Terms Beginning With 'e'

Earnings Before Interest, Depreciation and Amortisation

  • January 02, 2020
  • Team Kalkine

Earnings Before Interest, Depreciation and Amortisation (or EBIDA) reflects the earnings of a company post adding the interest expense, depreciation & amortisation to the net income, and taking tax into consideration.

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

What is Day Trading? Day trading is popular among a section of market participants. It is a type of speculation wherein trades are squared-off before the market close in the same day. An individual or a group is engaged in buying and selling of securities for a short period for profits, the trades could be active for seconds, minutes or hours.  One can engage in day trading of many securities in the market. Anyone who has sufficient capital to fund the purchase can engage in day trading. For a class of people, day trading is a full-time job.  Day traders are agnostic to the long-term implications of the security and motive is to benefit from the price changes on either side and make profit out of the asset price fluctuations within a day. They bet on price movements of the security and are not averse to take short positions to benefit from the fall in price.  Day trading is not only popular among individuals or retail traders but institutional traders as well, therefore the price movements are large sometimes depending on the magnitude of information flow and accessibility.  Everyone wants to make money faster, and many are inclined to speculate in markets, but it comes with considerable risk and potential loss of capital. People engaged in day trading also incur losses, and oftentimes outcomes are disheartening.  Day trading is a risky activity, similar to sports betting and gambling, and it could become addictive just like gambling and sports betting. Since the motive is to earn profits, the profits realised from day trading also tempt people to continue speculating.  People spend considerable time and efforts to make the most out of day trading. They have to continuously absorb and incorporate information flow, which has become increasingly accessible driven by new-age communications systems like Twitter, Facebook, forums etc. But not only information flows have been favourable, day traders are now equipped with best in class infrastructure to execute trades even on compact devices like mobile phones. The accessibility to markets is at a paramount level and gone are days of phone call trading and lack of information flows.  What are the essentials for Day Trading? Basic knowledge of markets With lack of basic knowledge of markets, day trading may yield unacceptable outcomes. It becomes imperative for people to know what’s on the stake. Prospective day traders should know about capital markets, and the securities traded in capital markets like bonds, equity and derivatives.  Buying shares and expecting a return from the price movements are on the to-do list for many. However, it is important to know about and risks and potential returns from speculating in capital markets.  After getting some basic knowledge about markets and securities, aspiring day traders should know how to analyse market prices of securities through fundamental analysis and technical analysis. Although day traders don’t practice fundamental analysis extensively, they spend considerable time to apply technical analysis, to formulate a entry and exit strategy.   Device and internet connection Trading is now possible on mobile applications as well as computer applications or websites. An aspiring day trader will likely begin with mobile phone given the accessibility, and laptops/computers are useful as scale grows larger and complex.  Internet connection is prerequisite to practising day trading, and it is favourable to have a fast internet connection to avoid glitches and potential problems. These perquisites are now available with large sections of societies.  Broker and trading platform A broker will facilitate a market for potential trades. The security brokerage industry has also seen a profound shift as technology has driven cost lower while competition is ramping up across jurisdictions. Large retail brokerages have moved towards zero commission trading in the U.S., and the same is seen being the trend across other geographies as well.  The entry of discount and online brokerages has perhaps given wings to the retail market participants as well as the retail market for security brokers. Robinhood has grown immensely popular in the United States, but there are many firms like Robinhood in other jurisdictions. Each country has some firms with business model on same lines as Robinhood.  Brokers now offer high-quality mobile applications and web services to clients, and trading security has never been so accessible. They also provide access to the global market along with a range of securities, including commodity derivatives, currency derivatives, CFDs, options, futures, bond futures etc.  Real-time market information flow   On public sources, market price information is at times not live due technical shortcomings, which will not work appropriately, especially for day traders. Brokers not only provide platform and market but several other services, including margin lending, real-time data, research.  Day traders closely track prices of securities and overall information flow to incorporate developments in bidding, and real-time data provides accurate prices throughout market hours.  Information flow largely relates to the news around the company, industry or economy. Day traders now have far better sources of information than the conventional sources, and sometimes these sources could be exclusive to a group.  What are the risks of day trading? Most of the aspiring day traders end up losing money, given the lack of experience and knowledge. They should rather only bet on capital that they are comfortable to loose, in short, they should avoid risk of ruin. Day trading is sort of pure-play speculation and application of knowledge, information flow, laced with good trading system is paramount. The only concern of day traders is movement in price, which contradicts from investments. Day traders try to time and ride the momentum in the price and exit the trade before momentum turns otherwise, which can happen frequently.  It consumes considerable time and induces stress on the individuals given the nature of security prices, which can move north and south abruptly throughout the day, hours, minutes and seconds. Day traders should have enough capital to trade in cash instead of margin.  Day trading on margin or borrowed money is extremely risky and has the potential to make a person insolvent, especially in cases of extreme risk-taking. The leverage associated with borrowed money magnifies profits as well as losses.  Aspiring day traders should equip themselves with adequate knowledge, competency and sound risk management process. Although fast money is dear to most, it is better to know what is at stake before jumping into markets with excitement.   

Early Exercise is a term in the option contract. The process allows the investors to buy or sell the underlying asset before the expiration date. It could be exercised in both types of option, i.e., American-Style and European- Style.

We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it. OK