How do you report data cleaning for analysis?

I use something simple and yet useful, ie, creating an inventory of the data. This is particular useful when one is starting in working with data. A table with 4 columns 1. Data Source: list down all possible places it could have data (eg, email, accounting app, warehouse cabinet) 2. Data: what kind of data available in there (eg, invoice listing, customer confirmation, delivery order, stock card) 3. Structure: identify whether it's a structured, unstructured or semi-strcutured 4. Digial: indicate whether the data is in soft copy or hard copy. This will give ideas on what to do next. I hope this gives you some ideas on how to identify data sources, in a new perspective.

As a digital marketer, reporting data cleaning for analysis is crucial for ensuring the accuracy and reliability of insights derived from marketing campaigns. Here's how I approach reporting data cleaning, along with an example: Suppose I'm analyzing the performance of an email marketing campaign for a client. During the data cleaning process, I discover inconsistencies in the formatting of email addresses, leading to duplicate entries and inaccurate metrics. To address this issue, I implement a series of cleaning steps, including standardizing email formats, removing duplicates, and verifying email validity using regular expressions.

Data Sources: a. Primary Data: This dataset comprises information collected directly from our organization's internal systems and processes. It includes transactional records, customer interactions, and operational metrics. b. Secondary Data: Supplementary data from external sources such as public repositories, government databases, and industry reports have been integrated for enriching the analysis. These sources provide contextual information and benchmarking metrics.

Before embarking on data cleaning, it's crucial to document the origin, collection methods, and meaning of your data. This ensures clarity regarding the data's scope, limitations, and quality for both you and your audience. Depending on data complexity, formats, and audience preference, you can employ various documentation methods such as tables, diagrams, or narratives. For instance, a table can succinctly list data file names, descriptions, and locations, while a diagram can visually represent data flow and relationships. Additionally, a narrative can provide context and purpose behind data collection, aiding in comprehensive understanding.

The best method to report data cleaning for analysis involves documenting each step of the process clearly and in detail. This includes outlining the initial data issues identified, the criteria used for cleaning (such as removing duplicates or handling missing values), the tools or scripts employed, and the impact of these steps on the dataset. Additionally, I always provide before-and-after summaries or visualizations to illustrate the effectiveness of the cleaning process. This approach ensures transparency and reproducibility and builds trust in the analysis outcomes by showcasing the rigour and thoughtfulness applied in preparing the data.

After documenting your data's origins, it's essential to transparently report your data cleaning process, including the tools, methods, challenges, and decisions encountered. This enables both you and your audience to assess the validity and reliability of your data analysis outcomes. You can detail your data cleaning steps sequentially, utilizing code, screenshots, or text to suit the level of technicality and detail required. For instance, code snippets can demonstrate actions like duplicate removal, variable renaming, or missing value handling, while screenshots can depict data visualization or quality verification processes.

Throughout the data cleaning process, the key decisions involve handling missing values, removing duplicates, renaming variables for consistency, converting data types for accurate analysis, and dealing with outliers. Personally, visualizing the dataset after each major cleaning/manipulation step helps me understand it better. While cleaning data in Python, I use tools like pandas for data manipulation and seaborn/matplotlib for data visualization to assess and improve the dataset's quality. My aim is to ensure the reliability and validity of the subsequent data analysis.

Antes de iniciar a limpeza de dados, é fundamental fazer uma cópia dos dados originais. Durante o processo, documente cada etapa e explique as decisões tomadas, considerando possíveis vieses ou limitações. Esse registro transparente é essencial para garantir a confiabilidade e reprodutibilidade da limpeza de dados.

It is important to keep a duplicate of the original data before beginning the data cleaning process. Document each step taken to clean the data and the reason behind your decision, incude any bais or limitations in the data cleaning process This transparency helps ensure that the data cleaning process is trustworthy and reproducible.

In our data cleaning process, we meticulously review each dataset, identifying and addressing inconsistencies, missing values, and outliers. We employ various techniques such as imputation, outlier detection, and standardization to ensure data integrity. Through thorough documentation and validation checks, we guarantee the accuracy and reliability of our analysis-ready data, setting a solid foundation for insightful decision-making.

