Data Analysis Process: A Comprehensive Guide

In today's data-driven world, effective data analysis is crucial for making informed business decisions.

Explore the step by step process of data analysis as shared by Hanish Sanghrajka , importance of root cause analysis, power of asking SMART questions, and best practices for data ingestion - the tricks and tips that he follows to standardize his day to day life as a product manager.

He is confident "By understanding these concepts, one can unlock valuable insights and drive meaningful action within their organization."

The Six-Step Data Analysis Process

1. Ask

The first step in data analysis is to ask effective questions and collaborate with leaders and managers invested in the outcomes.

Asking the right questions helps open up the four quadrants of knowledge: what we know, what we don't know, what we know we don't know, and what we don't know we don't know.

Example:

• What do you suspect is a leading cause of dissatisfaction among new employees?

Root Cause Analysis: The Five Whys

In this phase, you can use the Five Whys technique to drill down to the root cause of issues. By repeatedly asking "why," you can uncover the underlying problem.

Example:

Problem: A manufacturing plant is experiencing a high number of defective products coming off the assembly line.

Why #1: Machines are malfunctioning - Defective products are coming off the assembly line. Why?

Answer: Because machines are malfunctioning during the production process.

Why #2: Machines are malfunctioning. Why?

Answer: Because they are not receiving proper maintenance.

Why #3: Machines are not receiving proper maintenance. Why?

Answer: Because maintenance technicians are overloaded and struggling to keep up with the workload.

Why #4: Maintenance technicians are overloaded. Why?

Answer: Because the plant recently increased production output without hiring additional maintenance staff.

Root Cause: The plant increased production without adjusting its maintenance capacity.

This analysis reveals that the root cause of the problem wasn't faulty machines themselves, but rather a lack of resources dedicated to keeping them running smoothly. The company can now address this by hiring more maintenance technicians or by adjusting production quotas.

Asking SMART questions

• Specific: A SMART question targets a particular issue or area of interest. It avoids being vague or open-ended.
• Measurable: A SMART question allows you to gauge progress or track results. Ideally, it should incorporate some way to quantify the answer.
• Attainable/Actionable: A SMART question focuses on something that can be realistically achieved or acted upon. It shouldn't aim for solutions that are completely out of reach.
• Relevant: A SMART question directly relates to the problem or situation at hand. It shouldn't go off on tangents or explore irrelevant areas.
• Time-bound: A SMART question has a specific time frame associated with it. This helps to establish deadlines and expectations for finding an answer or taking action.

Example Problem Statement:

Our company website is experiencing a high bounce rate (visitors leaving after viewing one page) and low conversion rates (visitors making a purchase).

Example SMART questions:

1. By what percentage can we reduce the bounce rate within the next 3 months?(Measurable & Time-bound)
2. How can we improve website navigation and user experience to make it easier for visitors to find the information they need within 2 clicks?(Attainable & Relevant)

It is important to have the questions open-ended which allows you to accurately qualify or disqualify potential solutions to the specific problem.

Don't ask leading questions that will lead to a particular response.

And definitely don't ask vague questions that are not specific or do not provide context

2. Prepare

Identify the data needed to achieve the successful results identified in the previous step. This involves preparing specific questions and determining the type of data required.

Example:

• Prepare specific questions about employee satisfaction with different business processes, such as hiring, onboarding, and overall compensation.

Data Ingestion Methods

During the preparation phase, it's crucial to decide on the best data ingestion method to gather the necessary information. There are two distinct methods:

Batch Ingestion: Collect data at regular intervals and load it in bulk. This method is cost-effective and efficient for processing large amounts of data.

Real-Time Ingestion: Capture and process data as it is generated, providing immediate availability for time-sensitive applications.

Tools for Data Ingestion:

• Apache Kafka: Real-time data streaming.
• Fivetran, Airbyte, Stitch: Simplifying ELT processes and ensuring data quality.

3. Process

Collect and use data ethically, ensuring it is stored, managed, and protected properly. Implement access controls and aggregate data without revealing individual identities.

