The effects of IoT on Big Data

The Internet of Things(IoT) is gradually leading us to interact with “things”. The large amounts of data that many smart devices are going to produce is used to make this happen, and that is going to change the way Big Data is handled. Let us understand how.

Big Data Storage

How much data that will be generated is the first thing appearing when the adoption of IoT keeps increasing. Multiple channels are used to keep such large amounts of data. To create a flexible and scalable method to handle IoT data, Big Data organizations are prepping up to move to the PaaS (Platform as a Service) model.

Big Data Security

It is important to increase security when increasing data. IoT will create a network of varied devices that will also lead to a pooling of varied types of data. IoT Data Security will be a new challenge put across with Big Data Security professionals. If any security loophole happens, the entire network of connected devices will be put at risk of manipulation.

The verification and authentication of devices that are added to the IoT network will become vital. It will become the task of Big Data organizations to create a checkpoint to audit the devices that are added to the network.

Big Data Technologies

IoT is supposed to connect devices over Wi-Fi and Bluetooth. The data passing between the devices will also be sent over these channels and there must be leak-proof technologies implemented to capture this immense amount of data. Protocols must be put in place to offer a controlled mechanism of data receiving and storing. Mosquitto is a very popular protocol and adaption of Hadoop to store the data generated by IoT networks is also in process.

Big Data Analytics

IoT and Big Data are interconnected with each other. IoT is going to generate huge amounts of data that must be analyzed if the IoT networks are going to operate accurately. The networks may generate some redundant data and that is why it becomes important for Big Data organizations to spend their analytics power on the data that is important. So, a new element of data categorization will be added so that the Big Data Analytics tools deliver better performance.

Conclusion

Big Data organizations are going to receive a huge amount of data for analysis by IoT devices. At the moment, Big Data companies are only just becoming capable of handling this immense amount of data in a highly secure manner. The change we are expecting on the Big Data front would be the adoption of flexible and scalable solutions to enhance security, data storing, and data analysis capabilities.

IoT is still new for many people. The wider IoT’s adoption is, the more important that Big Data organizations must prepare to handle various type of data sending from different types of IoT devices.

Source: http://www.dataversity.net/will-internet-things-iot-impact-big-data/

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The contribution of IoT and Edge Computing to Cyber Security

Cybersecurity is concerned by many people, mainly because there are numerous use cases for IoT among which most of the devices do not have traditional IT hardware protocols. Cyber threats are still the problems which many enterprises are not prepared to fight against. Here are they ways how organizations can prepare themselves before the attack of cyber threats.

Are cyber risks invited by IoT?

The Internet of Things(IoT) and the Industrial Internet of Things (IIoT) consist of trillion devices. In the future, homes, power plants, hospitals, labs and more will be empowered by billions of devices. Large data collected by these devices will be transformed, analyzed, and turned into actionable intelligence by servers residing at the edge. Hence, it is wrong in confirming these IoT devices inviting threat calls. They are generating the data required for organizations, and therefore, required tools and techniques have to be adopted by organizations to fight any threat that arises.

IoT is securing its frontiers

Achieving its frontiers is what IoT has done very well. Hackers wanting to exploit the enterprise use malware, Trojans, and other indirect method. The common element in all of these attacks is the human element because of the phishing emails these employees open. By using these indirect attacks, hackers can implant malware on devices, routers, servers and other infrastructure that can remain undetected for an extended period, say for days, weeks or even months. The entire organization’s infrastructure and information could be exposed by these malware when they gets deeper into the network, and data and user accounts are often held hostage until the payment is made. Kiddies no more conduct the recent cyber crimes; it has become a business and has drawn a lot of attention. It is usually difficult in retrieving the lost data, and it is possible that data manipulation and permanent destruction of data or infrastructure can come after that.

How to solve the problem?

IT organizations are recommended to invest in processes, people and infrastructure to fight against the threats in the data center and the edge.

People and Processes: It is essential to hire a CISO and allowing him to build out his team is equally relevant, and all the employees must be similarly hyper-vigilant to protect the organization against any threat. Therefore, Cyber training is really important and must communicate with the employees on a regular basis and guide them regarding risks and related emails.

Invest in infrastructure: The new data center infrastructure equipment has advanced security protections built-in that protect better than the old product generations. Therefore, it becomes essential to upgrade to the latest generation of servers, and to take full advantage of new cybersecurity technologies available.

