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Big Data to Cure Alzheimer’s Disease

Big Data to Cure Alzheimer’s Disease

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.

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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.

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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.

 

The blog has been sourced from www.thehindubusinessline.com/news/science/big-data-may-help-get-new-clues-to-alzheimers/article26111803.ece

 

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Big Data: 4 Myths and 4 Methods to Improve It

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.

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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:

  1. All data must be collected and preserved
  2. Better predictive models come from more data
  3. Storing more data doesn’t incur higher cost
  4. More data doesn’t mean higher computational costs

Now the real picture:

  1. 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.
  2. Same kind of examples being repeated a hundred times doesn’t enhance the precision of a predictive model.
  3. 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.
  4. 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:

  1. Getting more of the same isn’t always useful: 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.
  2. 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.
  3. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.

For more trending news on big data, follow DexLab Analytics – the premier big data Hadoop institute in Delhi. Data science knowledge is becoming a necessary weapon to survive in our data-driven society. From basics to advanced level, learn everything through this excellent big data Hadoop training in Delhi.

 

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India and Big Data Analytics: The Statistics and Facts

India and Big Data Analytics: The Statistics and Facts

Data science, big data and analytics industry in India is expected to experience 8X growth hitting $16 billion by 2025 from the current $2 billion, experts say. Out of the terrific annual inflow to the analytics industry, nearly 11% can be ascribed to advanced analytics, data science and predictive analytics and a substantial 11% to big data.

In the next seven years, the Indian analytics industry will expand its horizons further and demand more analytics professionals to join the data bandwagon. Separately, the BI and analytics software market revenue in India will touch Rs 1980 crore in 2018, increasing at a rate of 18% per year. As a result, Indian companies and organizations are shifting their focus from traditional data reporting to augmented analytics tools that will not only enhance the process of data preparation and evaluation but will help predict the future outcomes, successfully.

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Trends in Analytics

Several sectors across the Indian industry of companies and startups have started embracing data analytics – no wonder, the data analytics landscape in India is growing rapidly, so is the revenue generation.

Contemporary, architecture-oriented data analytics tools are the order of the day. Rightfully so, the companies and budding startups are replacing tactical and traditional data analytics programs for more strategic approaches. The current breed of fast followers is even seeking hefty investments in advanced analytical solutions powered by AI, ML and Deep Learning. It would lessen the time taken to market and sharpen analytics offerings. Focused data management is bringing forth a rapid shift to the hybrid and cloud data management scenario – through iPaaS (Integration Platform as a Service) tools. Data lakes and hubs are also emerging here and there. They are in demand for ingesting and administering multi-structured data. Nevertheless, a lack of talent pool will cost the industry immensely. It can be a major deterring factor towards their seamless adoption.

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Statistics of Data Analysis

Geographically speaking, more than 64% of revenue generated from data analytics in India comes from the USA. We are a leading exporter of data analytics to the US, taking figures to as high as $1.7 billion. In the FY18 alone, the revenue generation from the US has increased by 45%. Next, ranks the UK with 9.6% revenue generation. Technically, analytics revenue generation in India has almost doubled from last year – in terms of countries Poland, UAE, New Zealand, Belgium, Romania & Spain. Furthermore, Indian analytics firms are not left far behind in the data game – they contribute 4.7% of analytics revenues to Indian analytics market.

Well, it seems India is doing pretty good in terms of adopting cutting data analytics technology and reaping fetching benefits. If interested in data analytics, don’t stay behind. Reach us at DexLab Analytics and throw your queries right away.

 

The blog has been sourced from ― www.dqindia.com/india-analyzes-big-data-science-analytics-market-india

 

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Most Popular Big Data Hadoop Interview Questions 2018 (with answers)

Most Popular Big Data Hadoop Interview Questions 2018 (with answers)

Hadoop is at the bull’s eye of a mushrooming ecosystem of big data technologies – its open source, and widely used for advanced analytics pursuits, such as predictive analytics, machine learning and data mining, amongst others. Hadoop is defined as a powerful open source distributed processing framework that’s ideal for processing of data and storing data for big data applications, running across clustered systems.

Below, we’ve put together a comprehensive list of interview questions with answers on Big Data Hadoop, focusing on the various aspects of the in-demand skill. For more, take up our intensive big data hadoop training in Gurgaon

What is the role of big data in enhancing business revenue?

