How do you clean data on the fly?

The more specific the task, the better, as attention is a crucial skill. Only after that can you consider optimizing a curator based on their expertise. Otherwise, cleaning data on a broad topic can lead to fatigue and yield less accurate results.

Ad hoc data requests often lack clarity, coming from stakeholders who are unfamiliar with data sources, reliability, and even availability. To address this, I make sure I understand the end-goal or pain point before I list down the relevant questions that will guide me in the following steps. Next, I identify the necessary attributes while fighting off the urge to go crazy with data exploration (I take note of ideas that come to me but are not relevant to the topic at hand). After a basic data cleaning (e.g. changing data types, replacing blank with null), I visualize them using simple charts so that I can easily spot discrepancies and inconsistencies. Then I go back to the query to continue the necessary cleaning and modelling.

One of my favorite ways to start paring down the data set is to ask the end users what they really care about. Asking "If you couldn't have everything in this file, what would you absolutely want to keep?" It's simplistic, high-level, and focuses attention on what's important.

Understanding the data, is paramount before you run into any processing. Make sure you understand their content, sources, and inherent characteristics, and what the 'information' really is in relation to your task. I consider this to be the foundation for gaining meaningful insights.

As per my experience, we should never rush to output tables. First analyze the collected data using some basic edit checks and ensure it accuracy.

Effective real-time data cleaning relies on understanding your data's details. Depending on factors like data source, format, size, and complexity, the tools you pick are crucial. For easier jobs, tools like Excel or Google Sheets work well, while for more complex tasks, Python and R give you flexibility. These let you make custom changes to data. When choosing a tool—Excel, Tableau, Python or Databricks—think about what you need and what you're good at. Knowing how each tool works helps you use them better. Matching the tool with your data's needs and your skills lets you clean data well and get useful insights.

As is the case with anything you do on a (somewhat) regular basis streamlining your process is key to success. When working with worksheet-based tools (MS Excel, Google Sheets, Apple Numbers, etc.) you can create a template that does the work for you by running formulas that sort, organize, and summarize your data in order to identify trends showing inconsistencies or incomplete data that needs to be cleaned. Similarly, reusing custom code blocks or functions in Python and R could help perform analysis and fix data in no time.

Use Excel / Google sheets. It allows you to understand what is present in the Hay Stack of Data. If you can't work on such a huge data set, at least it would give us insight on the type of data we need to work on. Else we need to start using R & Python to define advanced criteria.

I think the best tool is Python or R. This gave freedom and power to investigate data , manipulate data and visualize data. There are a number of powerful libraries with the help of which we can manipulate data very easily.

If you will need to output your data via Power Bi, the “transform” button is going to be an extremely useful tool to check if your data is consistent I prefer to use PowerBi in tandem with Excel to figure out anything I missed in the cleanup If you don’t have too much time, load the tables and create basic visuals and click on refresh, Power BI is equipped to point out your data errors even beyond 100,000 entries on the fly

Initiating real-time data cleaning in Python begins with a vital step: inspecting the dataset to uncover anomalies or issues. This foundation is built through: 1. Summary Statistics: Python's Pandas and NumPy compute essential metrics - mean, median, etc. These insights into central tendencies and variations offer an understanding of the dataset's structure and potential outliers. 2. Data Visualization: Matplotlib and Seaborn create visualizations like graphs, histograms, spotlighting patterns, trends, and anomalies hidden in raw data. Meticulous execution of these Python-driven steps establishes a strong base for data inspection. This initial scrutiny uncovers anomalies for subsequent cleaning techniques, ensuring reliable analysis.

I also find it helpful to look at the 5-10 or so largest and smallest observations. I use SAS for data management. PROC UNIVARIATE is a great was to examine virtually all aspects of a variable. Admittedly, if you have a lot of variables, there may be information overload.

This stage of the process is often referred to as EDA (Exploratory Data Analysis) and it helps you identify outliers in the data and quickly zoom in onto areas of interest - numbers and data points that stand out and require additional attention, i.e. cleansing.

To clean data on the fly, promptly tackle identified anomalies: 1. Data Transformation: Use Python's Pandas to adjust types, fill gaps, and ensure desired formats. 2. Outlier Handling: Apply truncation or imputation to manage outliers, averting skewed analysis. 3. Data Deduplication: Address duplicates for accuracy and unbiased conclusions. 4. Standardization and Validation: Ensure uniformity and validate against rules. 5. Quality Checks: Run checks to confirm alignment with expectations. Real-time actions bolster data for reliable analysis, rectify anomalies, and set a robust foundation for insightful conclusions.

One must be very careful with list deletion, which is suggested above (deleting rows). If the data are not missing at random, significant bias can be introduced. Also, suppose your key variable, say an intervention variable, is binary and you have few ones relative to zeros. List deletion could be disastrous if several ones are deleted, even if MAR. I prefer multiple imputation if I have a relatively substantial set of control variables. As a warning, though, I ran an MI procedure on a dataset of about 2500 obs and 40 variables, and it took 50+ hours to run. That is a hurdle.

Data cleaning on the fly requires meticulous documentation. Methods include: Code Comments: Explain decisions within your code. Version Control: Use Git to track changes and history. Jupyter Notebooks: Combine code and explanations. Markdown: Add explanations in notebooks. Data Dictionaries: Define variable meanings. Transformation Logs: Detail applied changes. Visual Summaries: Create graphs for each step. Collaboration Tools: Utilize platforms for shared documentation. These techniques ensure clarity and facilitate future analysis.

For data and potential auditing and inspection of that; it is imperative that you maintain crisp concise steps in your process so that it is reproducible. Science is about reproducibility and cumulative results. Add comments, retain the metadata in its original form, and make sure that the lens that you are observing the data is also in a validated frame for whatever you are attempting to qualify.

For researchers, creating a clear high-level pipeline graph in your research log or diary will be very useful. Make sure you enlist the final processing steps, the changes you made, and why you made them. This will help you and others understand your work in the future.

In academic research, cleaning data on the fly means that cleaning and improving the data happens in real time as the data is being processed. This method has a distinct advantage. While cleaning data on the fly offers advantages in terms of accuracy and reliability, it is important to acknowledge its limitations. One potential limitation is the increased computational resources required for real-time data cleaning. This method may also introduce the risk of inadvertently removing or altering valuable data if not implemented carefully. Tools like Excel, Google Sheets, and even Notepad can help you complete some tasks quickly, while programming, such as CL coding, can be extremely beneficial.