Source: https://www.cioreview.com/news/how-iot-and-edge-computing-contribute-to-cyber-security-nid-27550-cid-133.html

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How much do you really want Artificial Intelligence running your life?

Artificial Intelligence (AI) is the current hot item in "tomorrow world," as techies see it as the next new thing to take over outmoded human brains, some of which actually do possess a modicum of native intelligence. AI algorithms have been successfully implemented by many enterprises to do such tasks as determining credit risk, consumer marketing optimization, credit card fraud detection, investment decision making, x-ray and electrocardiogram interpretation, and efficient travel and navigation choices. So far, so good.

In the mold of "I am from the government and here to help you," AI is being promoted to even more critical tasks – say, driving a car. However, programmers and engineers might reflect a bit more on one of the more pervasive and deadly laws of the universe – the law of unintended consequences – and the limits of programmed intelligence.

Consider the recent crash of a Boeing 737 MAX aircraft, operated by Indonesian Lion Air, killing all 189 people on board. Flight data reports detail the vain struggle of the pilots trying to keep the aircraft level while the latest addition to the automated functions of the aircraft had erroneously declared an imminent stall and put the plane into a sharp, corrective dive. Attempts by the pilots to pull the plane to level flight were apparently overridden by the newest enhancement of the on-board computer system, and it nose-dived into the sea.

While the AI computer was making a billion calculations per second in a game of "match the output of the sensors to the library of stored known objects," the pilots of the doomed aircraft probably could tell that the aircraft was flying level in spite of questionable sensor input to the contrary.

Replacing human sensory input with electro-mechanical devices is common enough that the possibility of malfunction of either is a real consideration. Humans have the evolutionary advantage in that their brains have an innate ability to make distinctions in the real world. AI systems require learning exercises to identify objects and situations already mastered by a six-month-old child.  The AI computer must build its own library of objects against which it will base future decisions as it navigates its decision tree based on sensor inputs. What happens when a bug or ice fouls a sensor? AI also lacks the adaptability and value-judgement skills possessed by humans to deal successfully with a situation for which it has no prior training or reference data in its decision-tree core.

The unnecessary death of 189 people is a high price to pay for a computer programming glitch.  "To err is human" is good advice for AI programmers as well.

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Is Blockchain Technology Made For Every Business?

Amongst the appreciative talks of all the potential benefits of this trending technology, the markets are also abuzz with the repeated waves of crypto scams

Is Blockchain Technology Made For Every Business?

Since the beginning of 2017, cryptocurrencies and blockchain technologies have continued to disrupt the financial world with their potential. From entrepreneurs to seasoned business heads, all are looking for ways to get on this trending technology’s bandwagon. Amongst the appreciative talks of all the potential benefits of this trending technology, the markets are also abuzz with the repeated waves of crypto scams. This led to the birth of many stringent regulatory measures from the authorities and at times even the governments went on abridging the development of the technology in their state. Consequently, there are certain queries popping up regarding the potential benefits of the blockchain, are they really good for all? Is blockchain feasible for all businesses or not? And, is it necessary to do the blockchain compatibility test, before incorporating it into the current business model? This article will focus on all these and more such queries related to the dos and don’ts of the blockchain trend in order to clarify the concepts related to the technology and have a better understanding regarding the same.

What Is Blockchain?

To understand the blockchain feasibility of your business, the first step is to actually understand what it actually means and how its overall system functions. Simply put, blockchain is a decentralized ledger recording, checking and verifying each withdrawal, payment and trade processes within its bounds. Hosted around a network of nodes, it keeps track of all the transactions in blocks arranged in chronological order. Each block in this order carries a time stamp and reference to the previous ones thus, forming a chain- hence the name blockchain.

How Can It Help The Businesses?

For businesses, the spectrum of opportunities spans a great breadth- from the use of blockchain’s ability to achieve remote and autonomous consensus between users. The technology can help improve the transactional productivity and security, reduce the overall cost heads and even reduce the potential lag or downtime if faced any in the supply chain. Apart from securing the payment transactions from third-party interferences, blockchain technology also brings about a new level of transparency in the business ecosystems-something that is in high demand from the consumers nowadays. Data tracking regarding shipments, payments and other information is done instantly with a few smart clicks, thereby giving businesses a better mode of investigation and ultimately cutting down the administration time and costs.

