AA 4306 Lecture Notes - Lecture 2: Dodge Viper, Data Deduplication, Analytic Hierarchy Process
Lecture 1 – Introduction to business analytics
Business analytics is the ability of firms to collect, analyse and act on data.
It is the ability to generate knowledge such as:
- What products customers want?
- What prices will customers pay?
- How many will each customer buy?
- Why do they buy? Why do they buy more?
- How do problems affect the bottom line?
- To predict demand, performance and problems.
- Identify opportunities, discover new segments.
It is the use of data, information technology, statistical analysis, quantitative methods
and mathematical or computer-based models, to helps managers make better decisions.
It is a combination of (STAP) skills, technologies, applications and processes used by
organisations to gain insight into their business based on DISQM-C to drive or guide business
planning.
Competing on Analytics
In order for organisations to compete on analytics, Davenport suggests they must have data,
personnel and procedures.
Where is the data in an organisation?
Operational systems:
They are used to run day to day business operations – also known as Transaction processing
systems.
It uses OLTP databases to store daily business transactions. OLTP is a computer system
where time-sensitive, transaction-related data is processed immediately and is always
kept current. It is used for order entry, financial transactions, and CRM and retail sales.
It is a class of information systems that facilitate and manage transaction-oriented
applications, typically for data entry and retrieval transaction processing.
Characteristics of data under this system:
- Transaction-oriented
- May be inconsistent and incomplete
- Volatile (changing continuously)
- Current.
Databases are everywhere.
- Efficient transaction processing
Automatic data capture
Easy retrieval
Data quality, errors, inconsistencies
- Automating business processes
Production, Marketing, Accounting, Sales
Databases are great, but
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- Too many of them
Everybody wanted 1 or 2 or more
Production, Marketing, Accounting, Sales
- Everybody got what is best for them
IBM, Oracle, Access, Excel, file drawers
- Created the problem databases were meant to solve
Inconsistent, incompatible, inaccessible data
As a result, data are not effectively employed and hence opportunities for improving
performance are missed and there are problems with analysing data to improve decisions and
performance.
Organisations need to store data for day to day operations. Data is being stored in many
databases (usually duplicating data across them, and usually incompatible with each other).
Organisations could make use of this data to improve the decision making process through
some sort of business analytics or business intelligence. This could be accomplished via some
sort of computer system(s).
What is decision making?
It is a thought process of selecting a logical choice from available options. It is a process of
identifying and selecting – solve a specific problem – choice making which is part of decision
making, i.e., select one option from a set of alternatives.
There are programmed decision (structured decisions for routine problems) and non-
programmed ones (unstructured decisions for unique and unusual problems – rational
decision making).
Simon (Nobel Lecture) on rational decision making in organisations:
“ We do know how the information processing system called Man, faced with complexity
beyond his ken, uses his information processing capacities to seek out alternatives, to
calculate consequences, to resolve uncertainties and thereby – sometimes, not always – to
find ways of action that are sufficient unto the day, that satisfice”.
Examples of analytics
Experiments
- test alternative strategies, product designs
- different interest rate v/s fees
- ad messages
- incentives (cash back v/s loyalty points)
Identify best customer segment
- for each product design, promotion
- identify optimum price
Simulate effect on financial performance
Decisions justified by data
- Compensation, rewards, advertising, pricing, R&D, mergers, acquisitions
- “In God we trust. All others bring data”
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- “Do we think this is true? Or do we know?”
- Measuring, testing and evaluating quantitative evidence (Internal and external evidence,
analysis)
- Companywide practice
- Gotaas-Larsen shipping corporation in late 1970s
It used a decision support system (DSS) for preparing and revising a 15 month operation plan
for its cargo ships. The model-driven subsystem supported cash flow and pro forma analyses
on a per ship, per voyage, per division and company-wide basis. The DSS helped users
simulate results. The computer system aggregated plans for individual feasible voyages to
help managers assess whether the overall plan would be effective.
- Port of Melbourne Authority – Lotus 123 spread sheet designed to simulate the arrival,
unloading and loading of cargo ships from the point (took >24 hours to run each time). It
helped to plan arrival times of ships and also helped to estimate when and where workers
needed to be (within various union guidelines).
What personnel (with the right skill sets) do we need?
Expert employees
Essential for running BA as a company-wide practice.
Expertise with numbers, or training.
Arm with best available data and best quantitative tools.
Better decisions (big and small, everyday; modelling, optimization, opportunities)
What procedures do we need?
Organizational practices
Multiple applications
- Not one “killer” application, all parts of the organisation
Centralised resource, easily shared
- Expertise, army of PhDs with quantitative, statistical, modelling, and optimisation
skills
- “bilingual experts” who understands business as well as analytics
- Integrated data from multiple sources, DW
Apply to entire supply chain
Some success stories
Business analytics in action at MARRIOTT
They introduced an application called the RPO (Retail pricing optimizer) to predict consumer
behaviour and optimise product availability and price to maximise revenue growths. The
RPO helped hotels pinpoint where opportunities are exactly. Data about their business trends
to their market conditions are complex data and BA simplify them to make good business
decisions. RPO helped determine the optimal, transient rate, using analytically-driven,
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Document Summary
Business analytics is the ability of firms to collect, analyse and act on data. It is the ability to generate knowledge such as: It is the use of data, information technology, statistical analysis, quantitative methods and mathematical or computer-based models, to helps managers make better decisions. It is a combination of (stap) skills, technologies, applications and processes used by organisations to gain insight into their business based on disqm-c to drive or guide business planning. In order for organisations to compete on analytics, davenport suggests they must have data, personnel and procedures. They are used to run day to day business operations also known as transaction processing systems. It uses oltp databases to store daily business transactions. Oltp is a computer system where time-sensitive, transaction-related data is processed immediately and is always kept current. It is used for order entry, financial transactions, and crm and retail sales.
