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Resource Curse is typically defined as a situation in which a particular resource-rich country overuses its resources by neglecting other major sectors and enters into a phase of economic contraction or stagnancy. As a result of the resource curse, a country with one-rich resource becomes dependable in commodity prices and its GDP fluctuates extensively. Venezuela remains a classic example of resource curse with highly volatile inflation and GDP despite ample oil resources.
What is an Absolute Advantage? Absolute advantage is one of the key macroeconomic terms, which is based on the principles of Capitalism and is often utilised in international trade-related decisions. Absolute advantage refers to the competence of a company, region or country to produce goods or services in an efficient manner compared to any other economic entity. The efficiency in production can be achieved by: Production of the same quantity of good or services as produced by other entity by utilising fewer amount of resources Production of a higher quantity of good or services as produced by other entity by using the same amount of resources What is the Significance of Absolute Advantage? Different countries or businesses possess a different set of ability owing to their location, soil composition, weather, infrastructure, or human resource skills. When applied in the right direction, various factors may pan out to offer more cost-effectiveness and hence build absolute advantage of the entity in comparison to others. The absolute advantage remains one of the critical determinants for the choice of the goods or services to be produced. Absolute advantage in a particular area often translates into profitability in the area. The profit margin increases by the achievement of cost efficiency, allowing the entity to ensure higher profitability over the competitors. For example, let us assume that the US can produce ten high-quality aircrafts utilising a specific amount of resources. China, on the other hand, can build 6 similar quality aircrafts using the same amount of resources. Thus, in the production of an aircraft, the US holds Absolute Advantage Let’s say the US has the ability to manufacture a certain amount of steel using 10 tonnes of iron ore. China, on the other hand, can produce the same quantity of steel using 8 tonnes of iron ore.Here, China here holds Absolute Advantage in the production of steel. How Countries Build Absolute Advantage? While natural conditions, which include climatic factors, geometry, topography, cannot be altered for achieving absolute advantage, the countries use the underlying factors strategically in their favour. Furthermore, factors of production are focused at by many companies or nations for building absolute advantages. Some of the strategies for building absolute advantage includes: Development of Technological Competencies- The implementation of innovative or latest technological innovations allows the entities to lower their production cost, facilitating absolute advantage. Enhancing Skills of Human Resources- The improvement in the cost-efficiency, along with the quality of the products, is targeted through imparting varying skill development programs. Many countries subsidize or aid the apprentice or labour training for enhancing the absolute advantage in trade. Improving Infrastructure- The infrastructure enhancement in the form of road, telecommunications, ports, etc. can be useful in enhancing the cost-effectiveness across different industries. What Do We Understand by Comparative Advantage Vs Absolute Advantage? Evaluating the comparative advantage introduces the concept of opportunity cost, which is the deciding factor to determine the production of particular goods or services. Opportunity cost refers to the potential benefits associated with the next best possible alternative which is missed out when one option is chosen over another. The Absolute advantage simply considers the capability of a business or region to deliver goods or services in the most efficient manner. The Comparative Advantage, however, also takes into account the benefits that are forgone if an entity decides for production of a particular product or services. Comparative advantage, based on the notion of mutual benefits, is often used in international trade deals. The Comparative advantage has been the major factor driving the outsourcing of services in search of cheap labour. Understanding through an Example For instance, country A can produce ten televisions with the same amount of resources with which it can make 7 laptops. The opportunity cost per television is 7/10 or 0.7 laptops. Meanwhile, the opportunity cost per laptop is 10/7 or 1.42 television. It highlights that country A is forsaking the production of 0.7 laptops if it is deciding to manufacture one television. On the other hand, it is missing out the opportunity to manufacture 1.42 televisions for every single laptop manufactured. Now, say Country B’s opportunity cost for producing a television is 0.5 laptop, and that of producing laptop is 2 televisions. Then, country B will have a comparative advantage in making televisions, and country A will have comparative advantage in producing laptops. It has to be noted that despite country A having absolute advantages in both the products, it would be mutually beneficial for both the countries if country B produces television while country A produces laptops. Do You Know About Absolute Advantage Theory by Adam Smith? The concept of Absolute Advantage was indicated by Adam Smith in his book called ‘Wealth of Nations’ which focusses on International trade theory. Adam Smith, in his book attacked on the previous mercantilism theory, which mainly stressed for economies to maintain trade surplus in order to command power. The Absolute Advantage theory considered that the countries possess different ability with respect to the production of varying goods or services. It argued that it is not necessary that a state may hold an absolute advantage in the production of all goods, and here the relevance of trade comes into play. It advocates that countries should produce those goods over which they hold a competitive advantage. It would allow the countries to make the same amount of goods using few resources or in less time. The theory propagates the relevance of trade for economic sustainability. What Are the Limitations of the Absolute Advantage Theory? The assumptions used in the Absolute Advantage Theory by Adam Smith may limit the application in real bilateral trade. The limitations of the theory by Adam Smith include: Smith assumed that the productive capabilities of a country could not be transferred between the two countries. However, in practical terms, the competitive scenario aids the nations to acquire new capabilities and acquire new resources, especially in the technological and human resource skill aspects. The two-country trade which was used as a basis for the theory does not consider the trade barriers levied. The present scenario, however, is strikingly dominated by trade wars between economies. Nations impose huge tariffs, import duties and other type of barriers to promote local manufacturers. Absolute Advantage theory assumes that the trade between the two nations will take place only if each of the two economies holds an absolute advantage in one of the commodities traded. However, in general, countries despite not holding absolute advantage are engrossed in international trade, boosting their economic setup.