Big data analysis aids businesses in increasing their revenues and hitting notes of success. To explain further, let’s take an example, Walmart, one of the top notch retailers in the world uses big data analytics to increase the sales figure through improved predictive analytics tool, better customized recommendations and new set of products curated observing customer preferences and latest trends. Interestingly, it observed up to 15% increase in online sales for $1 billion in incremental revenue. Like Walmart, LinkedIn, JPMorgan Chase, Facebook, Twitter, Bank of America, Pandora, etc. follow suit.

Mention some companies that use Big Data Hadoop.

  • Yahoo
  • Netflix
  • Adobe
  • Spotify
  • Twitter
  • Amazon
  • Facebook
  • Hulu
  • eBay
  • Rubikloud

Highlight the main components of a Hadoop application.

Hadoop has a wide set of technologies that offers unique advantages for solving crucial challenges. Hadoop core components are given below:

  • Hadoop Common
  • HDFS
  • Hadoop MapReduce
  • YARN
  • Pig
  • Hive
  • HBase
  • Apache Flume, Chukwa, Sqoop
  • Thrift, Avaro
  • Ambari, Zookeeper

What do you mean by Hadoop streaming?

Hadoop streaming is an additional utility function that accompanies Hadoop distribution. Hadoop distribution includes a standard application programming interface, which is used to write Map and Reduce jobs in a number of languages, such as Python, Ruby, Perl, etc. Hadoop streaming is this entire process – here, users can develop and run jobs with any type of shell scripts or executable as the Mappers or Reducers.

Specify the port numbers for NameNode, Task Tracker and Job Tracker.

  • NameNode 50070
  • Job Tracker 50030
  • Task Tracker 50060

What are the four V’S in Big Data?

  • Volume – Scale of data
  • Velocity – Analysis of streaming data
  • Variety – Different forms of data
  • Veracity – Uncertainty of data

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Distinguish between structured and unstructured data.

Structured data is referred as the data that can be stored in conventional database systems in the form of rows and columns – data, which is stored partially in traditional database systems, is known as semi-structured data – raw or unorganized data is generally termed as unstructured data.

Example of structured data – online purchase transactions

Example of semi-structured data – data in XML records

Example of unstructured data – Facebook & Twitter updates, web logs, reviews

Hope you found these Hadoop interview questions useful; to gain further insights on Big Data Hadoop, please enroll for our big data hadoop training courses – they are adequate and developed considering latest industry demands. 

 

The blog has been sourced fromwww.dezyre.com/article/top-100-hadoop-interview-questions-and-answers-2018/159

 

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How Google Is Fighting Floods in India with AI

How Google Is Fighting Floods in India with AI

Of late, the search engine giant, Google made a slew of announcements tailor-made for India focusing on new-age technologies, including Artificial Intelligence and Machine Learning. They are to improve response time to natural disasters, such as flood along with address healthcare challenges.

At their recently conducted annual flagship conference Google For India 2018, the search engine giant also revealed the company is eager to use crisis response and SOS alerts to predict natural disasters. Speaking to the context, Rajan Anandan, VP, India and SEA Sales and Operations at Google stated that technology does come to rescue during extraordinary conditions.

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He further added, “India has gone online to rally behind the victims of the Kerala and Karnataka floods. Our Crisis Response team turned on SOS alerts on Google Search in English and Malayalam, and activated Person Finder to help people search for family and friends. Locations of flood relief resources like shelters are being shared on Google Maps. Outside of the tech support, Google.org and Googlers are contributing over $1 million to support relief and recovery efforts. And others are also donating towards the Kerala flood relief on Tez.”

Floods are ravaging; especially in countries India, Bangladesh and China. It’s for them that Google considered it’s high time to devise something to prevent disasters happening in these countries. Thus, the team started seeking ways to implement AI for flood prediction. The recent Kerala flood was an eye-opener. Hundreds have lost their lives and thousands are still living in makeshift relief camps. The numbers say more than 7.8 lakh people are said to be living in these camps across Kerala.

To offer help, Goggle has initiated a steady stream of measures to assist the state. It has activated SOS alerts on Google search, which hooked all the response numbers and emergency resources in languages, English and Malayalam.

Talking about the technology launch, Google Technical Project Manager (TensorFlow) Anitha Vijayakumar was found saying, “We have been doing AI research to forecast and reduce the impact of floods… Floods are the most common disaster on the planet, and with adequate warning, we can greatly reduce the impact of floods. The current modelling systems are only physics-based, and the data is not detailed enough, while Google is using a system that combines physics modelling plus AI learning, and combines that with elevation and satellite map data.”

