Summary Readings for Analytics in Accounting & Financial Management

Analytics in
Accounting
Book/theory
summary




1

,Table of Contents
Week 1: introduction..........................................................................................................3
Watson Chapter 1: Managing Data...............................................................................................3
Watson Chapter 9: The Relational Model and Relational Algebra................................................6
Watson Chapter 10: SQL...............................................................................................................8
Week 2: Getting and extracting data..................................................................................9
Watson Chapter 13: XML: Managing Data Exchange....................................................................9
Watson Chapter 17: Text mining & natural language processing................................................10
Week 3: Data visualization...............................................................................................11
Watson Chapter 16: Data visualization.......................................................................................11
Wilke Chapter 2: Visualizing data: Mapping data onto aesthestics.............................................12
Wilke Chapter 3: Coordinate systems and axes..........................................................................13
Wilke Chapter 4: Color scales.....................................................................................................15
Wilke Chapter 5: Directory of visualizations...............................................................................17
Week 4: descriptive analytics – Exploratory data analysis................................................20
Wickham Chapter 7: Exploratory Data Analysis..........................................................................20
James et al. Chapter 8: Tree-Based methods..............................................................................24
James et al. 12.4 Clustering Models............................................................................................32
Week 5: Diagnostic analytics – Understanding why?........................................................35
Week 6: Predictive analytics – What might happen?........................................................46
Kolassa & Siemsen Chapter 5 – Time Series Decomposition..............................................51
Kolassa & Siemsen Chapter 6 – Exponential Smoothing....................................................53
Kolassa & Siemsen Chapter 7 – ARIMA Models.................................................................55




2

, Week 1: introduction
Watson Chapter 1: Managing Data
Features common to all data management systems:
- There is a storage medium
o Data are stored electronically in each case
- There is a structure for storing data
o The address book has labeled spaces for entering pertinent data
- Rapid data entry and retrieval
o A calendar is stored in date and time sequence so that the data space for any
appointment for a particular day can be found quickly
- Selection of data management system requires a trade-off decision
o Screen dimension versus the amount of data that can be seen without scrolling

Internal memory is small, fast and convenient
External memory is slower to reference and not always as convenient

Organizational data management:
Storage devices are organized for rapid data entry and retrieval
Selecting how and where to store organizational data frequently involved a trade-off

Different types of information systems:
- Transaction processing system (TPS)
o Collect and store data from routine transactions
- Management information system (MIS)
o Convert data from a TPS into information for planning, controlling and managing
an organization
- Decision support system (DSS)
o Support managerial decision making by providing models for processing and
analyzing data
- Business intelligence (BI)
o Gather, store and analyze data to improve decision making
- Online analytical processing (OLAP)
o Provide a multidimensional view of data
- Data mining (DM)
o Use of statistical analysis and AI techniques to identify hidden relationships in
data
- Machine learning (ML)
o Using software to make decisions or
recommendations traditionally made by
humans

The information system cycle:

3

, Decision making, or preparing for the future, is the central activity of modern organizations.
Their success, and their organizations as well, depends on the quality of their decisions




Desirable attributes of data:

A data management system
for maintaining an
organization’s memory
supports transaction
processing, remembering the
past, and decision making. Its
contents must be shareable,
secure, and accurate. Ideally,
the clients of a data
management system must be
able to get timely and relevant
data when and where required.

A major challenge for data management professionals is to create data management
systems that meet these criteria. Unfortunately, some existing systems fail in this regard,
though we can understand some of the reasons why by reviewing the components of
existing organizational memory systems.

Components of organizational memory:
Data managers have a particular need to understand the
different forms of organizational memory because their
activities often influence a number of the components.

People -> Linchpin of an organizations memory. They
recall prior decisions and business actions. Create,
maintain, evolve and use data management systems.
Each person has a role and position in the hierarchy.
Both devices for remembering how the organization
functions and how to process data.

Tables -> accounting makes frequent use of tables. Rapid
searching is one of the prime advantages of a table.

Images -> are used as evidence and are widely used for identification and security. Also for
advertising and promotional campaigns or for selling.

Problems with organizational data management systems:

4