Question

Question 1 (15 Marks) Define the three types of Business Analytics approaches. Categories the following techniques into their appropriate type of Business Analytics. Provide at least two examples for each of the following application areas and also explain how they are playing a crucial role in their appropriate category of Business Analytics.

a) Expert Systems; b) Data Mining; c) Data Warehousing

Answer 1

a)Expert Systems:-

An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.Typically, an expert system incorporates a knowledge base containing accumulated experience and an inference or rules engine a set of rules for applying the knowledge base to each particular situation that is described to the program. The system's capabilities can be enhanced with additions to the knowledge base or to the set of rules. Current systems may include machine learning capabilities that allow them to improve their performance based on experience, just as humans do.The concept of expert systems was first developed in the 1970s by Edward Feigenbaum, professor and founder of the Knowledge Systems Laboratory at Stanford University. Feigenbaum explained that the world was moving from data processing to "knowledge processing," a transition which was being enabled by new processor technology and computer architectures.Expert systems have played a large role in many industries including in financial services, telecommunications, healthcare, customer service, transportation, video games, manufacturing, aviation and written communication. Two early expert systems broke ground in the healthcare space for medical diagnoses: Dendral, which helped chemists identify organic molecules, and MYCIN, which helped to identify bacteria such as bacteremia and meningitis, and to recommend antibiotics and dosages.

Examples: There are many examples of expert system. Some of them are given below:

• MYCIN: One of the earliest expert systems based on backward chaining. It can identify various bacteria that can cause severe infections and can also recommend drugs based on the person’s weight.
• DENDRAL: It was an artificial intelligence based expert system used for chemical analysis. It used a substance’s spectrographic data to predict it’s molecular structure.
• R1/XCON: It could select specific software to generate a computer system wished by the user.
• PXDES: It could easily determine the type and the degree of lung cancer in a patient based on the data.
• CaDet: It is a clinical support system that could identify cancer in its early stages in patients.
• DXplain: It was also a clinical support system that could suggest a variety of diseases based on the findings of the doctor.

b) Data Mining:-

Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.The process of digging through data to discover hidden connections and predict future trends has a long history. Sometimes referred to as "knowledge discovery in databases," the term "data mining" wasn’t coined until the 1990s. But its foundation comprises three intertwined scientific disciplines: statistics (the numeric study of data relationships), artificial intelligence (human-like intelligence displayed by software and/or machines) and machine learning (algorithms that can learn from data to make predictions). What was old is new again, as data mining technology keeps evolving to keep pace with the limitless potential of big data and affordable computing power.Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time-consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, telecommunications providers and insurers, among others, are using data mining to discover relationships among everything from pricing, promotions and demographics to how the economy, risk, competition and social media are affecting their business models, revenues, operations and customer relationships.

These are some examples of data mining in current industry.

• Marketing:- Data mining is used to explore increasingly large databases and to improve market segmentation. By analysing the relationships between parameters such as customer age, gender, tastes, etc., it is possible to guess their behaviour in order to direct personalised loyalty campaigns. Data mining in marketing also predicts which users are likely to unsubscribe from a service, what interests them based on their searches, or what a mailing list should include to achieve a higher response rate.
• Retail:- Supermarkets, for example, use joint purchasing patterns to identify product associations and decide how to place them in the aisles and on the shelves. Data mining also detects which offers are most valued by customers or increase sales at the checkout queue.
• Banking:- Banks use data mining to better understand market risks. It is commonly applied to credit ratings and to intelligent anti-fraud systems to analyse transactions, card transactions, purchasing patterns and customer financial data. Data mining also allows banks to learn more about our online preferences or habits to optimise the return on their marketing campaigns, study the performance of sales channels or manage regulatory compliance obligations.
• Medicine:- Data mining enables more accurate diagnostics. Having all of the patient's information, such as medical records, physical examinations, and treatment patterns, allows more effective treatments to be prescribed. It also enables more effective, efficient and cost-effective management of health resources by identifying risks, predicting illnesses in certain segments of the population or forecasting the length of hospital admission. Detecting fraud and irregularities, and strengthening ties with patients with an enhanced knowledge of their needs are also advantages of using data mining in medicine.
• Television and radio:- There are networks that apply real time data mining to measure their online television (IPTV) and radio audiences. These systems collect and analyse, on the fly, anonymous information from channel views, broadcasts and programming. Data mining allows networks to make personalised recommendations to radio listeners and TV viewers, as well as get to know their interests and activities in real time and better understand their behaviour. Networks also gain valuable knowledge for their advertisers, who use this data to target their potential customers more accurately.

c) Data Warehousing:-

A Data Warehousing (DW) is process for collecting and managing data from varied sources to provide meaningful business insights. A Data warehouse is typically used to connect and analyze business data from heterogeneous sources. The data warehouse is the core of the BI system which is built for data analysis and reporting.It is a blend of technologies and components which aids the strategic use of data. It is electronic storage of a large amount of information by a business which is designed for query and analysis instead of transaction processing. It is a process of transforming data into information and making it available to users in a timely manner to make a difference.The decision support database (Data Warehouse) is maintained separately from the organization's operational database. However, the data warehouse is not a product but an environment. It is an architectural construct of an information system which provides users with current and historical decision support information which is difficult to access or present in the traditional operational data store.