In addition, “We also activated Person Finder in English and Malayalam, to help people search and track family and friends – on last count, there were 22,000 records in person finder. We also extended this information on Google Maps to aid the rescue efforts,” said Rajan Anandan, Vice President of Google (South East Asia and India).

He further added that Google Tez’s (the notable payment) donation drive has so far raised USD 1.1 million towards Kerala Chief Minister Relief Fund. Also, Googlers and Google.org has donated USD 1 Million for recovery schemes and relief measures.

Now, that you are reading this blog, it means you are interested in the broad scopes of artificial intelligence and power it brings with it. Get enroll in Big Data Hadoop training in Gurgaon; DexLab Analytics offer state of the art Big Data Hadoop certification courses that’ll take you a step closer to fulfilling your dreams.

 

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6 Indian Union Ministries That Are Using Artificial Intelligence

6 Indian Union Ministries That Are Using Artificial Intelligence

It’s no brainer; artificial intelligence has seeped deep into our lives and it has even caught the attention of Indian government. Indian government initiatives are proof of that. The NDA-led BJP government is an admirer of new technology and has given enough importance to AI by setting up an AI Task Force to arm India for the Industrial Revolution 4.0.

In fact, if you follow through a series of events or speeches, you’ll find how frequently our Prime Minister, Narendra Modi projects India and his government to be technologically-driven. In this blog, we’ll share how our top 6 Union Ministries are using Artificial Intelligence on a wider scale for better economy and powerful country presence:

Ministry of Defense

AI Task Force of the Ministry of Defense led by Tata Sons Chairman N Chandrasekaran filed its final report to Defense Minister Nirmala Sitharaman about employing AI for military superiority. The report includes recommendations regarding making India AI-empowered, both in terms of offensive and defensive needs, across naval, aviation, cyber, land, nuclear and biological warfare verticals.

NITI Aayog (the former Planning Commission)

The National Institution for Transforming India recently identified 5 sectors, namely ealthcare, agriculture, education, smart cities and infrastructure, smart mobility and transportation where profound importance will given towards AI implementation to serve societal needs.

Ministry of Information and Broadcasting

Recently, BECIL (Broadcast Engineering Consultants India Limited), functioning under the Ministry of Information and Broadcasting unveiled a tender showing the present government is more likely to take public opinion and media seriously. The government has chalked out a proposal for a respective “technology platform”, which would tap into public emotions by analyzing social media blogs, accounts, posts and even emails to promote nationalism and negate any media bickering by India’s adversaries.

Ministry of Railways

Indian Railways has been in a line of fire for its food catering services. Say thanks to Artificial Intelligence – AI is transforming the way the food is prepared. Not only is it revamping the entire food menu in trains but also promoting a greener environment by going bio-degradable and delivering food in environment-friendly containers.

Ministry of Home Affairs

First of its kind, Intelligence Traffic Management System is going to be installed by the Delhi Police, under the supervision of Home Ministry. This initiative will initiate smart traffic signals, with the help of AI and their first phase is going to be completed by April 2019.

Ministry of External Affairs

With intent to enhance the flow of information between countries, the Ministry of External Affairs had recently conducted a confidential meeting including global AI stalwarts to discuss how to drag attention of Indian Diaspora.

A quick bite: Niti Aayog, under the guidance of CEO Amitabh Kant has been a key stimulator of numerous digital campaigns across the country, including Aadhaar, encompassing biometric programme and India Chain Project.

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The blog has been sourced from – analyticsindiamag.com/7-indian-union-ministries-who-have-embraced-artificial-intelligence-big-time

 

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Cryptojacking: How Businesses Can Protect Systems from This Latest Cyber Threat

Cryptojacking: How Businesses Can Protect Systems from This Latest Cyber Threat

The rising threat of cyber attacks and the sophistication of these crimes have created a frightening security situation all over the world. The cases of data and privacy breach are increasing every day. Both private and public sector are at risk and understandably the average internet user is very paranoid. Cybercriminals keep innovating new ways to take advantage of security vulnerabilities present in systems.

Cryptojacking is one such cyber threat that has targeted countless unsuspected users all around the world. Particularly in India, cryptojacking has become a pressing problem. According to a recent study by Quick Heal Technologies, from January to May 2018, nearly 3 million cryptojacking cases have been reported.

