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• Data is a collection of raw, unstructured facts or statistics which can be processed for analysis.
• Data is the monthly expenses one has, such as spending on groceries, entertainment, and bills.
• Data analysis is the comparison of monthly expenses over several months to see where costs can be cut.
• Data might include the number of cups of lemonade sold, weather conditions, and customer feedback.
• Data analysis could involve figuring out which days have the highest sales and if sunny weather correlates with more sales.
• Data analysis enables better decision-making.
• Data analysis helps identify trends and patterns.
• A factual basis for problem-solving can be provided by Data analysis.
• Data analysis and business modeling transforms input data into useful information.
• This data processing and business modeling enhances managers' ability to use data effectively to understand customers and markets better, and improve products and services.
• Understanding the value of data and information is essential, along with hands-on experience in data analysis, data management, and quantitative business modelling.
• Topics covered include definitions and concepts, the role of analytics in business, and the types of analytics, namely descriptive, predictive, and prescriptive.
• The relevance and use of data in business models and modeling, including deterministic and stochastic models are also included in the topics.
• The topics covered include descriptive, predictive, and prescriptive models, and the problem-solving process.
• Business analytics is the process of assessing the data available to a company and using it to make data-driven decisions.
• The process of collating, sorting, processing, and studying business data, and using models and methodologies to transform data into business insights.
• Businesses use data, processing(modelling) and insight for information technology, statistics, computer science and psychology.

Applications

• Pricing involves setting prices for consumer and industrial goods, government contract, and maintenance contracts.
• Airlines use data analysis to determine ticket prices according to demand, season, and competitor pricing.
• Customer segmentation identifies and targets key customer groups in retail, insurance, and credit card industries.
• Insurance companies analyze data to identify high-risk and low-risk customer groups and create tailored policies.
• Merchandising determines brands to buy, quantities, and allocations.
• Retail stores may look at past sales data to determine which brands of jeans sell most, and helping make smarter inventory decisions
• Location involves finding the best spot for bank branches, ATMs, or servicing industrial equipment.
• Coffee shop chains use data analysis to pinpoint locations with high foot traffic but low competition.
• Staffing ensures appropriate staffing levels and hires the right people.
• Seasonal trends can figure out how many cashiers are needed during different shifts or seasons.
• Healthcare schedules operating rooms for better use, improves patient flow and waiting times, purchases supplies, and predict health risk factors.
• Healthcare providers review patient data to find bottlenecks such as long waiting times.

Benefits of Business Analytics

• Business analytics can reduce costs or keep companies on budget.
• Manufacturing company uses analytics to reduce warehousing expenses.
• Personnel costs example
• Business analytics can improve decision-making.
• A fast-food chain employs real-time analytics to adapt menu and pricing based on immediate customer feedback.
• Business analytics enables the measuring of accomplishments against goals.
• An IT firm utilizes analytics to monitor employee productivity.
• Business analytics builds efficiency.
• A travel agency uses customer analytics to personalize offers, thereby increasing customer satisfaction and profitability.

Challenges of Business Analytics

• There is a lack of understanding of how to use analytics.
• A small business purchases an analytics tool but fails to train its staff.
• Competing business priorities delay the implementation of predictive analytics for patient care due to budget allocations.
• Insufficient analytical skills means that marketing teams can have extensive sets of data lacking the skills to analyze it for campaign optimization.
• Difficulty in getting good data and sharing information leads to retail businesses struggling with inconsistent data collection between branches.
• Misunderstanding the benefits versus perceived costs, education institutions hesitate to adopting tracking, fearing the costs outweigh the benefits.
• Statistics, data mining, business intelligence/information systems, simulation and risk, visualization, modeling and optimization are factors.

