BUS105 Business Information System Is Essential For Understanding Modern Business Operations.

1. Data mining involves a process in which data is analyzed from various angles and transforming it into meaningful information that can be used in various activities such as reducing costs, increasing revenues. etc. The process involves sorting large datasets with the aim of identifying meaningful patterns and relationships that can be leveraged to solve different problems (Han 2011). Data mining tools enable organizations to determine key patterns related to customer habits and predict future trends. Data mining applications are some of the analytical tools used to analyze data. The applications allow data analysts to conduct data analysis from various angles, classify it, and summarize the relationships identified in the data. Technically, data mining involves analyzing various kinds of data to identify patterns among multiple fields in relational databases.

Data mining is used in various industries including financial, retail, and telecommunication industries. Many companies in financial industry collect a lot of data which is leveraged to improve business operations.  Data in the financial industry has high quality and is reliable which facilitates data mining to identify patterns that could help financial enterprises. Some of the typical data mining applications include loan payment prediction, fraud detection, and customer classification (Kirkos 2007). Many companies in this industry have designed and developed data warehouses for data mining. Data mining is leveraged to analyze customers’ loan repayment data to predict their loan payment habits and credit policy analysis. Many banks use data mining to classify and cluster customers based on their demographic profile which includes details such as age and location. This is essential for companies that seek to segment target audience and implement targeted marketing strategy. Additionally, companies use the patterns to detect anomalies that could be indicators of financial crimes such as money laundering.

Data mining is appropriate for the retail industry as it collects a large amount of data that is instrumental to business operations of many companies. Some of the data collected include sales generated, goods transportation, product consumption, customer purchasing history, etc. Typically, the amount of data gathered is expected to continue growing due to increasing internet penetration. Data mining has become a key part of competitiveness in the retail industry as its assists in determining patterns indicating customer buying habits which enable companies to improve the quality of services offered, maintain customer satisfaction, and retain customers (Linoff 2011). Companies design and develop data warehouses considering the positive impacts of data mining on their business operations. Data mining is used to conduct a multidimensional analysis of customers, sales, products, and locations to determine product purchasing patterns (Phua 2010). Some companies use data mining to analyze customers and sales made to determine the effectiveness of sales campaigns. Retail giants such as Walmart and Amazon use data mining to cross-reference items and adjust their inventory to market demand.

Telecommunication industry is an emerging industry offering various services such e-mail, internet messenger, etc. Due to the rapid growth of computer and communication technologies, many telecommunication companies are using data mining to understand the business. Data mining assists in detecting fraudulent activities, improve resource utilization and improve the quality of service (Turhan 2009). Companies use data mining to analyze telecommunication for fraudulent patterns or unusual patterns.

2. The first element of data mining involves data extraction, transforming it into meaningful state, and loading the transformed data onto data warehouse. Data on computer sales can be extracted from point of sale systems, transformed into categories such as laptop models, make, year of manufacture, etc., and placed onto a database.

Storing and managing data in a multidimensional database system is another element of data mining. Based on the data of computer sales, database creator is used to define database hierarchies and levels before storing the data. Newly transformed data is stored in this database, and the old data is updated when changes occur.

The third element of data mining is providing data access to information technology professionals and business analysts. Information technology professionals can access the multidimensional database system to manage the data (Gnanapriya 2010). Business analysts are also allowed to access the data to analyze it and review it.

The fourth element is analyzing data by a software application. Once data in the multidimensional database is ready, analytics software can be used to analyze it. Tools for data mining can be used to sort through the data sets in search of patterns and relationships which build a model for predicting customer behavior and machine learning which use algorithms to analyze massive data sets (Kantardzic 2011). Some software applications used to analyze the data include statistical analysis software, text mining tools, business intelligence software, and data visualization tools.

The final element is presenting data analyzed in a meaningful way by using visual tools. When presenting an analysis of data on computer sales, visual presentation of data in a table or graph is used. The graph shows trends and patterns of customer buying habits and helps make the data clearer.