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What is Cryptojacking?

Cryptojacking is a method of hacking into systems and illegally using them to mine cryptocurrency. Malicious scripts are loaded into machines without the knowledge of owners. The group or individual that loads the malicious program reaps the rewards of cryptomining activities, while the owner of the machine isn’t provided any kind of compensation.

There are two types of processes used to carry out cryptojacking attacks:

In the first process, a cryptomining code is installed in the compromised system by means of an infected file.

In the second method, a website or online ad is infected with a JavaScript-based cryptomining script. When users click on this link, the scrip auto-executes itself.

Why is Cryptojacking So Challenging for Businesses?

The malicious script transmits processing power from the compromised machine to the unauthorized cryptocurrency mining. This affects the computer in the following ways:

  • Slows down the system
  • Causes the machine to lag
  • Some applications become completely inaccessible
  • Resource-intensive operations related to cryptomining damage the hardware of an infected system and at times even cause it to crash repeatedly.

Cryptojacking is a serious business hazard. These disruptions result in downtime and IT tickets, which basically cost the business a lot of money. Global businesses lose billions of dollars due to IT downtime. Infected systems consume huge amounts of electricity and hence the operational costs increase significantly. Bottom line, cryptojacking eats away business revenues, which if taken precautions may be avoided.

How to Protect from Cryptojacking Attacks?

The modern cyber-attack landscape evolves every minute. In the face of such dynamism, it is absolutely essential to adopt a multi-layered approach for preserving IT security. The need of the hour is to invest in advanced security solutions. These solutions must include the following features:

Endpoint Security: In order to protect endpoints from cryptojacking a robust Endpoint Security solution with cutting-edge features like behavior based detection and antivirus is necessary.

Web Filtering: Web Filtering includes a set of tools that can be customized to safeguard your business network from suspicious websites. Distrustful websites are blocked and users are prevented from accessing them.

Network Monitoring: This is a tool that is able to detect huge surges in processor activity, which is a well-known symptom of a cryptojacked device. It helps network administrators keep a close eye on data anomalies.

Mobile Device Management (MDM): Business users depend on mobile phones for conveniently carrying out activities. Hence, deploying a robust MDM solution is important for preventing this type of hijacking.

Apart from these, businesses must ensure basic security hygiene, such as installing a web security solution for the safety of visitors on their website and also carry out patching of latest security updates. For example, SecBI has developed an artificial intelligence solution that analyzes network data and identifies cryptojacking threats.

For more blogs on the latest technical innovations, follow the premier big data Hadoop training instituteDexLab Analytics. Do take a look at the course details for big data Hadoop certification in Delhi.

 

Reference: cio.economictimes.indiatimes.com/tech-talk/how-businesses-can-secure-their-systems-from-cryptojacking/3175

 

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How Big Data Analytics Power Profits for the Hospitality Industry

How Big Data Analytics Power Profits for the Hospitality Industry

The hospitality industry is highly dependent on customer satisfaction. And the analysis of big data can help this industry predict customer behavior by understanding their needs and expectations. This in turn enables the hotels and restaurants to provide personalized customer service and retain loyal customers.

Hospitality service provider Airbnb is making the most of the new ‘’mobile first’’ approach where responsive designs are created for the smallest screens. This mode allows customers to engage in Airbnb business through phones. Although big data and its analysis is a large part of the success for this industry, many companies are yet to fully understand the gains associated with big data.

Here are some ways how big data enables the hospitality industry to drive profits:

Take better control of business:

Effectively analyzing big data drastically changes how the business runs. The hotel industry is a data rich sector with massive volumes of web, audio and video content. However, many hotels don’t use their data to its full potential. For instance, hoteliers collect loyalty information but few exploit the data for making business decisions. Through analytical data exploitation, hotels can deepen their understanding of the behavior, needs and expectations of guests and develop better loyalty programs.

Customer segmentation and Targeting:

Hotels must use customer data to provide better customer service as that is essential for ensuring that customers return to avail their services again. Analyzing this data is crucial to segment customers based on booking and travel trends, preferences, chance of responding to promotions, etc. Targeting clients with wrong offers can hinder business growth. Data analysis allows them to retain their best repeat clients by good incentives and promotions. It also allows them to build separate deals for customers who don’t visit them often with the hopes of converting them to loyal customers.

