Business Analytics Exam 1

Analyzing the Problem

*Analytics plays a major role* Analysis involves some sort of experimentation or solution process, such as evaluating different scenarios, analyzing risks associated with various decision alternatives, finding a solution that meets certain goals, or determining an optimal solution.

Problem Solving with Analytics

1. Recognizing a problem 2. Defining the problem 3. Structuring the problem 4. Analyzing the problem 5. Interpreting results and making a decision 6. Implementing the solution

business intelligence> information systems >statistics> operations reasearch/ management science> decision support system

BI: IBM's term: collection, management, analysis, and reporting of data IS:modern discipline evolved from business intelligence Statistics-science of uncertainty and the technology of extracting information from data; an important element of business, driven to a large extent by the massive growth operations research/ management science- analysis and solution of complex decision problems using mathematical or computer-based models decision support system-combo of business intelligence concepts and or/ms models to create analytical-based computer systems to support decision making

impacts and challenges

Benefits: reduced costs, better risk management, faster decisions, better productivity enhanced bottom-line performance such as profitability and customer satisfaction. Challenges: lack of understanding of how to use analytics, competing business priorities, insufficient analytical skills, difficulty in getting good data and sharing information, not understanding the benefits versus perceived costs of analytics studies.

Big Data

Big data to refer to massive amounts of business data from a wide variety of sources, much of which is available in real time, and much of which is uncertain or unpredictable. IBM calls these characteristics *volume, variety, velocity, and veracity*

Measurement Scales: Categorical Ordinal Interval Ratio

Categorical (nominal) data - sorted into categories according to specified characteristics. (gender, hair color, genotype) Ordinal data - can be ordered or ranked according to some relationship to one another. (very unhappy to happy, ranking, poor, rich etc) Interval data - ordinal but have constant differences between observations and have arbitrary zero points. (temp, SAT score ) Ratio data - continuous and have a natural zero. (age, weight, # children)

Defining problem

Clearly defining the problem is not a trivial task. Complexity increases when the following occur: - large number of courses of action - the problem belongs to a group and not an individual - competing objectives - external groups are affected - problem owner and problem solver are not the same person - time limitations exist

data set vs database

Data set - a collection of data. Examples: Marketing survey responses, a table of historical stock prices, and a collection of measurements of dimensions of a manufactured item. Database - a collection of related files containing records on people, places, or things. A database file is usually organized in a two-dimensional table, where the columns correspond to each individual element of data (called fields, or attributes), and the rows represent records of related data elements.

Data vs information

Data: numerical or textual facts and figures that are collected through some type of measurement process. --> important source of data is from the WEB Information: result of *analyzing* data; that is, extracting meaning from data to support evaluation and decision making.

tools to support business analytics

Database queries and analysis Dashboards to report key performance measures Data visualization Statistical methods Spreadsheets and predictive models Scenario and "what-if" analyses Simulation Forecasting Data and text mining Optimization Social media, web, and text analytics

Decision Model

Decision model - a logical or mathematical representation of a problem or business situation that can be used to understand, analyze, or facilitate making a decision. Inputs: Data, which are assumed to be constant for purposes of the model. Uncontrollable variables, which are quantities that can change but cannot be directly controlled by the decision maker. Decision variables, which are controllable and can be selected at the discretion of the decision maker.

descriptive, predictive and prescriptive analytics

Descriptive analytics: the use of data to understand past and current business performance and make informed decisions --> most common Predictive analytics: predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time. Prescriptive analytics: identify the best alternatives to minimize or maximize some objective. ex)aircraft or employee scheduling simply involve too many choices or alternatives for a human decision maker to effectively consider.

types of prescriptive models

Deterministic model - all model input information is known with certainty. Stochastic model - some model input information is uncertain. For instance, suppose that customer demand is an important element of some model. We can make the assumption that the demand is known with certainty; say, 5,000 units per month *(deterministic).* On the other hand, suppose we have evidence to indicate that demand is uncertain, with an average value of 5,000 units per month, but which typically varies between 3,200 and 6,800 units *(stochastic).*

types of metrics

Discrete metric - one that is derived from counting something. For example, a delivery is either on time or not; an order is complete or incomplete; or an invoice can have one, two, three, or any number of errors. Some discrete metrics would be the proportion of on-time deliveries; the number of incomplete orders each day, and the number of errors per invoice. Continuous metrics are based on a continuous scale of measurement. Any metrics involving dollars, length, time, volume, or weight, for example, are continuous.