Example:

• Use anonymized data to protect employee privacy while analyzing satisfaction trends.

ETL vs. ELT

Decide whether to use ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) based on the needs of your analysis.

ETL: Extracts data, transforms it into a usable format, and then loads it into an analytics platform.

Use Case: Ideal for systems where data transformation needs to be completed before analysis, such as in traditional data warehousing.

ELT: Extracts data in its raw format, loads it into the analytics platform, and then transforms it.

Use Case: Suitable for modern data architectures like data lakes where raw data is ingested and transformed within the storage system.

Why is it important?:

• Efficiency: Automating data pipelines reduces manual effort.
• Scalability: ELT processes leverage the power of modern data platforms to handle large data volumes efficiently.
• Flexibility: ETL is beneficial for predefined, structured data needs, whereas ELT offers flexibility for dynamic and evolving data requirements.

4. Analyze

Find trends and patterns in the data that point to specific conclusions.

Example:

• Identify that employees with poor onboarding experiences are more likely to leave the organization.

Data Standardization Techniques

During the analysis phase, standardizing data ensures accuracy and consistency, making it more useful for analysis.

Key Techniques:

Data Cleaning:

• Purpose: Remove duplicates, correct errors, and fill in missing values.
• Example: Ensuring all dates are in the same format, correcting misspelled entries, and removing redundant records.

Data Transformation:

• Purpose: Convert data into a common format.
• Example: Standardizing units of measurement (e.g., converting all lengths to meters), normalizing categorical values (e.g., using consistent terms for job titles).

Data Matching:

• Purpose: Identify and merge records that refer to the same entity but are represented differently across data sources.
• Example: Consolidating customer information from different databases where the same customer may have different IDs.

Data Enrichment:

• Purpose: Enhance data quality by adding additional context or metadata.
• Example: Adding geographic coordinates to addresses or appending demographic information to customer records.

Validation Rules:

• Purpose: Implement rules and checks to ensure data adheres to predefined standards and formats.
• Example: Ensuring all email addresses follow the correct format, all phone numbers have the required number of digits.

5. Share

Communicate the results with stakeholders and managers, providing the full picture with the right context.

Example:

• Present findings to HR and suggest improvements in the onboarding process.

Articulating Findings

For visualising data, understand the data, the target audience and the objective of visualising the data. Effective storytelling involves articulating findings through descriptive narratives and contextualizing insights. Not all data insights require visualization; some can be effectively communicated through detailed explanations and context.

Textual Data Analysis:

• Sentiment Analysis: Understand customer sentiment through reviews or social media comments.

Example Tools: Python, Google Cloud Natural Language API.

• Topic Modeling: Cluster similar documents based on content.

Example Tools: Python libraries like Gensim.

• Qualitative Research: Summarize interview transcripts and survey results to identify common themes or patterns.

Example Tools: Using tools like NVivo to analyze qualitative data

6. Act

Work with leaders within the company to implement changes and take actions based on the findings by evaluating recommendations.

Example:

• Create a standardized onboarding toolkit for new employees.

Key Actions:

1. Evaluate Recommendations: Collaborate with relevant departments to assess the feasibility and impact of proposed changes. Also, prioritize actions based on their potential to address the identified issues effectively.
2. Implement Changes: Develop a detailed action plan with clear timelines, responsibilities, and milestones. Follow on to communicate the plan to all stakeholders and ensure alignment.
3. Monitor and Adjust: Continuously monitor the impact of implemented changes. Ensure to be prepared to make adjustments based on feedback and new data insights.

Conclusion

Effective data analysis involves asking the right questions, preparing and processing data ethically, analyzing trends, sharing insights, and taking action based on findings. By integrating root cause analysis, SMART questions, proper data ingestion and standardization techniques, and effective communication of findings into the six-step process, organizations can make better-informed decisions and drive positive outcomes.

What challenges have you faced in data analysis, and how have these steps helped you overcome them? Share your stories and let’s discuss how we can leverage data more effectively.