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data
raw figures and captured facts, such as categories, measures, and calculations
information
knowledge gained from data that is relevant for analysis purposes
data analytics
the process of analyzing raw data to answer questions or provide insights
self-service business intelligence (SSBI) software
easy-to-use, accessible software that can prepare data, analyze data, and report results
provides extended data processing capabilities for preparing data, analyzing data, and reporting data analysis results
easy to use
two key features of SSBI software
null value
an unknown or missing value in a data set
data visualization
graphical representation of information and data to provide meaning and insights during the data analysis process
dashboard
a graphical user interface that shows key performance indicators for an organization; useful tool to communication key information to everyone who needs it
plan
analyze
report
three stages in the data analysis process
understand motivation
determine the objective
design the data and analysis strategy
three steps in the planning stage of the data analysis process
motivation
the reason or stimulus for performing data analysis; the “why” behind a project
external motivation
the project originates from a request or requirement by another party, such as external stakeholders (investors, creditors, etc.)
internal motivation
the project originates by a desire to better serve a client, better understand phenomena to gain business intelligence, or to perform job responsibilities; the incremental information gained is believed to outweigh potential costs involved with performing the data analyses
objective
the goal of a data analytics project; a statement that details what the project will accomplish
determine the data necessary to answer questions
decide what type of analysis is appropriate considering both the data and those questions
two aspects involved in developing a strategy for the planning stage
internal data
external data
two categories of data
internal data
data generated within an organization, such as sales and customer data; more easily controlled and verified by an organization
external data
data that are acquired from outside an organization, such as weather or social media data; somewhat riskier to use since we often cannot know if the data are accurate or complete; can provide more insights that internal data alone cannot provide
descriptive
diagnostic
predictive
prescriptive
four types of data analysis methods
descriptive analytics
data analysis method designed to understand and investigate what is currently happening or what has happened in the past; most common and easily understood analytics method; first analytics method performed to help understand data; examples include - sum, count, average, median, standard deviation
diagnostic analytics
data analysis method designed to understand and reveal why something has happened; examples include - anomaly and outlier detection, trend analysis, pattern recognition
predictive analysis
data analysis method that helps understand and predict what is likely to happen in the future; method uses data, statistical algorithms, and machine learning to identify the likelihood of future outcomes based on historical data; examples include - forecasting, regression analysis, time-series analysis
prescriptive analysis
data analysis method that determines the best course of action to achieve a goal in a specific scenario; helps understand what should happen to meet goals and objectives; these analyses recommend one or more possible courses of action; examples include optimization and what-if analyses
prepare data (ETL)
build information models
explore the data
three steps in the analysis stage of the data analysis process
extract-transform-load (ETL)
the process of retrieving raw data from a source, cleaning, restructuring, and/or integrating them with other data, and then loading the data into software for analysis purposes
the process by which data is retrieved from a source - could be downloading an Excel file or obtaining data from a database
what does extracting in the ETL process refer to?
the process by which data are cleaned, restructured, and/or integrated with other data prior to using it for analysis
what does transforming in the ETL process refer to?
data profiling
process of reviewing the data for possible issues
the process of importing transformed data into the software used to perform analyses; many types of analysis software include Excel and Power BI
what does loading in the ETL process refer to?
building information models
the creation of information needed for analysis purposes, starting from the data collected; examples include - calculations for net income, profit margins, total assets, etc.
identify patterns, trends, or unusual observations; lets one discover, question, and investigate data relationships to successfully execute data analysis objectives
what does exploring data in the analysis stage refer to?
interpret results
communicate results
two steps in the reporting stage of the data analysis process
the process of reviewing analyses to be sure they make sense based on the project’s objective and that the results are valid and reliable
what does data analysis interpretation in the reporting stage refer to?
oral presentations
written reports
dashboards
data visualizations
different ways in which the results of a data analysis project can be communicated
data analytics mindset
the professional habit of critically thinking through the planning, analysis, and reporting of data analysis results before making and communicating a professional choice or decision; minimizes the risk of biased or subjective thoughts; businesses can make decisions based on evidence rather than assumptions; individuals must focus on developing skills such as critical thinking, data literacy, technological agility, and communication skills
ask the right questions
extract, transform, and load relevant data
apply appropriate data analytics techniques
interpret and share the results with stakeholders
a data analytics mindset includes the abilities to …
critical thinking
data literacy
technological agility
communication skills
four skills in developing a data analytics mindset
critical thinking
disciplined reasoning used to investigate, understand, and evaluate an event, opportunity, or issue; the foundation of a data analytics mindset
reasoning
the human process of logically forming conclusions, judgements, or inferences from facts
data literacy
the ability to read, write, and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use of case application and resulting value
technological agility
an awareness of the latest technological developments and a willingness to work with new tools and try new things; the ability to quickly and smoothy adapt to or integrate current technologies with newer, different, disruptive, expansive, or convergent technologies
communication skills
being able to concisely, coherently share results to multiple audiences; involves writing clear and effective memos and reports, preparing successful presentations, creating meaningful data visualizations for analysis, and telling compelling data stories
stakeholders
purpose
alternatives
risks
knowledge
self-reflection
six elements of critical thinking when performing data analytics
stakeholders
individuals or groups who may be impacted by and/or have an interest in the outcome of a data analysis project
internal stakeholders
individuals or groups involved in a business’ operations who may be impact by and/or have an interest in the outcome of a data analysis project; includes an organization’s managers and employees
external stakeholders
individuals or groups outside a company who may be impacted by and/or have an interest in the outcome of a data analysis project; includes investors, creditors, regulators, etc.