3. While data mining has many potential benefits, its implementation is affected by various problems. One major problem is mining information global information systems and heterogeneous databases. The data can be mined from different data sources on Local network or the Internet. These data sources may be structure, unstructured, or semi-structure which adds challenges to data mining (Kaisler 2013). It is difficult for a data mining system to effectively mine these data sources and achieve good mining results from various sources. Different sources may need unique algorithms or methodologies. The proliferation of heterogeneous databases at semantic and structural levels pose a challenge to the data mining efforts.

Handling complex types of data is an issue in data mining. Databases may contain spatial data, multimedia data objects, temporal data, etc. which cannot be mined by one data mining system (Cao 2010). Much of the real world data is heterogeneous and exists in different data forms such as audio, complex data, etc. It is tough to handle these data types and extract meaningful information.

Often, data mining systems have to handle noisy or incomplete data which produces ineffective results. Data cleaning methods have to be used when handling incomplete data. If the methods are not used, the accuracy of the patterns discovered may be poor. Since real-world data is incomplete, heterogeneous, and noisy, a large amount of data mined is inaccurate. These problems occur due to errors of instruments used to measure the data or human errors. Noise and incomplete data makes data mining challenging and leads poor results which may not achieve the intended goal.

Another problem is data redundancy which is found in various data collected. Integrating redundant data such as geo data, text, social, multimedia files, etc. is a challenge is data mining. Due to data redundancy, data mining methods have to consider various classifications based on what is considered appropriate in the data. This can be wasteful given that the data be duplicated or repeated which creates complexity in the data mining process.

Security and privacy concerns by individuals and institutions is another challenge that undermines data mining. Data mining in various industries may allow discovery of patterns that can breach an individual’s privacy (Kleinberg 2007). For example, categorizing patients based on multiple factors such as age and gender can result in discriminatory practices by health providers and insurers.  The problem is how to make information anonymous to prevent disclosure of confidential data to users. Often, personal identifiers such as name and age are removed to make it hard to link data to specific individuals. However, the data can still be linked to a particular population group such as people living in a specific region. Another example of privacy issues with data mining is payment information held by clinicians. Performing data mining on patient financial information without identifying this purpose with the patient during data collection can be considered to misuse of information. As such, data mining in some settings requires consent of individuals involved. Since data mining focuses on extracting unknown patterns, data mining systems used does not know valuable personal data or relationships can emerge from analyzing sensitive information such as payment data. Thus, determining the primary purpose of the data mining process and restricting the use of the data in data mining can be challenging.

1. a)There are 7,978 accounting-related jobs.

b)

Today, five Job categories have more jobs than Accounting category. Trades & Services category has 14000 jobs the highest compared to Accounting with 7978. The category is followed by Information & Communication Technology, Healthcare & Medical, Manufacturing, Transport & Logistics, and Sales with 13724, 11522, 9678, and 8298 jobs respectively.

The number of jobs are less at lower pay rates but increase as pay rate increases. The salary range $50k-$60k has the largest number of jobs hence could be defined as the optimum pay range. Pay rates above $60k have a decreasing number of jobs.

d)

Tasmania has the least number of jobs available while New South Wales has the highest number of jobs.

e) Based on the findings from the site, there are particular areas, pay scale, and job categories that have a high number of jobs. Pay scale $50k-$60k has the highest number of accounting jobs. Pay scale $50k-$50k and $60k-$70k also have many jobs. It’s recommendable for graduates to look for accounting jobs within the pay range $40k to $70k. New South Wales, Queensland, and Victoria have the highest number of jobs. Graduates should focus on applying for accounting in these states as they have more job opportunities. The accounting job types with the highest number of jobs include Business Services & Corporate Advisory, Financial Accounting & Reporting, Account Officers, and Taxation. Graduates should consider applying for these job types.