Software Support

IBM Cognos Express An integrated business intelligence and planning solution designed to meet the needs of midsize companies, provides reporting, analysis, dashboard, scorecard, planning, budgeting and forecasting capabilities. SAS Analytics Predictive modeling and data mining, visualization, forecasting, optimization and model management, statistical analysis, text analytics, and more. Tableau Software Simple drag and drop tools for visualizing data from spreadsheets and other databases.

Influence Diagram

Influence diagram - a visual representation of a descriptive model that shows how the elements of the model influence, or relate to, others. An influence diagram is a useful approach for conceptualizing the structure of a model and can assist in building a mathematical or spreadsheet model.

Metric, Measurement and Measures

Metric - a unit of measurement that provides a way to objectively quantify performance. Measurement - the act of obtaining data associated with a metric. Measures - numerical values associated with a metric.

Model

Model - an abstraction or representation of a real system, idea, or object. Captures the most important features Can be a written or verbal description, a visual representation, a mathematical formula, or a spreadsheet.

Interpreting Results and Making Decisions

Models cannot capture every detail of the real problem Managers must understand the limitations of models and their underlying assumptions and often incorporate judgment into making a decision.

Example of descriptive, predictive and prescriptive

Most department stores clear seasonal inventory by reducing prices. Key question: When to reduce the price and by how much to maximize revenue? Potential applications of analytics: Descriptive analytics: examine historical data for similar products (prices, units sold, advertising, ...) Predictive analytics: predict sales based on price Prescriptive analytics: find the best sets of pricing and advertising to maximize sales revenue

Types of applications that can enhance by using analytics

Pricing: setting prices for consumer and industrial goods, government contracts, and maintenance contracts Customer segmentation: identifying and targeting key customer groups in retail, insurance, and credit card industries Merchandising:determining brands to buy, quantities, and allocations Location: finding the best location for bank branches and ATMs, or where to service industrial equipment Social Media: understand trends and customer perceptions; assist marketing managers and product designers

Recognizing the problem

Problems exist when there is a gap between what is happening and what we think should be happening. For example, costs are too high compared with competitors.

data reliability vs validity

Reliability - data are accurate and consistent. Validity - data correctly measures what it is supposed to measure. Examples: A tire pressure gage that consistently reads several pounds of pressure below the true value is not reliable, although it is valid because it does measure tire pressure. The number of calls to a customer service desk might be counted correctly each day (and thus is a reliable measure) but not valid if it is used to assess customer dissatisfaction, as many calls may be simple queries. A survey question that asks a customer to rate the quality of the food in a restaurant may be neither reliable (because different customers may have conflicting perceptions) nor valid (if the intent is to measure customer satisfaction, as satisfaction generally includes other elements of service besides food).

Structuring Problem

Stating goals and objectives Characterizing the possible decisions Identifying any constraints or restrictions

Mathematical Model

TC=FC+VC( Q*V)

3 forms of models

The sales of a new product, such as a first-generation iPad or 3D television, often follow a common pattern. Verbal description: The rate of sales starts small as early adopters begin to evaluate a new product and then begins to grow at an increasing rate over time as positive customer feedback spreads. Eventually, the market begins to become saturated and the rate of sales begins to decrease. Visual model: A sketch of sales as an S-shaped curve over time 3. Mathematical model: S = aebect where S is sales, t is time, e is the base of natural logarithms, and a, b and c are constants.

Implementing Solution

Translate the results of the model back to the real world. Requires providing adequate resources, motivating employees, eliminating resistance to change, modifying organizational policies, and developing trust.

uncertainty and risk

Uncertainty is imperfect knowledge of what will happen in the future. Risk is associated with the consequences of what actually happens.

Model Assumptions

assumptions are made to SIMPLIFY model price elasticity- ratio of % change in demand compared to % change in price linear demand prediction model- price increases, demand falls non linear demand- assumes price elasticity is constant

prescriptive decision models

help decision makers identify the best solution. Optimization - finding values of decision variables that minimize (or maximize) something such as cost (or profit). Objective function - the equation that minimizes (or maximizes) the quantity of interest. Constraints - limitations or restrictions. Optimal solution - values of the decision variables at the minimum (or maximum) point.

Business Analytics

is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions.