3 Jan 2017
School
Department
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Professor
INFO 2020 – Lecture 1
Business analytics: scientific process of transforming data into insight to making better
decisions
A Categorization of Analytical Methods and Models:
Descriptive analytics: encompasses the set of techniques that describes what has
happened in the past
o Data queries
o Reports
o Descriptive stats
o Data visualization
o Data-mining techniques
o Basic what-if spreadsheet models
Data query: a request for information with certain characteristics from a database
Data dashboards
Predictive analytics: consists of techniques that use models constructed from past data
to predict the future or ascertain the impact of one variable on another
o Survey data and past purchase behavior to predict the market share of a new
product
Techniques:
o Linear regression
o Time series analysis
o Data mining to find patterns or relationships
o Simulation: the use of probability and stats to construct a computer model to
study the impact of uncertainty on a decision
Prescriptive Analytics: indicates a best course of action to take
o Optimization models: models that give the best decision subject to constraints of
the situation
Portfolio models – finance – uses historical investment return data to
determine the mix of investments that yield the highest return
Supply network design models – operations
Price markdown models – retailing – uses historical data to yield revenue-
maximizing discount levels and the time of discount offers when goods
have not sold as planned
o Simulation optimization: combines the sue of probability and statistics to model
uncertainty with optimization techniques to find good decision in highly complex
and highly uncertain
Big Data: a set of data that cannot be managed, processed, or analyzed with commonly
available software in a reasonable amount of time
o Four Vs of Big Data:
Volume – terabytes to exabytes of existing data to process
Velocity – streaming data, milliseconds to seconds to respond
Variety – structured, unstructured, text, multimedia
Veracity – uncertainly due to data inconsistency and incompleteness,
ambiguities, latency, deception, model approximations
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