Contractual transactions which are otherwise time-consuming and extremely costly for the businesses due to the involvement of mediators are made a quick breeze with blockchain technology. The technology has provided access to the smart contracts, agreements which can be automatically signed, validated and enforced. With several features consolidated in one single technology, businesses are facilitated with the opportunity of integrating services without disclosing an excessive amount of crucial data to third parties. 

Conclusion

No matter how extensively blockchain is providing solutions to the businesses, there is still a huge pocket of cybersecurity concerns that need to be addressed otherwise the damage caused by the technical glitches inflicted by hackers and spammers can be much severe than the previous ones. The inclusion of blockchain technology in operation leads to a complete switch from a centralized hierarchy to a decentralized system. So, it is advised to the companies planning for a switch to strategize well with close consideration of all positive and negative aspects of the technology before opting for a transition.

In case your companies are in need of a long-term and strategic Blockchain partner, TMA Solutions with 21 years of experiences in providing offshore R&D services for leading companies worldwide in Blockchain, Artificial Intelligence/Machine Learning, Internet of Things (IoT), Big Data. TMA Innovation Center or TIC is working with many strategic partners and universities to bring the best core values together. TMA Innovation Center creates an open environment fostering idea & knowledge sharing, R&D collaboration, creativity & innovation, technology transfer and incubation. So, visit us on our website https://www.tmasolutions.com/ or http://tma-innovation.center/ to find out more about our expertise on Blockchain as an innovative service.

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Cryptocurrencies Have Failed, And Blockchain Still Has Yet To Be Proven Useful

There are two common patterns in technological invention and its subsequent commercialization. The first is a technological breakthrough that sparks interests in commercializing it. The invention of the transistor in the legendary Bell Lab in the 1940s (which later gained the three inventors the Nobel Prize in Physics) fits this pattern. It took a while to work out the commercial applications of the transistor, beginning with the radio, then, in a powerfully transformative way, in computers. The second is an existing demand waiting for a new technology to be invented to meet the need. The invention of the internal combustion engine in the 19th century fits this pattern. The industrial revolution led to proliferating demand for a machine that can provide rotary power to move mechanical devices, including powering land and water vehicles, and eventually aircraft. The internal combustion engine was the answer.

The technology of blockchain and its application called Bitcoin, however, came as a single package. Since the beginning of this year, the dollar values of cryptocurrencies have collapsed. The two leading cryptocurrencies, Bitcoin and Ether, collapsed most spectacularly, losing up to 70% of their dollar value.

So, what’s next for cryptocurrencies?

It is abundantly clear by now that cryptocurrencies have utterly failed in their purported function as money. Very few day-to-day transactions are conducted with any of the cryptocurrencies, the exceptions are in the so-called dark web where they are used as anonymous medium of exchange of large transactions, equivalent to suitcases full of unmarked large denomination dollar bills that are favored by drug lords and terrorists. Their dizzying volatility means their values are mostly speculative, not for storage. And the fact that cryptocurrencies continue to define their value in dollar terms underscores how useless they are as a unit of account.

The function of money in a modern economy is also critically dependent on its institutional underpinning and support. Governments need to accept it as payment of taxes, the market must accept it for debt issuance, there must be a reasonable independent central bank that competently manages its supply to ensure price stability. In other words, the ability of money to perform all its critical economic functions are embedded in a deep sense of social trust secured with public institutions built around a central authority, the government.

If cryptocurrency as a specific application of the blockchain technology has been a spectacular failure, what then of blockchain itself? Once we unbundle the package of blockchain and cryptocurrency, we are then back to the first pattern of technology commercialization: an invention looking for a viable commercial application. Because blockchain was invented to create Bitcoin, what else can blockchain do on its own is a matter of exploration. As yet, nothing has been proven. There are a number of experiments taking place, and significantly all of them are with direct involvement of either the government or regulatory authority. For example, several Japanese banks have recently launched a mobile app for domestic payment called Money Tap that is based on blockchain technology, but it was done with formal approval from the Ministry of Finance of the Japanese government, the operations of which will come under the usual government financial regulation and oversight. And because viable commercial applications of the blockchain technology have yet to be realized, the valuation of the technology today is therefore exactly zero. This does not mean that blockchain is useless. It could be the transistor of the 21st century, or it could end up in the garbage heap of technological inventions that failed to find a commercial application. We will have to wait and see.

Great news! TMA Solutions will be your perfect partner if you want to do R&D about Blockchain with our 21 years of experience and 2400+ engineers in software development. TMA Innovation Center (TIC) was established to cooperate with worldwide technology companies, especially in new technology like AI/ML, Blockchain. TIC consisted of hundreds of skilled and experienced professionals working in various industries, which will help your project development. Click here to visit TMA Solutions and TMA Innovation Center for more information.