the main reason for an analysis; knowing the reason for data analyses and articulating specific questions maintains focus on the data and analyses steps that will achieve objectives; clarifies questions, goals, or issues
identifying the purpose in critical thinking refers to
single system
multiple system
no system
three types of questions
single system questions
type of question that employs evidence and reasoning within a specific domain; you can reach a correct answer
multiple systems
type of question that employs evidence and reasoning across several domains; you can reach an informed judgment, but there are better and worse answers
no system
type of question that requires stating a subjective preference; cannot assess the answer
alternatives
different options that are considered and ranked based on the objectives and goals of the data analysis
obtaining information to inform your reasoning
seek out diverse or opposing views
identify the system(s), concepts, and theories that may apply
what does considering the alternatives in critical thinking involve?
risk
obstacles and challenges to our thinking or analyses
data risk
analysis risk
assumptions risk
biases risk
four types of risk
data risk
risk that involves choosing inappropriate data, or data that are incomplete or incorrect
analysis risk
risk that involves choosing an inappropriate type of analysis, or apply a data analysis method incorrectly
assumptions risk
risk that involves not understanding or evaluating assumptions about the data or results
biases risk
risk that involves unconscious and mental shortcuts that can affect decisions; closed-minded
knowledge
the concepts that add meaning to the data and analyses
recognizing when more knowledge is necessary to complete the analysis
acquiring knowledge from a reliable source
learning how to correctly apply it to data analysis
identifying knowledge gaps in critical thinking includes
self-reflection
a review of what worked and what did not
asking “did you have control and resources needed to make progress”
identifying assumptions
degree of randomness vs predictability
refining questions, goals, or issues
developing findings or recommendations
considering the implications
performing self-reflection in critical thinking includes
clear
fair
logical
actionable
relevant
critical thinking - decisions that are:
volume
variety
velocity
veracity
value
five v’s of data
data volume
the amount of data selected for an analysis project
cloud data services
a popular solution for storing high volume data; these services provide secure data storage on large servers using the organization’s own resources or third-party resources
easily expandible data storage capacities
expert data management
secure data, hardware, and software
IT expertise and advice
benefits of cloud data services
private
public
hybrid
three types of cloud data services
private clouds
data clouds with restricted access that store the data for one organization or shared between an agreed-upon group of organizations; often include a single large company, a group of supply chain partners, or non-competing independent companies from different industries
offers high data privacy and data security
offers high data interactivity
more customization and adaptability are possible
benefits of data storage for a single organization
can be the most expensive option of cloud data services
may be difficult to manage if developed in-house without hiring staff with cloud expertise
costs of data storage for a single organization
offers high data privacy and data security
offers high data interactivity between supply chain partners
more customization and adaptability are possible
sharing data storage makes it a less expensive option for each company
benefits of data storage that is shared by a set group of non-competing organizations
can have some restrictions on customization and adaptability
may involve shared contracts and joint revisions
costs of data storage that is shared by a set group of non-competing organizations
public clouds
data clouds that securely store data from multiple companies on shared servers using virtual server data separators; shared by many organizations who can easily come and go
lowest individual company cost
fastest implementation
most appropriate for small businesses
benefits of public clouds
least adaptable or customizable cloud data service
cost of public clouds
hybrid clouds
data clouds that offer both private and public cloud data storage, serving the needs of the security and use characteristics of the data involved; allow organizations to choose which data subset is best stored in a private cloud, while storing the rest in public clouds for cost efficiencies
data variety
the diversity of data structures and measurement scales in data that are useful for analysis
data velocity
the speed at which new data points are generated
data veracity
the reliability, or the integrity of the data used for analysis
whether the data will likely provide insights for the analysis objective, either by itself or as a component in an informational model, such as a net income calculation
how accurate the data must be to generate useful insights for the objective
how complete the data must be to generate useful insights of the objective
three things data veracity depends upon
data value
the benefits of certain data given the objective of a data analysis project
data mining
the systematic process/practice of looking for issues or patterns occurring within or between data fields; lets us better understand data behavior and test expectations about data values, patterns, or relationships; most common form of data analytics
blockchain technologies
new online technology that creates a single, shared record of transactions between parties
smart contracts
secure, online shared digital contracts; specific and popular application of blockchain technology; allow business contracts to be securely negotiated, settled, and signed online in a fraction of the traditional time
robotic process automation (RPA)
set of software technologies that record a specific order of human keystrokes and mouse activity steps within or across digital applications; used for stable processes where the steps of the process do not change over time; once saved or recorded, these routines can be repeated by simply rerunning the tool
process mining tools
tools that measure the timing and flow of data captured as business events occur; help businesses better evaluate process efficiency and effectiveness
continuous auditing technologies
programmed modules embedded in an organization’s AIS that evaluate transactions as they occur throughout the year; routine transactions are typically captured and tested in automated routines for consistent processing; results in more accurate assessment of risk and reduces audit workload at year end
textual analysis
the process of analyzing word choice in footnotes, investor communications, blogs, social media postings, and other documentation
textual mining
the process of transforming text into a format that can be used for analysis; evaluates word choice and usage to reveal insights about honesty, transparency, intent, and sentiments in communications by management, elected officials, and customers
cognitive technologies
artificial intelligence technologies that use algorithms that mimic the human thought process; can range from simplistic decision models to dynamic, complex models
data set
a collection of data columns and rows available for analysis
relational database
a collection of logically related data that can be retrieved, manipulated, and updated to meet users’ needs; structured tables composed of rows and columns
table (files)
how data related to an object of interest are stored and linked in a relational database; can be linked together with relationships
files
a database is a collection of …
rows and columns
tables are comprised of…
one record or instance of the entity
what does each row in a table represent?
records
data in the rows of the data set representing instances of the phenomena being captured; represent the collection of columns that hold the descriptions of a single occurrence of the data set’s purpose
instance
a specific, unique representation of the entity; rows of a data set