Finally, communicate the outcomes of your data cleaning efforts, highlighting the changes, improvements, and insights gained. This step is crucial for showcasing the value and impact of your data cleaning on subsequent analysis. Depending on the nature and significance of the results, you can present them quantitatively or qualitatively using tables, charts, or narratives. For instance, tables can illustrate reductions in data size or enhancements in accuracy, charts can visualize changes in data distribution or the emergence of patterns, while narratives can elucidate how research questions were addressed or project goals achieved through the cleaning process.

To highlight your data cleaning outcomes, you should contrast them with your original data sources and show how they changed. You should explain how your data cleaning process made your data ready for analysis, and how it enhanced its quality and fit for your purpose. You should also show how your data cleaning outcomes helped you answer your research questions or achieve your project goals, and what new insights or findings you gained from your data. For instance, you can use a data cleaning summary to display your data cleaning results, and how they supported your data analysis and insights.

When reporting data cleaning for analysis, it's important to highlight what happened during the cleaning. Show how you fixed things like missing values or strange numbers. Tell people how many problems you found and fixed. This helps others trust that the data is good for analysis. If there were challenges, explain how you dealt with them. By talking about these cleaning outcomes, you make sure everyone understands the improvements you made to the data, making it more reliable and suitable for analysis. This makes your analysis more trustworthy and clear for others to use.

When you clean data, show: 1. Before and after: What the data looked like originally and how it changed after cleaning. 2. Why it matters: How cleaning made the data easier to analyze and improved its quality for your specific use. 3. The results: How the cleaned data helped you answer your questions or achieve your goals. 4. New discoveries: What interesting things you learned from the data after cleaning it. Think of it like this: Imagine cleaning a messy room before inviting guests. You explain what the room looked like before (dirty clothes, scattered toys), what you did to clean it and why it's better now. You also share what you found while cleaning. This shows the whole process and the positive outcome.

When highlighting your data cleaning outcomes, think beyond the typical metrics. Share how integrating unstructured data (e.g., clinical notes) and using NLP improved patient stratification models, revealing previously hidden subgroups with specific risk profiles. For instance, transforming free-text physician notes into structured data may have unearthed critical insights about patient comorbidities, leading to more accurate predictive analytics. Visualize these findings with before-and-after accuracy charts or narrative case studies, demonstrating how these insights directly influenced patient care strategies and outcomes.

The reporting of data cleaning for analysis hinges on audience considerations. For a technical team, comprehensive insights into the process are crucial for potential future development. In such cases, detailing the entire procedure is beneficial. Conversely, a non-technical stakeholders may prioritize a more generalized overview. In either scenario, I would like focusing on key elements of identifying data sources as input and showcasing the outcomes of both data cleaning and analysis. The report should serve as a clear bridge between the raw data and the final insights. Additionally, for those requiring in-depth information, I include an appendix detailing the intricacies of the data cleaning process which can be valuable.

Consider documenting any assumptions made during the data cleaning process and their implications for the analysis. Include information on data validation techniques used to ensure the reliability of the cleaned dataset. Collaborate with stakeholders to validate the cleaned data and address any concerns or discrepancies.

Is this cleaning effective? Do I kept enough data for analysis? Am I inserting my own bias in this analysis? The cleaning process must remove the misfit data, but it should not be a personal decision, neither should it be allowed to interfere with analysis in a biased way.

Consider the importance of collaboration in data cleaning. Sharing experiences of overcoming specific challenges or leveraging unique solutions fosters a collective learning environment. Highlighting such insights encourages a culture of continuous improvement and ensures a more comprehensive understanding of data quality across the team.

In reporting data cleaning for analysis, it's crucial to document every step to ensure reproducibility and transparency. As a Data Analyst and BI Developer, I've learned that a meticulous description of the data cleaning process not only builds trust in the analysis but also serves as a valuable reference for future projects. By highlighting the outcomes, we demonstrate the impact of clean data on the reliability of insights, which is essential for data storytelling and business development. Effective communication of these steps can turn a routine task into a compelling part of the data narrative.