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Artificial intelligence could help doctors identify hard-to-spot colon polyps

Colon cancer is the second leading cause of cancer-related deaths in the U.S., but colonoscopies have been found to reduce the risk of death from the disease by 70 percent by finding and removing benign polyps before they have time to turn into cancer. Doctors, however, don't always find every polyp.

As a gastroenterologist, CBS News medical contributor Dr. Jon LaPook knows all too well that colon polyps can be tough to spot. They may be partly hiding behind a fold, or so flat and subtle that they're barely visible to the eye.

A new high-tech tool may be able to help doctors spot them. LaPook decided to give the new technology a test run - not as a doctor, but as a patient.

The colonoscopy was performed by Dr. Mark Pochapin, chief of gastroenterology at NYU Langone Health. Assisting Dr. Pochapin is a second set of eyes: a computer powered by artificial intelligence.

"The good news is what we do really prevent cancer … but we do miss polyps, and we have to recognize that anybody, no matter how good they are, has the potential to miss something because we're only human," Pochapin said.

A recent study published in the journal Nature Biomedical Engineering found artificial intelligence was able to detect polyps more than 90 percent of the time. Researchers in China had fed the computer more than 5,000 images from colonoscopies, and the computer used those pictures to teach itself to recognize polyps.

Dr. Seth Gross is heading one of the first studies exploring whether AI can help find polyps not just in a computer lab but in patients actually undergoing colonoscopy.

"The parts that we're trying to improve upon detection are those flat ones, very subtle. … And this is where artificial intelligence can be most helpful," Gross said.

LaPook is one of the early participants in the study, which began about two months ago. The first polyp spotted by the AI in LaPook's colon was very subtle, small and flat. Pochapin removed it but can't say for sure whether he or the computer spotted it first. As Pochapin withdrew the instrument, the AI spotted another small polyp.

We're happy to report that the polyps were totally benign, and not precancerous. LaPook's colonoscopy also highlighted the importance of doing a good job with the prep. His polyps were so subtle, they could easily have been missed if the lining of the colon wasn't clean.

The Final Say

Cancers are dangerous threats that the world is facing now. With the advent of new technology, the smart devices can give a help to prevent them like applying Artificial Intelligence to spot colon polyps.

With 21 years of experience in software development, TMA Solutions focuses on new technology such as Artificial Intelligence and Machine Learning to solve real life issues easier. If you need a software outsourcing provider working on Artificial Intelligence project in every industry, TMA Solutions is your eligible choice to cooperate. Our skilled developers in TMA Innovation Center bring the core values to go further with you.

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Artificial Intelligence: Useful, but risky.

Four out of 10 executives are concerned about the legal and regulatory risks of artificial intelligence, according to a recent Deloitte survey.

Lawyers and their clients are increasingly becoming aware of the benefits of artificial intelligence, but the risks of the burgeoning technology have left some clients wary of implementing AI.

In fact, four out of 10 executives had a “high degree of concern about the legal and regulatory risks associated with AI systems,” according to Deloitte’s recently released survey “State of AI in the Enterprise.” 

Artificial intelligence regulations and its risks cut across many practice. Lawyers suggested that clients should be fully aware of the data used by their AI and to keep an eye on any results it provides.

Companies may be wary of implementing AI if the program’s results have broad applicability, are difficult to reverse or have results that aren’t predictable. For example, the possibly difficult position a financial institution may face if it uses AI when issuing a loan. In the event the software makes a discriminatory or incorrect decision, detecting, correcting or stopping the result may be difficult.

If the results of an AI program’s algorithm causes a “detrimental outcome” to customers that a business may not be aware of, U.K. regulators won’t allow an enterprise to use, “‘Oh well, I didn’t know the computer would do that’” as a defense.

The slow adoption of AI may be based on the lack of regulation regarding AI, uncertainty about how their AI implementation could be challenged and not seeking to change the status quo.

It’s important, when using AI, to know its integrated data and the science behind it. Cross validation of important data should be performed to determine which should be used and that users should constantly retest the algorithm.

Some organizations are tepidly embracing AI because of the amount of data needed for training artificial intelligence and machine learning.

Regulations

There isn’t a single law regulating artificial intelligence, lawyers said, and AI touches a myriad of legal issues. However, a few attorneys cited provisions in the European Union’s General Data Protection Regulation as targeting AI.

