue to increase in the years to come.
Improvement in Market Research
Companies can conduct quantitative and qualitative market research much more inexpensively and quickly than ever before. The tools for online survey mean that customer feedback and focus groups are inexpensive and easy to implement, and data analytics make the results easier to take action.
Prediction of Buyer Behavior and Sales
For the past several years, sales teams, in order to rate their hottest leads, have made use of lead scoring. But, with the help of predictive analytics, a model can be generated and it can successfully predict sales and buyer behavior.
Enhanced Content Marketing
Previously, the return-on-investment for a blog post used to be highly difficult to measure. But, with the help of big data and analytics, the marketers can effortlessly analyze which pieces of content are highly effective at moving leads via a sales and marketing funnel. Even a small firm can afford to use tools for implementing content scoring which can highlight the content pieces that are highly responsible for closing sales.
Optimize Customer Engagement
Data can provide more information about your customers which includes who they are, what they want, where they are, how often they purchase on your site, and how, when they prefer to be contacted, and various other major factors. The organizations can also examine how users interact not only with their website, but also their physical store to enhance the experience of the user.
Tracking Competitors
New tools for social monitoring have made it easy to gather and examine data about the competitors and their efforts regarding marketing as well. The organizations that can utilize this data will have a distinct competitive advantage.
Managing Reputation
With the help of big data, organizations can monitor their brand mentions very easily across different social channels and websites to locate unfiltered testimonials, reviews, and opinions about their company and products. The savviest can also utilize social media to offer service to the customers and create a trustworthy brand presence.
Marketing Optimization
It is quite difficult to track direct ROI and impact with traditional advertising. But, big data can help organizations to make optimal marketing buys across various channels and to optimize their marketing efforts continuously through analysis, measurement, and testing.
What is Needed for Big Data?
At this point, talent and leadership are the major things that big data needs. In most of the companies, the marketing teams don’t have the right talent in place to leverage analytics and data. Apart from people who possess analytical skills to understand the capability of big data and where to use it, companies require data scientists who can extract meaningful insights from data and the technologists who can develop include new technologies. Due to this, there is a high demand for experienced analytics talent today.
Big Data Limitations for Marketing
In spite of all the promise, there exist certain limits to the usefulness of big data analytics in its present state. Among them, the major one is the major one is the analytics tools’ and techniques’ complex “black box” nature which makes it hard to trust and interpret the output of the approaches of big data and to assure others of the accuracy and value of the insights generated by the tools. The difficulty of gathering and understanding data also limits the capability of marketing companies to more fully leverage big data. Beyond this, the marketers are identifying many hurdles to expanding their utilization of big data tools and they include lack of sufficient technology investment, the inability of senior team members to leverage big data tools for decision-making, and the lack of credible tools for measuring effectiveness.
Conclusion
Cloud computing is also playing a major role in marketing with the Cloud Marketing process. Cloud Marketing is a process that outlines the efforts of a company to market their services and goods online via integrated digital experiences. Once the data analytics tools become available and accessible to even the smallest businesses, there will be a much higher impact of big data on the marketing sector as there will be much broader utilization of data analytics. This can only be a boon as organizations enhance their marketing and reach their customers in innovative and new ways.
Author’s Bio: Savaram Ravindra was born and raised in Hyderabad, popularly known as the ‘City of Pearls’. He is presently working at Mindmajix.com. His previous professional experience includes Programmer Analyst at Cognizant Technology Solutions. He holds a Masters degree in Nanotechnology from VIT University. He can be contacted at savaramravindra4@gmail.com. Connect with him also on LinkedIn and Twitter.
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As digitization grows in size and width, more and more companies are seeking ways to modify their digital lending services. It would be effective as well as profitable for both borrowers and lenders. And as a topping on the cake, companies resort to Artificial Intelligence and Big Data as they believe they are the future powerhouse of loans.
Originally banks being the lenders make the lending decision based on a loan applicant’s credit score – which is a 3-digit number collected from renowned credit bureaus, like Equifax and Experian. Credit scores are obtained from large piles of data, such as credit history length, payment history and credit line amounts, and are used to decide whether the applicants would be able to repay their debts. They are also good to determine the interest rate of loans.
Low credit score is an indication that you are a risky borrower, which may end up in rejection of your loan application or else you have to pay excessively higher interest rate.
