ISBA 2 - Chapter 1 & 2

Analytics

is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models. It is used to help managers gain improved insight into their business operations and make better, fact-based decisions.[cite: 1]

Pricing

Setting ________ for consumer and industrial goods, government contracts, and maintenance contracts.[cite: 1]

Customer Segmentation

Identifying and targeting key customer groups in retail, insurance, and credit card industries.[cite: 1]

Merchandising

Determining brands to buy, quantities, and allocations.[cite: 1]

Location

is best for bank branches and ATMs, or where to service industrial equipment.[cite: 1]

Social Media

Understand trends and customer perceptions; assist marketing managers and product designers.[cite: 1]

Reduced cost

identifies unnecessary expenses, allowing organizations to reduce costs and allocate resources more effectively.[cite: 1]

Better Risk Management

helps organizations identify potential risks, predict future problems, and implement preventive measures before issues become serious.[cite: 1]

Faster Decision

provides real-time insights that help managers and decision-makers[cite: 1]

Better Productivity

helps organizations optimize workflows, improve employee performance, and automate repetitive tasks.[cite: 1]

DESCRIPTIVE ANALYTICS

(Knowing The Past "What happened?") Examine historical data for similar products[cite: 1]

PREDICTIVE ANALYTICS

("What will happen?") Predict the possible sales based on specified prices. Use statistical methods and forecasting[cite: 1]

PRESCRIPTIVE ANALYTICS

("How Can We Make it Happen?") Find the best sets of pricing and advertising to maximize sales revenue.[cite: 1]

SQL

Various Databases[cite: 1]

EXCEL

Spreadsheets[cite: 1]

TABLEAU SOFTWARE

Simple drag and drop tools for visualizing data from spreadsheets and other databases.[cite: 1]

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.[cite: 1]

R/PYTHON

Advanced programming-based data preparation, analytics and visualization.[cite: 1]

SAS/SPSS/RAPID MINER

Predictive modeling and data mining, visualization, forecasting, optimization and model management, statistical analysis, text analytics, and more using visual workflows.[cite: 1]

Data

Numerical or textual facts and figures that are collected through some type of measurement process.[cite: 1]

Information

The result of analyzing data; that is, extracting meaning from data to support evaluation and decision-making.[cite: 1]

INTERNAL SOURCES

Inside the organization[cite: 1]

EXTERNAL SOURCES

Outside the organization[cite: 1]

Big data

refers to massive amounts of business data from a wide variety of sources/[cite: 1]

VOLUME

Amount of Data[cite: 1]

VARIETY

Diversity of Data[cite: 1]

VELOCITY

Speed of Data Generation[cite: 1]

VALUE

Worth of Data[cite: 1]

VERACITY

Accuracy of Data[cite: 1]

Discrete data

consist of countable values.[cite: 1]

Continuous data

are measured and can take any value within a range, including decimals.[cite: 1]

Nominal

are grouped into categories or labels. The categories have no order or ranking[cite: 1]

Ordinal

consist of categories that have a meaningful order or ranking[cite: 1]

Ratio

Numerical data with equal intervals and a true zero[cite: 1]

Interval

Numerical data where the difference between values is meaningful, but there is no true zero[cite: 1]

Little data

describes the data segments or files that help individual businesses keep track of customers.[cite: 2]

BUSINESS ANALYTICS

refers to a broad use of various quantitative techniques such as statistics, data mining, optimization tools, and simulation supported by the query and reporting mechanism to assist decision makers in making more informed decisions within a closed-loop framework seeking continuous process improvement through monitoring and learning (Min, 2017).[cite: 2]

Step 1: Defining the business needs

The first stage in the business analytics process involves understanding what the business would like to improve on or the problem it wants to solved.

Step 2: Explore the data.

This stage involves clear computations for the data, making data, removing outliers, and transforming combinations of variables to form new variables.

Step 3: Analyze the data

At this stage, using statistical analysis methods such as correlation analysis and hypothesis testing, the analyst will find all that are related to the target variable.

Step 4: Predict what is likely to happen.

At this stage, the analyst will model the data using predictive techniques that include cecision trees, neural networks and logistic regression.

Step 5: Optimize (find the best solution).

At this stage, the analyst will apply the predictive model coefficients and outcomes to run 'what-if" scenarios, using targets set by managers to determine the best solution with the given constraints and limitations.

Step 6: Make a decision and measure the outcome.

The analyst will then make decisions and take action based on the derived insights from the model and the organizational goals.

Step 7: Update the system with the results of the decision.

the results of the decision and action and the new insights derived from the model are ecorded and updated into the database.