Near Field Communication (NFC) refers to the contactless or short-range wireless technology that allows the transfer of the data between two NFC-enabled devices. Using NFC technology, it is possible to make smartphones, wearables, payment cards, tablets, and many other smart devices smarter. Through NFC, you could even transfer data at a faster rate with a single touch.
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NFC has evolved from the integration of contactless detection and interconnection technologies.
Near Field Communication allows data transmission via electromagnetic radio field that allows two devices to communicate. It is essential to know that two devices using this technology can communicate with each other if they are NFC-enabled or simply, we can say that the devices have NFC chips.
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Both NFC and Bluetooth technology allow two devices to communicate with each other. However, there are certain advantages of NFC over Bluetooth. Let’s look at the key advantages.
NFC devices are classified into two types- Passive NFC devices and Active NFC devices.
Passive NFC devices
Passive NFC devices comprise tags or small transmitters that send information without any power source requirement. These devices do not process any information that is sent from other sources. Besides, these devices cannot connect to other passive components.
Active NFC devices
Active NFC devices can send as well as receive data. They are capable of communicating with both active and passive NFC devices. An example of an active NFC device is a smartphone. Another example of active NFC device is a card reader most commonly used when one is travelling using a public transport.
At present, there are three different modes of operation to determine the type of information that would be exchanged between the devices. These are:
What is Data Analytics? Data Analytics involves a set of quantitative and qualitative approaches and processes that can be used to determine useful information for business decision-making. The process involves various patterns and techniques, including: extracting a raw database, and categorising it to identify and analyse the behaviour, relation and connection of the results. The ultimate goal is to acquire valuable information in order to make decisions for businesses’ benefit and productivity. In today's competitive times, most companies chalk out their business plan with the help of data analytics. With organisations becoming customer-service oriented, data analytics has become a critical tool to reach the target audience in an effective manner while understanding their requirements. Once data is collected, it is analysed and stored according to organisations’ requirements. The data analysis process has multiple layers involved, and its diverse modules are not just used in businesses but also in science and social science fields. Rather than making decisions based on just available information, one can utilise data analytics in examining the data in standard ways and churning out the results from it. It has been observed that companies generally make decisions based on past references and future outcomes. Data analytics appears advantageous in providing useful information towards this end. Why do Businesses Need to Use Data Analytics? Many data analytics’ tools and softwares are readily available these days. These systems use resources, such as machine learning algorithms and automation. Data scientists and analysts are counted amongst the leading career options as well. These professionals use data analytics techniques while researching and presenting useful information for businesses to increase productivity and gain. The process helps companies understand their target audience and determine effective ways to cater to their needs. Data analytics can further be used to design strategies in marketing campaigns and promotions and also evaluate its results. Data analytics is primarily used in business-to-consumer (B2C) processes to boost business performance and improve the bottom line. There are data collection firms which gather consumer information and provide it to the businesses so that the companies can effectively influence the market. The collected data is not only used to understand and impact consumer behaviour but also determine market economics and its practical implementation. The data used in the process can be either be data collected in the past or newly updated data. There are various methods to manage consumer and market information. It may come directly from the customers or potential customers or can be purchased from the data collection vendors. The data primarily includes audience demographics, behavioural patterns and expense threshold. How Can Data Analytics be Effectively Used in Business Processes? Data analytics is an ever-evolving technique. Earlier, the data was collected manually, but with the rise of internet and technology, data is now collected online with the help of search engines and social media platforms. Subsequently, the information is analysed through available software. Here is a list of some key steps businesses can follow to leverage the benefits of data analytics: Set up crucial metrics: This step reduces the guesswork and provide data-based insights to the businesses. Before embarking on the data analytics process, it is vital to determine the goal for your business. Analysing customer data helps in understanding conversion rate, consumer spending ability, demographics etc. The results of the analysis can support the businesses while making decisions in launching an advertising or marketing campaign. Similarly, the unwanted data can be erased from the database so that the brands can focus on their right target audience. The relevant metrics will change the course of the company and push it in the right direction. Moreover, once your key metrics are set, even when the market conditions change in the future, you can adjust the metrics according to the requirement and achieve the results. Set a clear module: It is important to examine the data correctly by avoiding common mistakes. An ambiguous path can produce confusing insights while wasting time and energy of businesses. Therefore, it is recommended to draw a clear goal in order to achieve actionable insights. The data, when collected from different sources, need to be merged accurately in the analytics model. Businesses can modulate their data analytics systems either manually or through automation. There are various data modelling practices available in the market. The best use of these techniques can simplify the process of modelling complex data. Data visualisation: Once the relevant data is collected, and the modules are set to analysis, visualisation of that data will assist in understanding the information correctly. When the businesses have an acute knowledge of what their target audience wants, they can then focus on strategising advertisement and content, which matches the consumers' interest. It is the critical step in the data analytics process to distinguish insights from information. Not everyone is comfortable dealing with numbers. Hence, ensuring that key stakeholders understand essential points and information can be displayed in a visually appealing format seem crucial to capitalise on data effectively. Right tools to implement insights:Having access to data and insights can get overwhelming. However, the information is worthless if the businesses are unable to implement it successfully. While it is important to collect the data and set critical metrics and modules to analyse it, it is also imperative to translate the data into practical actions. The eventual goal is to improve sales or grow profits. It is ultimately in the marketers' hands to transform the gained insights into a successful implementation. The consumers' insights should be incorporated while establishing a marketing plan and at all decision-making steps.
What is Data Mining? Data mining is a process that facilitates the extraction of relevant information from a vast dataset. The process helps to discover a new, accurate and useful pattern in the data to derive helpful pattern in data and relevant information from the dataset for organization or individual who requires it. Key Features of data mining include: Based on the trend and behaviour analysis, data mining helps to predict pattern automatically. Predicts the possible outcome. Helps to create decision-oriented information. Focuses on large datasets and databases for analysis. Clustering based on findings and a visually documented group of facts that were earlier hidden. How does data mining work? The first step of the data mining process includes the collection of data and loading it into the data warehouse. In the next step, the data is stored and managed on cloud or in-house servers. Business analyst, data miners, IT professionals or the management team then extracts these data from the sources and accordingly access and determine the way they want to organize the data. The application software performs data sorting based on user’s result. In the last step, the user presents the data in the presentable format, which could be in the form of a graph or table. Image Source: © Kalkine Group 2020 What is the process of data mining? Multiple processes are involved in the implementation of data mining before mining happens. These processes include: Business Research: Before we begin the process of data mining, we must have a complete understanding of the business problem, business objectives, the resources available plus the existing scenario to meet these requirements. Having a fair knowledge of these topics would help to create a detailed data mining plan that meets the goals set up by the business. Data Quality Checks: Once we have all the data collected, we must check the data so that there are no blockages in the data integration process. The quality assurance helps to detect any core irregularities in the data like missing data interpolation. Data Cleaning: A vital process, data cleaning costumes a considerable amount of time in the selection, formatting, and anonymization of data. Data Transformation: Once data cleaning completes, the next process involves data transformation. It comprises of five stages comprising, data smoothing, data summary, data generalization, data normalization and data attribute construction. Data Modelling: In this process, several mathematical models are implemented in the dataset. What are the techniques of data mining? Association: Association (or the relation technique) is the most used data mining technique. In this technique, the transaction and the relationship between the items are used to discover a pattern. Association is used for market basket analysis which is done to identify all those products which customer buy together. An example of this is a department store, where we find those goods close to each other, which the customers generally buy together, like bread, butter, jam, eggs. Clustering: Clustering technique involves the creation of a meaningful object with common characteristics. An example of this is the placement of books in the library in a way that a similar category of books is there on the same shelf. Classification: As the name suggests, the classification technique helps the user to classify and variable in the dataset into pre-defined groups and classes. It uses linear programming, statistics, decision tree and artificial neural networks. Through the classification technique, we can develop software that can be modelled so that data can be classified into different classes. Prediction: Prediction techniques help to identify the dependent and the independent variables. Based on the past sales data, a business can use this technique to identify how the business would do in the future. It can help the user to determine whether the business would make a profit or not. Sequential Pattern: In this technique, the transaction data is used and though this data, the user identifies similar trends, pattern, and events over a period. An example is the historical sales data which a department store pulls out to identify the items in the store which customer purchases together at different times of the year. Applications of data mining Data mining techniques find their applications across a broad range of industries. Some of the applications are listed below: Healthcare Education Customer Relationship Management Manufacturing Market Basket Analysis Finance and Banking Insurance Fraud Detection Monitoring Pattern Classification Data Mining Tools Data mining aims to find out the hidden, valid and all possible patterns in a large dataset. In this process, there are several tools available in the market that helps in data mining. Below is a list of ten of the most widely used data mining tools: SAS Data mining Teradata R-Programing Board Dundas Inetsoft H3O Qlik RapidMiner Oracle BI
