SBL1400 Lesson Summaries

Week 2 Introduction

• Business analytics is the process of analysing and using data to make smarter business decisions.
• Data consists of facts and statistics collected together for reference or analysis
• Generally there are three dimensions to big data: volume, variety, velocity and veracity

Week 3 The business decision process

• The business decision making process consists of six steps:
• Business problem/opportunity
• Data analysis plan
• Data collection
• Analytics
• Reporting
• Recommendations

Week 4 Data types and collection methods

• Data can generally be categorised as:
• Qualitative/quantitative
• Structured/unstructured
• External/internal
• Common types of analytics include:
• Text analytics
• Speech analytics
• Video/image analytics
• Combined analytics

Week 5 Business Systems HR and Accounting

• HR systems fulfil similar business needs, including job planning, employee recruitment and selection, workforce development, employee safety and health and labour relations.
• The goal of human resources analytics is to provide an organization with insights for effectively managing employees so that business goals can be reached quickly and efficiently.
• An accounting system is the system used to manage the income, expenses, and other financial activities of a business
• Accounting analytics ➢ Boost competitiveness ➢ Enhance financial reporting ➢ Manage risks ➢ Identify fraud

Week 6 Business Systems Marketing and CRM

• An effective customer relationship management (CRM)system improves customer satisfaction, expands the company’s customer base, enhances business sales and Improves workforce productivity.
• CRM analytics comprises all programming that analyse data about an enterprise's customers and presents it so that better and quicker business decisions can be made
• Marketing business systems ➢ Build relationships with the audience ➢ Involve the customer ➢ Generate income
• Marketing analytics themes ➢ Unmet need analytics ➢ Market size analytics ➢ Demand forecasting ➢ Market trend analytics

Week 7 Risk Management

• Risk Management is the forecasting and evaluation of financial risks together with the identification of procedures to avoid or minimize their impact
• The risk management process includes five steps:
• Identify the Risk
• Analyse the risk
• Evaluate or Rank the Risk
• Treat the Risk
• Monitor and Review the risk
• For risk managers analytics is key to highlighting areas of concern, identifying root causes of problems and choosing which high impact risk should be their focus

Week 8 Ethical Decision Making

• Business ethics involves applying a moral code to business operations and decision making.
• There is a fine line between the use and misuse of data in business research.
• In Australia we have The Privacy Act 1988 which regulates how personal information is handled.
• The Etlinger & Groopman (2015) Framework for Ethical Data Use suggests businesses should adhere to the following principles when dealing with private data: ➢ Beneficial ➢ Progressive ➢ Sustainable ➢ Respectful ➢ Fair

Week 9 Descriptive Data Analysis

• Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way Common
• Descriptive statistics are very important because if we simply presented our raw data it would be hard to visualise what the data was showing
• Variables can be either numeric or categorical. Then further broken down into continuous or discrete numeric variables, and ordinal or nominal categorical variables.
• Descriptive analytics simply describe the past using a range of data to draw comparisons.
• There are three main types of variables: continuous, discrete, and categorical
• Typically, two general types of statistic are used to describe data: Measures of central tendency (Mean, Median and Mode) and Measures of dispersion (Range, Variance and Standard Deviation)
• Measures of association measure the relationship between two numerical variables.

Week 10 Reporting/Data Visulisation

• Data visualisation is important as it transforms data into visuals that are much easier to interpret
• Effective data visualisation will allow the reader to see patterns and trends easily so that they can analyse the data
• Basically data is visualised in either tables or charts or a combination of both
• When visually displaying quantitative data all the ink in the chart or table should help to deliver the message or the meaning of the data, any extra ink should be removed

Week 11 Turning Insights into Reccomendations

• Decision making and problem solving are ongoing processes of evaluating situations or problems
• Recommendations are made on the findings of evidence based research
• Recommendations should be both relevant and feasible
• Recommendations should be written concisely, clearly and precisely