D596 The Data Analytics Journey —Task 1 The Data Analytics Life Cycle and Real-World Application (20

D596 The Data Analytics Journey —Task 1 The Data Analytics Life Cycle and Real-World Application (2026-2027) Western Governors University Brennen Goode The Data Analytics Journey — D596 Task 1

The Data Analytics Life Cycle and Real-World Application A. The Seven Phases of the Data Analytics Life Cycle The data analytics life cycle includes seven important steps that help organizations go from identifying a problem to finding a data-driven solution.

1. Business Understanding This step focuses on identifying the real business problem. You need to understand the organization’s goals and what success looks like for the business you're working for.

Example: A hospital wants to reduce patient discharge wait times.

How to gain expertise: Practice active listening, learn about the business, and ask thoughtful questions. You can also gain insight by reading industry reports, attending stakeholder meetings, or working closely with business leaders.

2. Data Acquisition This is where data is collected from various sources like internal databases, APIs, or external files.

Example: Pulling patient time logs from hospital systems.

How to gain expertise: Learn SQL and Python for data retrieval. Familiarity with

APIs, web scraping, and cloud data platforms like AWS or Azure also helps.

3. Data Cleaning Data often comes with errors or missing values. Cleaning ensures your data is accurate and usable.

Example: Filling in or removing missing patient discharge times.

How to gain expertise: Use Python libraries like Pandas. You should also study data profiling techniques and tools like OpenRefine.

4. Data Exploration This step involves analyzing the data visually and statistically to discover patterns and trends.

Example: Visualizing delays in specific hospital departments.

How to gain expertise: Learn tools like Tableau or Seaborn. Also, understand how to use summary statistics and correlation analysis to spot significant trends.

5. Predictive Modeling You build models to predict future outcomes using machine learning.

Example: Predicting which patients will likely stay longer.

How to gain expertise: Practice with Scikit-learn or R. Completing online courses on supervised learning, logistic regression, and neural networks will deepen your knowledge.

6. Data Mining This involves deeper analysis to find hidden relationships in the data.

Example: Finding common paths of patients with long stays.

How to gain expertise: Learn clustering and classification methods. Techniques like association rule mining and dimensionality reduction are also key.

7. Reporting and Visualization The final step is to share your insights in a clear and actionable format.

Example: Building a dashboard showing average discharge times.

How to gain expertise: Use Power BI, Tableau, or Google Data Studio.

Understand the principles of data storytelling and audience-tailored design.

Understanding an organization’s goals and mission is essential for identifying the correct business requirements. The mission ensures your analysis supports the broader objectives of the organization. For example, if a hospital’s mission is focused on improving patient care, your analysis should aim to enhance that outcome, not just reduce costs. A clear understanding of the organization’s values and direction helps you concentrate on what truly matters. Knowing the specific goals also gives analysts a better idea of the actual business needs. For instance, if the hospital aims to discharge most patients within four hours, that sets a clear target. From there, you can determine what data to collect, which processes to examine, and where delays might be occurring.

Having that direction makes it easier to stay focused on solving the right problems.

B. Using Process Mining During the Data Exploration Phase Process mining is a tool used during the exploration phase. It helps visualize how processes actually happen in real time by analyzing them with event log data.

Decision-Making Process for Tool Selection If you have event logs with time-stamped data, process mining is ideal. Tools like Celonis or Disco are selected based on system compatibility, ease of use, cost, and data complexity. Analysts must also consider how well the tool integrates with the organization’s existing IT infrastructure.

Problem It Solves Hospitals often think they know why discharges are delayed, but process mining reveals the exact steps where delays occur. It uncovers bottlenecks, redundancies, and deviations from the intended workflow (Mans et al., 2015).

Justifying the Tool Research shows process mining improves hospital efficiency. Mans et al. (2015) demonstrated reduced patient wait times using process mining. Additionally, Partington et al. (2015) compared the workflows in Australian hospitals and showed that process mining revealed inefficiencies.

C. Legal and Ethical Risks of Process Mining Three Risks

● Privacy Issues: Patient data may be exposed if not properly handled. This risk compromises confidentiality and could lead to serious privacy breaches.

● Data Misinterpretation: Lack of clinical context can lead to incorrect conclusions and poor decision-making, affecting treatment and operational decisions.

● Tech Integration: Improper system integration can disrupt hospital operations, delay treatment processes, or expose sensitive data to breaches.

Legal and Ethical Problems Based on the Risks Above ● HIPAA Violations : If patient data is not adequately de-identified, it may result in violations of HIPAA regulations. This could lead to lawsuits, federal penalties, and loss of patient trust .

● Ethical Accountability : Drawing flawed conclusions from misinterpreted data may unjustly assign blame to staff or departments. This can damage morale and lead to poor decisions. Ethical analytics practices require understanding data limitations and contextualizing findings.

● Negligent System Use : If a poorly integrated system causes delays or failures in patient care, this can lead to ethical and legal accountability. Healthcare organizations have a duty of care, and failing to provide stable tech integration can lead to problems.

References Mans, R. S., van der Aalst, W. M. P., & Vanwersch, R. J. B. (2015). Process mining in healthcare: Evaluating and improving the patient process in real time. Journal of Biomedical Informatics, 57, 218–232. https://doi.org/10.1016/j.jbi.2015.06.007 Partington, A., Wynn, M. T., Suriadi, S., Ouyang, C., & ter Hofstede, A. H. M. (2015).

Process mining for clinical processes: A comparative analysis of four Australian hospitals. ACM Transactions on Management Information Systems, 5(4), 1–19. https://doi.org/10.1145/2629446