What are the Do’s and Don’ts while analysing data

The realm of data analytics is vast and complex, often presenting challenges that can be overwhelming. To ensure success, it is crucial to have a clear roadmap
In this blog, we have curated a list of do's and don'ts to guide you. By adhering to these principles, you can optimize your data analysis, uncover valuable insights, and make informed decisions that propel your business forward. With a focused approach, you can navigate the sea of data, extract meaningful value, and drive innovation and growth. Let these guidelines serve as your compass as you embark on your data analysis journey.
Before proceeding, we kindly request you to explore another informative blog post on How to know your data? In this article, we delve into the process and ideology of data analysis, Additionally we discussed how to effectively analyse your data. We also covered a number of tools that can assist you throughout the analysis journey, enabling you to make the most of your data-driven endeavors.

Do’s

1. Test Output & Check Record Counts First

• After performing data cleaning or preprocessing, examine sample records to ensure they meet your expectations. This step helps you verify that the data is in the desired format and ready for analysis.
• Count the number of records in the table each time. Then compare it to the source data system to ensure no data is missed.

2. Know When To Stop Your Analysis

It can be tricky to know when to stop. There always remains the possibility that improvements can be made and it's possible you might become frozen in indecision as you try to gather more and more information.
So you must have some parameters to draw a line between analysis and analysis paralysis.

• Acceptable error rate - You might choose an acceptable error rate in advance and stop when you get there. However, you must also specify the real-world cost of model error. For instance, you may set a goal of obtaining at least 99.50% accurate data.
• Estimate the incremental value of additional analysis - The more time and thought you give to a problem, the more effective and perceptive your solution will be. This is true to a certain extent. Remember the 80/20 rule? which claims 80% of output is generated by 20% of work. So, try determining the value that will be generated if you perform a certain level of analysis.

3. Automating Tasks

Ever wondered? The scripts, tasks and queries that you run frequently can be automated. Yes, it can be done by creating a cron job or task scheduler.
‍By creating cron jobs or task schedulers, you can schedule the execution of these tasks at designated times without manual intervention. Automation saves time and effort, allowing you to focus on more complex and critical aspects of analysis. It also ensures consistency and reduces the risk of human error.

4. Document Your Process 

Maintaining proper documentation throughout the data analysis journey is crucial. Document the steps you take, the transformations applied to the data, and any insights or findings discovered. This documentation serves as a reference for future analysis and helps others understand your methodology and replicate your results if needed. Additionally, documentation enhances transparency and allows stakeholders to track the decision-making process and understand the reasoning behind the conclusions drawn.

5. Collaborate and Seek Feedback 

Data analysis is often a collaborative effort. Engage with colleagues, domain experts, or stakeholders to validate your findings and gain additional perspectives. Seek constructive feedback on your analysis methodology, assumptions, and interpretations. This collaborative approach fosters a more robust analysis process and ensures that  your conclusions are well-informed and reliable.
By following these do's to enhance data analysis, make informed decisions, derive meaningful insights, maintain balance, leverage automation, document, collaborate, and seek feedback for credibility and accuracy.

Don'ts

1. Don’t Get Lost in the Sea of Data

Define clear business objectives to avoid drowning in data, start by clearly defining the business objectives you want to achieve through analysis. Identify specific use cases and goals that align with your organization's strategic priorities. This focused approach will help you prioritize data and extract meaningful insights.

2. Don’t Start Without a Plan

Starting an analysis without a plan is like embarking on a journey without a map. Outline the steps, methodologies, and tools you will use to conduct your analysis. Begin by working with a small sample of data to test your techniques and validate their effectiveness. Once you have confidence in your approach, scale up to larger datasets.

3. Don’t Work With Messy Data

Data analysts get data from a variety of sources, including spreadsheets, SaaS apps, and cloud databases. This data is usually formatted in different ways.

Therefore, a data warehouse must be built to assure data consistency as creating a data warehouse increases the efficiency and ease of accessing data .

When I refer to "Data Warehousing," I simply mean creating a new table from many already-existing tables that include the required columns and rows necessary for performing analysis.

4. Don't Overlook Data Governance   

Data governance plays a vital role in data analytics. It involves establishing policies, processes, and standards for data management, ensuring data quality, privacy, and security. Neglecting data governance can lead to inaccurate insights, compromised data integrity, and legal or compliance issues. Implementing robust data governance practices is essential for successful and trustworthy analytics.

5. Don't Neglect Data Visualization

Data visualization is a powerful tool for presenting and interpreting data. Neglecting data visualization can hinder effective communication of insights. By visualizing data through charts, graphs, and interactive dashboards, analysts can convey complex information in a clear and intuitive manner, enabling stakeholders to understand and act upon the insights more easily.
By avoiding these common pitfalls in data analytics, you can enhance the accuracy, reliability, and effectiveness of your analysis. Stay focused on your objectives, plan ahead, maintain data quality, consider the context, communicate effectively, adhere to ethical standards, and document your work for transparency and reproducibility.

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