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 data warehousing? Data warehousing is defined as the method of gathering & handling data from different sources to get meaningful output and insights. Data warehousing is central to the BI system and is built for data analysis and reporting. Source: © nfo40555 | Megapixl.com In simple terms, a data warehouse is a large collection of data utilized by businesses to make investment decisions. What are the characteristics of data warehousing? Data warehouse has supported businesses in making informed decisions efficiently. Some of its key features are highlighted below: The data in a data warehouse is structured for easy access, and there is high-speed query performance. The end users generally look for high speed and faster response time – two features present in data warehousing. Large amount of historical data is used. Data warehouse provides a large amount of data for a particular query. The data load comprises various sources & transformations. What are the benefits of data warehousing? The Companies which used data warehousing for analytics and business intelligence found several advantages. Below are some of them: Better Data: When data sources are linked to a data warehouse, the Company can collect consistent and relevant data from the source. Also, the user would not have to worry about the consistency and accessibility of the data. Thus, it ensures data quality and integrity for sound decision making. Faster decisions: Through data warehousing, it is possible to make quicker decisions as the data available is in a consistent format. It offers analytical power and a comprehensive dataset to base decisions on tough truths. Thus, the people involved in decision making do not have to rely on hunches, incomplete data, and poor quality data. It also reduces the risk of delivering slow and inaccurate data. How does a data warehouse work? A data warehouse is like a central repository where the data comes from various sources. The data streams into the data warehouse from the transactional system and other relational databases. These data could either be structured, semi-structured or unstructured. These data get processed, altered, and consumed in a way that the end-user can gain access to the processed data in the data warehouse via business intelligence (BI) devices, SQL clients and spreadsheets. A data warehouse merges the data that comes from various sources into a complete database. The biggest advantage of this merged data is that the Company can analyze the data more holistically. It also makes the process of data mining smooth. Copyright © 2021 Kalkine Media Pty Ltd. Component of a data warehouse A data warehouse can be divided into four components. These are: Load Manager Load Manager, also known as the front component, does operations related to the mining and loading the data into a data warehouse. Load manager transforms the data for entering into Data warehouse. Warehouse Manager The warehouse manager manages the data within the data warehouse. It analyses data to confirm that the data in the data warehouse is steady. It also conducts operations such as the creation of indexes and views, generation of denormalization and aggregations, modifying and integrating the source data. Query Manager Query Manager is a backend component that does operations concerning the supervision of user queries. End-User access tools End-User access tools comprise data reporting, query tools, application development tools, EIS tools, data mining tools, and OLAP tools. Roles of Data Warehouse Tools and Utilities The tools and utilities in a data warehouse are used for: Data extraction: The data extraction process involves gathering data from heterogeneous sources. Data cleaning: Data cleaning consists of searching for any error in the data. Data transformation: Data transformation process involves changing the data into a data warehouse setup. Data loading: This process involves data sorting, recapping, consolidating, verifying integrity. Refreshing: This process requires revising data sources to the warehouse. Application of data warehouse Data warehouse plays a considerable role across multiple sectors. Some of the sectors it caters to are highlighted below. Aviation sector In the aviation sector, a data warehouse’s role can be seen in crew assignment, route profitability analysis, any promotional activity. Banking Industry In the banking sector, the focus is on risk management, policy reversal, customer data analysis, market trends, government rules and regulations and making financial decision. Through a data warehouse, banks can manage the resources available on the deck effectively. Banks also take the help of a data warehouse to do market research, analyze the products they offer, develop marketing programs. Retail industry Retailers act as an intermediary between the producers and the customers. Hence, these retailers use a data warehouse to maintain the records of both producers as well as the customer to maintain their existence in the market. Data warehouses help track inventory, advertisement promotions, tracking customer buying trends and many more. Healthcare industry In the healthcare industry, a data warehouse is used to predict the outcome of any test and taking relevant action accordingly. Data warehouses help them to generate patient treatment report, offer medical services, track the medicine inventory. Many patients visiting hospital have health insurance. Through a data warehouse, hospitals maintain the list of insurance providers. Investment and insurance sector In the insurance and investment sector, the role of data warehouse becomes important in tracking the data pattern, customer trend and market movement. Services sector In the services sector, a data warehouse is used for maintaining financial records, studying the revenue pattern, customer profiling, resource management and human resource management. Telecom The telecom sector uses a data warehouse in the promotion of its offerings, making sales decision, distribution decision, features to include in case they decide to launch a new product based on the customer requirement. Hospitality The hospitality sector involves hotel and restaurant services, car rental services etc. In this sector, the companies use a data warehouse to study the customer feedback on the various services offered and accordingly design and evaluate their advertising and promotion campaigns.