Types of Business Analytics

• Descriptive analytics help in understanding past and current business performance to make decisions, addressing questions like "What happened?" and "What is happening?".
• Statistical measures like means, standard deviations, and probability distributions are the tools.
• Predictive analytics predicts the future through historical data by detecting pattern or relationships in these data, and then extrapolating these relationships forward in time.
• Decision analysis, decision trees regression models, forecasting and simulation are techniques that can be applied.
• Prescriptive analytics identifies the best alternatives to minimize or maximize some objective, asking "What should I do?" and "Why should I do it?".
• Linear programming is a suitable tool.
• Most department stores mark down prices to clear seasonal inventory and the main question is when to decrease.
• Descriptive analytics use data to examine price sold, advertising, units, historical.
• Predictive analytics uses data to forecast sales based on price.
• Prescriptive analytics uses data to find sets of pricing and advertising to maximize sales revenue.

Data

• Data includes facts and figures collected through measurement, can be numerical or textual.
• Cross-sectional data is gathered at a single point in time to compare groups or categories.
• Example: Customer Satisfaction Survey collected in a specific time frame
• Time series data is gathered over multiple periods to track changes and trends, and forecast future values.
• Example: Monthly sales data collected over the last two years
• Information is the result of analyzing data which helps extract meaning to support decision-making.

Big Data

• Big Data represents a massive amount of different qualities: volume, variety, velocity, and veracity.
• Volume refers to the amount of data, where Netflix uses multiple terabytes per hour to improve user experience.
• Variety includes different structured data and unstructured data, where healthcare systems use both types.
• Velocity is the speed at which new data is generated, with platforms like social media seeing hundreds of thousands of updates per minute.
• Veracity involves the uncertainty of data, for example, user-generated content on review sites.
• Reliability measures that the data is consistent and accurate.
• Validity measures that the data is measuring what it should be.
• The number of calls to customer service help assess dissatisfaction.
• A reliable number of customer service calls are counted each day.
• It cannot be used to assess customer dissatisfaction, as many might be simple queries.

Models used in Business Analytics

• Models are abstractions of real system, ideas, or objects.

• Models are written or verbal description, a visual representation, a mathematical formula, or a spreadsheet

• A stochastic model account for certain levels of unpredictability or randomness in data to predict outcomes

• A deterministic model gives constant results every time for the same set of inputs.

• Decision models are logical or mathematical representations of a problem that can be used to assist with a facilitating decision.

• Inputs include data, uncontrollable variables, and decision variables(controllable)

• Constant Data includes costs of glass lemonade over a time period.

• Weather conditions are variables which are not directly in control of the owner

• "Buy One, Get One Free." is an example of a decision variable.

• A production cost model can calculate the amount that product costs versus outsourcing costs to improve efficiency. Breakeven point = 1000. IF quantity is < 1000, outsource.

• Descriptive models can allow "What if?" questions.

• Predictive models are analysing historical data. To determine promotion.

• Prescriptive models help best find solutions. To maximize total revenue, by identifying the price variable.

• Step 1 to solving a problem involves recognizing a problem.

  • Example being customer service noticing cancellations.

• Step 2 is defined as complexity that can occur for courses of action.

  • The customer attrition may have risen over the last quarter negatively impacting overall growth overall.

• Step 3 is goals, objectives and any constrictions.

  • Increase customer retention with current obligations in the future using the current budget available.

• Step 4 includes the experimentation or evaluating the data/scenarios.

  • Evaluate exits for any cancellation feedback with usage data.

• Step 5 is interpreting results from model assumptions.

  • 60% cancelled due to customer service, 30% mentioned a lack of advanced features

• Step 6 includes the implementing of solutions with resources available.

  • Providing adequate feedback on issues.

Conclusion

• Data: Data is numerical or textual facts and figures that are collected through some type of measurement process
• Business Analytics : The process of looking at and assessing the wealth of data a company already has at its disposal and using it to make data-driven decisions.
• Types of Analytics: An overview of descriptive, predictive, and prescriptive analytics and their applications.
• Decision Models: How to formulate and analyze decision models to make informed choices.
• Problem-Solving Framework: A systematic approach to recognizing, defining, structuring, and solving business problems.