Set best prices for rooms:

Big data analytics is very important for setting competitive hotel prices so that it attracts more guests. Apart from setting the best price for rooms, hospitality-driven businesses can optimize the budget for utilities through analysis of weather data and energy rates.

On-time delivery:

As big data tools become more and more advanced, it shall enable better collection of data from traffic, temperature, weather, route and other sources. This will improve food delivery by providing better estimates of time taken to deliver. Moreover, it shall help restaurants understand how all the aforementioned sources affect the quality of food. Thus, it helps to plan the transportation beforehand and optimizes the utilization of resources.

Menu enhancement:

Using the customer data on food preferences, restaurants can build a customer profile that contains their favorite food and drinks. From the data gathered through feedback forms and online surveys, they can identify the most popular items in their menu and determine whether their menu needs to be improvised or completely reengineered.

Hence, new sources of data and emerging technologies like IoT (Internet of Things) and AI (Artificial Intelligence) enable the hospitality industry to understand the current trends in the market and boost the overall profit of the enterprise.

Companies who are embracing the power of big data are reaping huge profits, and students who are enrolling for big data Hadoop courses are earning big bucks! So, unlock your career with a big data Hadoop certification in Gurgaon. And follow DexLab Analytics for the latest big data related blogs and information.

References:

www.smartdatacollective.com/hospitality-industry-emergence-big-data

www.hiddenbrains.com/blog/big-data-analytics-driving-restaurant-industry-towards-profitable-growth.html

insidebigdata.com/2018/08/03/three-industries-profiting-big-data

 

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Tapping Into Big Data for Better Talent Acquisition

Tapping Into Big Data for Better Talent Acquisition

There are many variables that need to be considered while making hiring decisions. Most importantly, there’s need to fill skill gap. Other factors are candidate behavior and financial aspects of hiring, like cost for training new employees. Big data and analytics help form valuable insights into the job market. Consider the example of IBM acquiring the services of consulting firm Kenexa. It was used to access data of 40 million workers in order to find the personality trait most suitable for a sales job. All kinds of information starting from the workers’ job applications to managerial level was analyzed and it was determined that ’persistence’ is the most valued trait.

Here are some important ways big data helps firms attract promising candidates:

Automate HR Affairs

Talent Acquisition encompasses a wide variety of tasks and when HR teams work in tandem with AI then many day-to-day tasks gets simplified. It helps with tasks like filtering and tracking application status of candidates, getting new hires onboard and making future decisions about employees by analyzing data of previous employees. Data enabled systems saves a lot of time and makes tedious tasks much easier.

Predictive Analytics for Better Hiring Decisions

Hiring professionals need 360 degree information about a particular situation in order to make the best decision possible. They need to analyze everything starting from the human capital requirement in the organization to the economics. Big data enables them to form a clear idea about the skill gaps in the company’s workforce, analyze current trends in the market, follow the financial KPI’s and demographic traits associated with hiring, set the hiring quota and identify the skills and talents to look for in new hires.

Discard ‘’Eleventh Hour’’ Hiring Method

The urgency to fill skill gaps often pressurizes HR professionals to make quick hires, which can be impulsive and not the best. With the help of predictive analytics, these last minute situations can be completely avoided. It allows HR teams to form long-term hiring strategies that align with company goals and also enables them to make timely hires. Using the power of big data, you can be aware of the future needs of your company and job market trends. Hence, it helps eliminate panic situations where you make a hire only to realize later that he/she doesn’t fit the bill.

Social Media for Insights

Big data helps firms attract the right candidates that fit a role. The hard data available on the social media platforms of promising candidates and their search behavior online give organizations crucial information that help them make right decisions. Talent Bin is one of the many employment websites that use information from social media to form insights.

Targeted Job Ads

With the help of analytics, companies can create target groups and rope them in by showing relevant ads. For example, if there’s a financial service provider who enjoys a large talent network interested in marketing on LinkedIn, then they can take this opportunity to post marketing-specific job advertisements. Many potential candidates might find these posts engaging and the company will find the right fit for the job.

Wrapping up, we can say that big data has opened up fresh avenues to make better hires. The influence of big data in every aspect of the modern corporate sector is truly astounding. The smartest candidates are enrolling for big data courses to build skills that sell the most in today’s world of work. For expert-guided big data Hadoop training in Gurgaon, visit DexLab Analytics.

 

Reference: insidebigdata.com/2018/07/20/big-data-talent-acquisition-effective-synergy-make-better-hires

 

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