Visual Analytics Exam 2

Preattentive Attributes

• features that can be used in a data visualization to reduce the cognitive load required to interpret it

  • Include color, shape, size, length

Data Ink

the ink used in a table or chart that is necessary to convey the meaning of the data to the audience

Data-Ink Ratio

the proportion of ink used for data to the total amount of ink in a table or chart (high ratio is good)

Decluttering

• the process of increasing the data-ink ratio in a chart

Table Design Principles

• Keep the data-ink ratio high

• Use lines only to separate labels from data and calculated fields

• Labels should be left-aligned

• Values should be right-aligned

• Center vertical labels

Crosstabulation

a useful approach to describing a tabular summary of data for two variables

Pivot Table

a crosstabulation in Excel

Scatter Charts

• a graphical presentation of the relationships between two quantitative variables

Multiple Line Charts

Line Chart

Bar Charts

Stacked Column Chart

Clustered Column Chart

Pie Charts

usually inefficient use of preattentive attributes of color and size make it a poor choice for data visualization

Data Dashboards

• a data visualization tool that illustrates multiple KPIs and automatically updates as new data becomes available

KPIs

key performance indicators are metrics indicative of current operating characteristics

Raw Data

• data that has NOT been processed or prepared for analysis; also called source data or primary data

Data Wrangling

cleaning, transforming, and managing raw data so that it is more reliable and can be more easily accessed and used for analysis

Data Wrangling Objective

to produce a final dataset that is accurate, reliable, and accessible

Data Wrangling Activities

• Merging multiple data sources

• Identifying missing values

• Identifying erroneous values

• Deleting duplicate observations

• Identifying extreme values (outliers)

• Subsetting data

Data Wrangling 6 steps

Discovery, structuring, cleaning, enriching, validating, publishing

Structured Data

refers to data arrayed in a predetermined pattern to make them easy to manage and search (flat file is most common pattern)

Unstructured

data not arranged in a predetermined pattern

Semi-structured Data

not organized as structured data but contain elements that allow for isolating some raw data elements

Stacked Data

organized so that the values for each variable are stored in a single field

Unstacked data

are organized so that the values for one variable correspond to separate fields

Legitimately Missing Data

a value of a field is missing because of an appropriate reason

Illegitimately missing data and how to handle them

• 1. Discard records (rows) with any missing values

• 2. Discard any field with missing values

• 3. Fill in missing entries with estimated values

• 4. Apply a data-mining algorithm that can handle missing values

Data Dictionary

documenting several attributes should be included: names, definitions, and units of measure used in the fields, the raw data sources and relationship(s) with other data, and miscellaneous attributes

Internal Controls Definition

a process, implemented and managed by an entity’s board of directors, management, and other personnel, designed to provide reasonable assurance regarding the achievement of objectives in the following categories:

• Effectiveness and efficiency of operations

• Reliability of financial reporting

• Compliance with applicable laws and regulations

Risk

the potential for loss, damage or destruction of an asset as a result of a threat exploiting a vulnerability

Assessment Criteria of Risk

Likelihood (probability)

Impact (severity)

Likelihood (probability)

How likely is the event to happen

Impact (severity)

Assuming it happens, how bad is it?

4 Purposes of Internal Controls

1. Safeguard assets

2. Ensure reliable financial reporting

3. Promote operating efficiency

4. Encourage compliance with management directives (and the law)

Brown's Taxonomy- 4 Categories of Risk: a way to think about risk

1. Financial risk

2. Operational risk

3. Strategic risk

4. Hazard risk

can fit into multiple categories

Examples of Controls

Adequate documentation, background checks, firewalls, internal audits, password policies, job rotation, video surveillance, data encryption, computer backup

rows, records, obersvations

columns

field names

flat file

table

Discovery

become familiar with the raw data and conceptualize it use

Structuring

arrange the raw data to be more readily analyzed

cleaning

find and correct errors in the raw data that might distort the ensuring analyses

enriching

incorporate values from other data sets and/or apply transformations to portions of the existing data

validating

verify that the wrangled data are accurate, reliable and ready for the ensuing analysis

publishing

create a file containing the wrangled data and documentation and make it available to its intended users

Financial Risks

market risk, credit risk, liquidity risk

Operational Risks

systems risk, human error risk

Strategic Risks

legal and regulatory risks, business strategy risk

Hazard risk

directors and officers liability risk