WGU D596 Task 1: The Data Analytics Life Cycle and Real-World Application | 2026 Update with
complete solutions.
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The Data Analytics Journey
A. The Seven Phases of the Data Analytics Life Cycle
The data analytics life cycle consists of seven structured phases that guide organizations from
identifying a business problem to implementing a data-driven solution. Each phase builds upon
the previous one to ensure accurate, relevant, and actionable outcomes.
1. Business Understanding
The first phase focuses on identifying and clearly defining the core business problem. This step
requires understanding the organization’s mission, objectives, and definition of success. Without
proper alignment with business goals, analytics efforts may produce results that are technically
correct but strategically irrelevant.
Example:
A hospital wants to reduce patient discharge wait times in order to improve patient satisfaction
and operational efficiency.
How to Gain Expertise:
Practice active listening during stakeholder meetings
Study the organization’s mission and strategic goals
Ask clarifying and outcome-focused questions
Review industry reports and case studies
Understanding organizational goals ensures analytics work supports broader objectives. For
example, if a hospital’s mission prioritizes patient care, the analysis must enhance care quality—
not simply reduce costs. Clear performance targets (e.g., discharging patients within four hours)
help define measurable success criteria and guide data collection.
, 2. Data Acquisition
This phase involves collecting relevant data from appropriate sources. Data may come from
internal databases, APIs, electronic health records, spreadsheets, or cloud platforms.
Example:
Extracting patient admission and discharge time logs from hospital information systems.
How to Gain Expertise:
Learn SQL for querying databases
Use Python for data extraction and automation
Understand APIs and web data retrieval
Gain familiarity with cloud platforms such as AWS or Azure
Proper acquisition ensures that collected data aligns with business objectives and includes all
relevant variables.
3. Data Cleaning
Raw data often contains missing values, inconsistencies, duplicates, or formatting errors.
Cleaning ensures accuracy, completeness, and usability.
Example:
Correcting or removing missing patient discharge timestamps.
How to Gain Expertise:
Use Python libraries such as Pandas
Apply data profiling techniques
Practice handling missing values and outliers
Use tools like OpenRefine
High-quality analysis depends on clean, reliable data.
4. Data Exploration
In this phase, analysts explore data visually and statistically to identify trends, patterns, and
anomalies.
Example:
Analyzing discharge delays by hospital department to identify bottlenecks.
complete solutions.
_______________________________________
_
The Data Analytics Journey
A. The Seven Phases of the Data Analytics Life Cycle
The data analytics life cycle consists of seven structured phases that guide organizations from
identifying a business problem to implementing a data-driven solution. Each phase builds upon
the previous one to ensure accurate, relevant, and actionable outcomes.
1. Business Understanding
The first phase focuses on identifying and clearly defining the core business problem. This step
requires understanding the organization’s mission, objectives, and definition of success. Without
proper alignment with business goals, analytics efforts may produce results that are technically
correct but strategically irrelevant.
Example:
A hospital wants to reduce patient discharge wait times in order to improve patient satisfaction
and operational efficiency.
How to Gain Expertise:
Practice active listening during stakeholder meetings
Study the organization’s mission and strategic goals
Ask clarifying and outcome-focused questions
Review industry reports and case studies
Understanding organizational goals ensures analytics work supports broader objectives. For
example, if a hospital’s mission prioritizes patient care, the analysis must enhance care quality—
not simply reduce costs. Clear performance targets (e.g., discharging patients within four hours)
help define measurable success criteria and guide data collection.
, 2. Data Acquisition
This phase involves collecting relevant data from appropriate sources. Data may come from
internal databases, APIs, electronic health records, spreadsheets, or cloud platforms.
Example:
Extracting patient admission and discharge time logs from hospital information systems.
How to Gain Expertise:
Learn SQL for querying databases
Use Python for data extraction and automation
Understand APIs and web data retrieval
Gain familiarity with cloud platforms such as AWS or Azure
Proper acquisition ensures that collected data aligns with business objectives and includes all
relevant variables.
3. Data Cleaning
Raw data often contains missing values, inconsistencies, duplicates, or formatting errors.
Cleaning ensures accuracy, completeness, and usability.
Example:
Correcting or removing missing patient discharge timestamps.
How to Gain Expertise:
Use Python libraries such as Pandas
Apply data profiling techniques
Practice handling missing values and outliers
Use tools like OpenRefine
High-quality analysis depends on clean, reliable data.
4. Data Exploration
In this phase, analysts explore data visually and statistically to identify trends, patterns, and
anomalies.
Example:
Analyzing discharge delays by hospital department to identify bottlenecks.