The GDPR’s AI regulations are geared toward programs not being able “to run out of control and make substantial effects without human intervention and monitoring.”

Taking a global perspective when assessing which jurisdiction an AI program is confide to artificial intelligence is not limited to one jurisdiction.

Clients seek advice on how to develop their product and product counseling to minimize the client’s legal liability. Clients tend to also ask for advice regarding the legislative outlook for AI and how to manage their risk.

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Quality control and testing are improved by big data

Quality control and testing have proved themselves to be really important in product development and manufacturing. Without those aspects, faulty products could hit the marketplace, thus the consequences could be really serious, such as reputational damage, excessive costs and even life endangering.

However, big data is gaining popularity as an essential technology in creating quality control and testing more efficient and effective.

Reducing the amount of time to market

Companies can be able to significantly reduce the amount of time needed to handle validation testing before placing a product on the market by examining the findings of big data interfaces. One instance involved a company combining big data with artificial intelligence (AI) to capture tremendous amounts of data and process it more quickly than humans.

Moreover, AI make it easy for Intel to locate bugs while eliminating tests that are not relevant. According to the mentioned company, this solution reduces the number of tests performed by 70%, helping products reach the market faster without sacrificing quality.

Providing compiled insights that inform improved product design and testing

Some companies may have product tests being carried out all over the world and plan to use the results from those experiments to inform new, enhanced designs. Before big data became popular, collecting the information from those tests required a considerable amount of time, and locating users have to gather feedback about the product.

However, today's big data platforms can quickly look at opinions broadcasted on social media or, in the case of an internet-connected device, keep tabs on how people use products in development without explicitly reaching out to them to get their feedback.

For example, big data could find out which features within fitness trackers that a tester uses more frequent and the steps they go through to do so.

Collecting data throughout a period of time and extracting the meaningful sentiments from it could also increase the likelihood of a new product's later success. Predictive analytics can examine various aspects of the product development process and find the factors within it that highlight the things people like the most, as well as the things that frustrate them. Big data is also capable of predictive models, allowing brands to create thousands of versions of a product in seconds.

Improving testing relevance

By obtaining the information from big data platforms is useful in helping companies to choose between highly accelerated life testing (HALT) and accelerated life testing (ALT) procedures. Company’s money can be saved and more customer satisfaction can be ensured by using HALT, since it is able to find failures in products early in the development process. Calculating how long a product could perform before components start to break down by speeding up its aging process is what ALT can do.

Big data might show a potential unexpected weakness in a product, thereby spurring the manufacturer to see if a HALT could give more details about the causes for the failure. Then, the people overseeing quality control could target those problems before proceeding further with the development process.

Bringing analytics into existing equipment

Some companies offer bigdata analytics platforms with plug-and-play functionality enabling manufacturers to swiftly incorporate analytics tools into factory equipment. Then, those entities can potentially spend less money than they otherwise might to start being more reliant on analytics for quality testing.

Finding enhanced results by searching through data

Big data is already well established for quality control and testing purposes, but it's likely the technology and the respective tools to harness big data will become increasingly prominent as innovations progress and businesses explore ways to deliver high-quality, well-tested products to customers within reasonable time frames. With big data, consumers can benefit from enhanced products, and companies spend less time and money on irrelevant testing.

With 21 years in providing R&D services for leading companies worldwide, TMA Solutions has intensive experience and capabilities to bring your products and solutions to a new level by embracing new technologies (big data & analytics, AI, IoT, mobile, cloud, etc.). TMA Solutions's BI, big data and analytics team has supported many customers in building BI and analytics solutions to process large amounts of business data and provide real-time reports for business decisions. You can visit our website here to find out more information or email us now.

(Source: https://channels.theinnovationenterprise.com/articles/how-big-data-is-improving-quality-control-and-testing)

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Driving Big Data Innovation to New Levels by using Natural Language Processing and AI

“Data is the new gold” is the expression we have all heard, and it is not doubtful that customer data in particular can fundamentally transform a business. However, data is rarely stored, located or created in one place—and it’s really hard to know what data does exist to have a holistic and accurate view of a business. Any business executive with accountability to the bottom line will tell you, the success of a strategic initiative is dependent on accurate and timely information. Today though, manual processes that require folks in IT to dig through data to get insights for everyone else simply doesn’t scale.