However, according to digital lending platforms, this kind of information isn’t enough – they fail to draw the actual picture of the loan applicant’s credit worthiness. Rather, it is advisable to include hundred other data points in the scrutiny process, and they don’t have to be based on financial interactions alone. Include educational certifications, employment documents, and even you can take help from minor information, like your nap time, website browsing preferences, chatting habits and so on.
The mechanism of Peer-To-Peer Lending
At times, the concept of Big Data is downright challenging – it creates more confusion than clearing things out. Even Artificial Intelligence is included in this, though marketing teams of countless companies are relying on this advanced technology to enhance profitability and efficiency in operations – pundits from the online lending industry believes AI can actually change the way fintech companies perform.
For example, Upstart, a California-based Peer-to-Peer online lending company uses the power of AI to process loans. It implements machine learning algorithms to perform underwriting decisions. Machine Learning possesses the ability to analyze and coordinate large chunks of customer data to draw patterns that would remain unnoticed if done manually through human analysts.
According to Upstart, this process eventually works out well for people with limited credit history, lower income level and young borrowers. The company has also initiated an automation of 25% of its less risky loans to keep future prospects in mind.
Another Chicago-based startup Avant is harnessing machine learning to identify fraud – by comparing customer behavior with the initial available data belonging to normal customers, while singling out outliers. They are now planning to extend their services to brick-and-mortar banking structures that are planning to set their foot in the online lending business.
Today, digital lending is witnessing a steady growth worldwide, and India is not lagging behind. The perks of introducing machine learning and analytics are evident everywhere, so get yourself charged up and ride on the road of analytics. DexLab Analytics offers excellent big data hadoop certification in delhi ncr. Get enrolled today to experience the best!!
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While waiting to board your plane, the last thing you would want to hear is – we regret to inform, your flight has been delayed or worse cancelled, leaving you exasperated at the very hour. Even though your flight takes off on time, while waiting in front of the baggage carousel, you may face some moments of anxiety before your bag arrives on time. Luckily, these distresses are now becoming a thing from the past.
Things are changing, and technology world is evolving drastically. By leveraging Big Data and technology upgrades, aircraft industry has been able to improve their operations and work more smoothly and accurately. In addition, the air travel industry is witnessing several benefits, in terms of revenue and consumer satisfaction.
Here are the ways in which airlines have been using data to derive maximum operational gains everyday:
Smart Maintenance
Wear and tear is common, even the most advanced airplane models equipped with superior technology require time to time maintenance. Owing to this, travelers may experience delays – as per 2014 survey data, mechanical glitches were the second most reason for the majority of flight cancellations and delays. Maintenance takes its toll on airlines potential as the planes need to be grounded for repairing.
With Big Data, airlines can easily track their planes, predict crucial repairs to be done, and provide advice about which parts need to be bought ahead of time and which to keep in reserve on hand for last minute technical issues.
Reducing Jet Fuel Use
It is impossible to predict how much fuel is used onboard for any given route, historically. But Big Data analytics and cloud data storage has made the impossible possible – you can now track how much fuel is being consumed by each airplane, while taking all the factors into consideration. This paves the way for airlines to draw predictions about the amount of fuel required for a trip to how many number of passengers can board at once.
Taking the Boarding Decisions
Remember, airlines lose if they fly with empty seats, so it’s in their best interest to get everyone onboard. With the help of real-time data, airlines can now easily decide whether to wait for a passenger or leave on time so as not to harass other passengers who might catch connecting flights. Smart boarding is now the key, gone are the days when decisions used to be based on instincts. It’s time to enhance efficiency and performance.
Tracking Bags
Travelers who travelled before had to be hopeful about their luggage making it back to them. But now, Big Data revolution and tracking technology has changed a lot of things. Nowadays, airlines ensure its travelers the peace of mind that they will surely receive their luggage as promised. Delta is the first airline that offered tracking data facility for its passengers, using an app format. Customers can easily monitor their bag, reducing the uncertainty revolving luggage arrival.
Flight operations, crew operations, marketing and air cargo are some areas in airlines industry that boast of rich opportunities for Big Data solutions implementation. In our modern economy, competition is at its peak. To make your airfare rates cheaper and save big on jet fuel, shaking hands with Big Data technology is imperative.
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