Dead Cat Bounce Dead Cat bounce is a colloquial phrase which is quite popular in the financial markets. The term was coined a long time ago and generally referred to the peculiar behaviour of the price. The phrase denotes a recovery in the asset’s price, often a sharp one after a prolonged downtrend. Sometimes it is also referred to a short but sharp fall, succeeded by an equally sharp recovery. How does a downtrend continue for a long time? Quite often, some securities in the financial markets depict a very long downtrend which may last from a few months to a few years depending on the severity of the fundamental headwinds. These prolonged downtrends are so strong that no support levels can withhold the downtrend and the prices keep on falling. Every support level gets taken out by excessive selling, which pushes the prices even lower. These lower prices force the long holders to liquidate their positions as no visible halt in the downtrend is noticed. This liquidation from existing buyers further fuels the selling, leading to the continuation of the downtrend. As the price keeps on falling, the buyers do not get enough confidence to buy and consequently keep getting overpowered by selling pressure continues the downtrend. So what is the ideology behind “Dead Cat Bounce”? In due course of a downtrend, the security tends to become oversold for the time being. Oversold is a technical term is used for security which seems to have fallen quite a bit in a specified period. In other words, a security that has been continually sold in a specified period tends to reach a level wherein the sellers are no more interested in selling at further lower rates. This is where the buyers’ step in and try to buy these stocks at low prices, leading to an increase in demand over the supply. This fresh buying tends to push the price up hence resulting in a short upside movement or, in technical parlance a “Bounce”. This point is where the downtrend witnesses a temporary upside momentum which is exactly quoted as a “Dead Cat Bounce”. The ideology is “Even a dead cat will bounce if fallen from a great height.” Likewise, a short bounce is quite expected after a prolonged downtrend which does not change the trend as a bounce does not mean the cat has become alive. Image Source ©Kalkine Group Does it signify a reversal from a downtrend? A Dead Cat bounce is an upside momentum, witnessed after a prolonged downward trend, generally near the oversold price region. But it is to be noted that this price bounce is merely a reaction of the downtrend which is often witnessed in the oversold areas. This does not change the entire trend, and more often than not, the trend continues in the primary direction after the bounce fizzles out. Why is it difficult to trade a Dead Cat Bounce? Most of the time it is difficult to trade a move like a Dead Cat Bounce as the bounce is often very quick and short-lived. The overall trend remains negative, which is in contradictory to the short-term bounce. Also, few investors mistake it for the trend change, which often proves to be a mistake. It generally becomes difficult to estimate some key support areas from where the bounce may occur as the downtrend is quite strong and lacks demand to support the price. However, there are some momentum indicators like RSI (Relative Strength Index), Stochastics oscillator etc. which may help to gauge oversold zones from where the bounce may occur. What are the reasons for a Dead Cat Bounce? There could be many reasons for a Dead Cat Bounce to occur on the charts as the sudden demand may come due to numerous reasons. Some of the reasons are Oversold Price As discussed, due to a prolonged downtrend and continued selling the price often comes to a level wherein the sellers are no more interested in selling at these lower prices and at the same time buyers often find a value proposition. This leads to a spike in demand, which ultimately results in a Dead Cat Bounce. Strong support area There are some levels of support on the price chart that are quite prominent. In other words, there are some regions of support which are quite strong and may remain relevant for years. These support levels are generally hard to break at the first attempt, which results in a bounce or a complete reversal. How to profit from a Dead Cat Bounce There are two different strategies when it comes to trading these kinds of sharp and against the trend moves. They are contradictory to each other, but both are based on proven price behaviour. Short Selling the rally As the primary trend of the underlying is still downward, one thought arises to go short on the bounce. This strategy one to participate in the downtrend but with a much better price. If these rallies are met with a resistance level like a falling trendline, horizontal price resistance etc. then these areas are ideal to sell the bounce in a downtrend. Buying into the rally Another opinion arises, why not to participate in the bounce? This strategy can also be fruitful provided the bounce should be stronger and last for a while, which is not always the case. This essentially calls for a very quick decision making while capitalising on the temporary bounce. Bottomline A Dead Cat Bounce is a prolonged downtrend followed by a short-term bounce. These bounces generally don’t last long, and once they fade, the trend continues towards the south. However, sometimes a bounce may also act as a reversal, but for the added confirmation a trader should also look at other signals of a reversal like bullish divergence at the bottom or a double bottom chart pattern.
Earned Premium typically reflects the premium collected by the insurance agency during the tenure of the policy that has expired to bear the risk of compensating the policyholder during the insured period. Insurance companies typically recognise the associated premium payment they take from the policyholder unearned till the expiry.