To really drive innovation to a whole new level, we’re seeing a rise in the use of Natural Language Processing (NLP) and Artificial Intelligence (AI) to make finding and using trusted, quality data that much easier. In fact, Machine Learning (ML), AI and NLP are quickly becoming table stakes for analytics are what leading industry analyst companies have been aware. That requires significant heavy lifting at the infrastructure level and it’s not an easy thing to do.

For example, NLP cannot understand the context of an attribute without a systematic view of grammar. In a query if someone types in ‘show me all the PII data’, it is required that all the words in the phrase must be understood by the system, where ‘show me’ equates to ‘execute a search’. ‘All’ would signal a search in every row, column and comment field specifically for ‘PII’ which encompasses a multitude of other variables such as a person’s name, their date of birth, social security numbers, credit card numbers, address fields, etc. A search can be executed right at the moment when the system understand each word and how one word relates to another

Significant contributions have been made by the open source community in order to make NLP and ML algorithms more mature. Along with the constant evolution of open source software, developers are constantly putting code in the wild to trial and enhance. The open source community has led work in deep neural networks, AI, ML and NLP to help developers understand the underlying grammatical logic – what’s a noun, what’s a verb, how does one word relate to another.

Customers and prospects alike keep telling about the value of being able to search using natural language. The need and opportunity among healthcare providers and organizations for example, is incredible. A founder of a biomedical organization shared with us recently that he has a lofty goal—to build a global team science consortia to provide a quicker and better public health response to cancer by leveraging the best biomedical informatics, information and communication technology available. A technical challenge arose which is how to collect all the raw data from hundreds of cancer research projects around the world and provide an easy to use interface to help oncologists tap in to this global base of knowledge.

Within our connected enterprises we’ve amassed a rich metadata directory where we can extract value from defining relationships – even putting them into a knowledge graph and then building the NLP layer above that. The more data sources—the stronger the graphing capability. This is a very smart way to visualize the user experience and it’s another way to empower more business users to innovate faster through data-driven insights.

All of this drives the market forward and as we see NLP and AI technology maturing our use and implementation for data analytics will lead business transformation well into the next decade.

With 21 years in providing R&D services for leading companies worldwide, TMA Solutions has intensive experience and capabilities to bring your products and solutions to a new level by embracing new technologies (big data & analytics, AI, IoT, mobile, cloud, etc.).

TMA Solutions's BI, big data and analytics team has supported many customers in building BI and analytics solutions to process large amounts of business data and provide real-time reports for business decisions.

You can visit our website here to find out more information or email us now.

(Source: https://insidebigdata.com/2018/11/05/natural-language-processing-ai-innovation/)

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Education revolutionized by Big Data

Almost every sector has Big Data as an integral part, therefore, it is time for the education sector to deploy Big Data as well. In order to improve pedagogy, newer technological tools have been used by a lot of education hubs. Today, an Indian student has access to data and resources available anywhere in the world, which was impossible a couple of decades ago.

Big Data is essentially large sets of data that need to be studied in order to find insights that help a business grow. Business managers can be able to make better decisions because of Big Data uncovering hidden patterns and correlating insights. In the education sector, too, Big Data and machine learning together are creating a stir. While machine learning and its application has been around since the 1970s, it has gathered momentum thanks to artificial intelligence and Big Data Analytics. Given the pace with which things are changing, Big Data will certainly bring a revolutionary change in the education sector. Machine learning is basically data-mining which helps teachers accumulate all the information at one place. It helps teachers improve their teaching methodology by focusing on the strengths and weaknesses of each student.

Apart from benefiting students, it helps teachers in various ways. Some of its visible advantages are:

Becomes an assistant of teachers:

It is certainly difficult to manage a class of 80-plus students, also considering that each student is different and so is her intelligence quotient as well as learning style. Machine learning can serve as a great aggregator for learning by helping the teacher shed some of the administrative weight. Teachers get more freedom to devise courses and provide customized learning experience for students as per their learning capabilities.

Predict performance of students:

Teachers can be able to analyze the performance of students in both academics and sports by using machine learning and Big Data. The speed of an individual and her competitor can be marked, and thus the strengths and weaknesses can be worked on. Also, indicators like time taken to answer, sources referred to, types of questions skipped can be ascertained and analyzed. This, in turn, makes the assessment more personalized and holistic. It allows teachers to measure, monitor and respond in real-time to a student’s understanding of anything.

Tests students:

The method of ‘one size fits all’ is obsolete. Today, artificial intelligence can help provide feedback to teachers, parents and students regularly—clearly charting out the weak links, interests and areas of concern as far as the student is concerned. This helps both the student and the teacher to understand the sort of development and progress they have made towards their goal. This, in turn, can help create a corrective action plan based on real-time data.

Provides customized learning:

With Big Data, it is possible to provide a customized learning experience to each student. The learning and the behavioral patterns of each student will be provided for teachers. A customized learning experience can be created by using these inputs. In addition, classrooms can have groups based on the assessment the teacher receives through artificial intelligence. Besides ensuring a better learning experience, Big Data makes it possible in minimizing dropouts and disengagement amongst students.

Helps in recruitment:

Because of Big Data, it has become possible for colleges to analyze the potential employability of a particular student in a particular job vertical. This, in turn, can help them direct their as well as the student’s attention towards preparing for a job in that particular vertical. This can save a lot of time and energy for potential recruiters, and can aid colleges in successfully placing a majority of their students, thereby boosting their placement figures. For companies, it can ensure a better person-job fit and lesser turnover. The transition from traditional learning to learning aided by Big Data is happening at a rapid pace. It is the need of the hour and warrants a speedy acclimatization for all concerned. Towards that, active collaboration between academic institutions, government bodies and the private sector should be encouraged in order to stay ahead of the curve.

Source: https://www.financialexpress.com/education-2/how-big-data-can-revolutionise-education/1378871/

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Digital Marketing succeeds because of Big Data

The days when marketing decisions were guided by intuition and experience is over. Big Data is now in charge of determining important marketing decisions. This refers to the study and application of big, complex datasets, which cannot be processed by traditional data-processing applications. Companies can make better business decisions and strategic moves by using these figures which generate insights. The quality of decisions making and detailing processes is improved because of companies applying the right technology.

Because of the increase of number of outputs and customer characteristics, companies are faced with huge volumes of both structured and unstructured data. This level of processing is beyond the capacity of traditional databases and software techniques.

I use Google Analytics extensively in my work and also have my hands on a couple of big data tools. Digital marketing is what I am good at, so I know where businesses can start when using big data in digital marketing. In this article, you will learn three examples of how big data can be leveraged for digital marketing success.

Improving Marketing Campaigns

By using Big Data, companies are enabled to better target the core needs of customers by developing rich and informative content. Let’s understand how it helps companies collect data about customer behaviors. One example is cookie files. They collect information about customers’ activities as they browse the internet, generating personalized data in the process.

Aggregative advertising used in the past is no match against campaigns using big data. Taking the guesswork out of determining what customers want can be considered as the good thing about using big data when it comes to forming marketing campaigns. By using data like customer behavior, purchasing patterns, favorites and background, marketers can develop different buyer personas. For example, they may find that women are more likely to respond to email campaigns, use coupons and engage in bargains and deals, and shape their digital marketing campaign from there.

Though applying big data to digital marketing is a great idea, it’s necessary to use good analytical tools to ensure the data presents valuable conclusions. These methods ensure that actionable insight is derived in an efficient manner so that companies can make their decisions without delays. To evaluate what makes a good analytical tool, it should be able to access all types of data including cloud, social media data, log files, websites, emails and other unstructured data. It should support campaign attribution tracking, real-time analytics, funnels and third-party testing and integration tools.

Deciding prices of products and services

Companies traditionally price products and services using basic information like product cost, competitor pricing, perceived value of the product from the customer and demand. With big data, many other factors can also be used to make pricing decisions. For example, you can use data from completed deals, incentives and performance-based data. Big data emphasizes making pricing decisions as granular as possible, particularly in the business-to-business (B2B) sector, as each deal is different from the next.

It is necessary that companies need to remember that they may already have plenty of unused data at their disposal, such as customer preferences and general economic information. The challenge is how to derive valuable insight from this information. For example, does your pricing strategy consider what products a particular customer has purchased over the last five years? What is their disposable income? How much can they afford to pay for a product? Additionally, does your pricing strategy consider macroeconomic indicators like quarterly GDP growth rate, inflation rate, exchange rate, interest rate and government spending of the countries you operate in? Incorporating these insights will lead to better pricing decisions.

By using Big Data, human assistance possibly is no longer required, thus less chance of error since big data also allows you to automate, which can save time in price settings and lead to more accurate pricing decisions

Showing Appropriate Web Content

Online marketers will be able to serve customized content to their website visitors by tapping into their knowledge base to determine which content will be more engaging to each visitor. By using the information about which movies and shows the visitors have watched, Netflix does an exceptional job providing visitors with individualized recommendations. You can apply the same concept when designing your website by refraining from thinking of your page as a static site. For example, look at “time spent on page” data to determine what the visitor is interested in; the next time that particular visitor comes to your website, you can show them relevant content based on their browsing history.

Just as search engines return different results when you search for a term in different locations, your website will look different depending on who is looking at it. Though it will be a technical challenge to show customized content, an increasing number of consumers are demanding personalized experiences. Organizations cannot compete against their competitors if their digital marketing teams cannot meet these demands.

Use deductive and inductive research and customer self-selected methods in order to build your personalization strategy. Ultimately, the consumer will decide where to click and what to purchase, and companies that can serve those consumers better will win the game. It will be the early adopters who win the race because they have an initial lead.

Because of being able to gain insight into what their customers need and want, the amount of attention gained by big data has been significantly increased. The capacity to process large datasets is far more complex and advanced compared to traditional systems. Unlike early adopters, not all organizations have integrated big data into their marketing strategies. If those organizations want to compete in today market, evaluating their current systems is one thing they must do.

Source: https://www.forbes.com/sites/theyec/2018/11/08/how-can-big-data-contribute-to-digital-marketing-success/#43348f541ca8

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The uses of Internet of Things in your work

In April 1999, I created a PowerPoint presentation, and it was the first time I wrote about “the internet of things”. I was working for Procter & Gamble, manufacturer of everything from shampoo and soap to toilet paper, and I was pitching a new idea: how to improve efficiency by building wireless, internet-connected sensors into our products and supply chain. That seems obvious today, but back then it was crazy. The late 1990s was the time of AOL, internet cafés and the dotcom boom. Almost every internet-connected device was a computer and almost every internet connection was wired.

“The rise of mobile phones is simply the most important technological transformation of the 21st century”

No one knew about Wi-Fi, the name wasn’t created until later that year. At that time wireless sensors cost pounds – and we needed them for pennies. It was difficult to purchase mobile phones since they were expensive, and they were only connected to other phones and were used for talking, and annually there were fewer than five text messages were sent by an average person.

That world is hard to remember now, when the internet is wireless and everywhere, sensors cost a penny or two, and phones are internet-connected mobile devices carried by almost everyone.

That last point is especially significant. The rise of mobile phones is the most important technological transformation of the early 21st century. In 1999, less than 30pc of the world’s population had a phone. Today there are more phones than people on the planet and many phones are smartphones, even in developing nations. This matters for the internet of things. Phones are not phones any more: “phone” comes from the Greek for “voice”, and today only 1pc of mobile phone traffic comes from voice calls. A surprising amount of the rest comes from sensors. The average smartphone has 10 sensors, including a camera, microphone, barometer, thermometer and locator, and all of them are connected to the internet. A smartphone puts the internet of things in your pocket.

Smartphones can be counted as an example of how, in 20 years, the internet of things significant gains its popularity. The question now is what to do about it, and there is a straightforward way for businesses to find out.

Create a three-column list: in column one, write some things you don’t know about your operations. Don’t worry about whether the things seem knowable; imagine you have the chance to become omniscient. This can be hard because we don’t always notice what we don’t know.

“Smartphones are just one example of how, in 20 years, the internet of things has gone from nowhere to everywhere”

In column two, estimate how valuable knowing these things would be – not just for you but also for your customers.

Column three will take some research. Evaluate how easy it would be for internet-connected sensors to help you get that information. You may not need to sense the information directly. Most internet-of-things systems use proxy data: for example, inferring fruit ripeness from colour images, or room occupancy by sensing if lights are on.

When the list is complete, pick the thing that seems most valuable to know and easy to discover, and begin a project to see if you can discover it. Start small and cheap. Try to sense the thing once, in a controlled location, in the simplest way possible. Use off-the-shelf components. Assemble them with duct tape, either literally or figuratively. Do not leap straight into the ocean. Splash in a puddle first.

Then expand gradually. Go to a larger environment, or sense all the time instead of some of the time. Make the data valuable, by sending it to people who can use it, or by triggering an automated response. Keep expanding. Soon, your solution will have grown from a crazy idea into something ubiquitous, just like the internet of things itself did between 1999 and today.

Source: https://www.telegraph.co.uk/business/future-technologies/make-the-iot-work-for-your-business/

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