Wie lernen Sie aus dem Feedback bei der Bereinigung von Daten?

Before initiating the data cleaning process, it's essential to have a comprehensive understanding of its source, collection methodology, and contextual meaning. Thoroughly review accompanying documentation, metadata, and the credibility of data origins. Validate the data's accuracy, consistency, and relevance to your analysis objectives. If inconsistencies or gaps are identified, take steps to rectify them or meticulously document them for future reference. This meticulous approach lays a strong foundation for effective data cleaning and meaningful analysis.

One thing I've learned as Financial Analyst is that no Data is perfect. However, the worst kind of Data to deal with is when multiple people are contributing, in segregation or collusion. In such cases, even with data validation rules, naming conventions, Data Key enforcement, you will still end up with unclean data. The problem becomes worse when multiple data sources with large numbers contributing. Clear Data management policies and training is needed to overcome this issues and constant feedback loop between the Analyst and the sources

Business acumen is also important to validate data without biases or misunderstanding. It will drive you to define which data is relevant for your analysis

Understanding data collection process is very important. Let's discuss few scenarios: 1. If certain features are actually captured in data system manually by a data entry operator then there is a high chance of human error in those features compared to features for which data collection is automated. 2. When a data entry operator is entering the data manually he/she will only observe due diligence in the mandatory fields so data quality issues will be higher in the optional fields that he/she can actually skip while collecting the data. Hence, understanding of data collection helps in deciding data treatment steps accordingly.

Learning from feedback when cleaning and validating data sources is a crucial aspect of ensuring the quality and accuracy of the data used for various purposes. This iterative process involves refining data collection, transformation, and validation techniques based on the insights gained from feedback. Here's a breakdown of the steps involved in learning from feedback while cleaning data to validate data sources: 1. Initial Data Cleaning 2. Data Transformation 3. Validation and Quality Check 4. Feedback Collection 5. Root Cause Analysis 6. Adjusting Cleaning Procedures 7. Iterative Improvement 8. Documentation 9. Incorporating Automation 10. Collaboration and Communication

Validating your data sources before cleaning the data is a critical step in ensuring the accuracy and reliability of your analysis. 1. Data quality 2. Data Integrity 3. Identifying outliers 4. data consistency

In data cleaning, embracing a holistic approach is key. Understanding data sources, applying quality rules, rigorous testing, thorough documentation, and seeking external feedback are vital for mastering this crucial step in data analysis. Collaboration and continuous improvement through feedback are essential for refining data-cleaning skills and ensuring reliable, insightful outcomes.

A clear understanding of the entire process is essential. Before cleaning, it is essential understand the data collected in respect to their assigned data type and the implications of conversion. Always back-up, back-up, and back-up and then back-up the back-up :). It is essential to test for accuracy, consistency and completeness. As I tell my team, do not assume, interpret or circumvent. Document findings in detail, along with the entire clean-up process to ensure accountability. Communicate issues and explore avenues of improvement through teamwork!

It serves as the foundation for any meaningful analysis, ensuring accurate results and reliable insights. One essential tool that has proven to be invaluable in this process is visual data exploration. This practice involves the utilization of graphs, charts, and other visual aids to gain a deeper understanding of the dataset's structure, uncover patterns, and identify anomalies. Visual data exploration serves as a powerful feedback mechanism during the data-cleaning process. 1. Identifying Data Discrepancies 2. Detecting Missing Values 3. Validating Data Transformation 4. Exploring Relationships 5. Uncovering Data Integrity Issues 6. Assessing Data Balance 7. Refining Feature Selection 8. Monitoring Cleaning Progress

I personally feel that visualization is a self-validation step, especially descriptive statistics charts like box plots and histograms.the visualization provides instant feedback on the following: Are you receiving what was expected? What insights are currently received from the charts? Can a pattern be detected? This type of information allows me to know what type of analysis I should perform or what model is needed after the data cleaning.

Visual exploration of data and the initial exploratory data analysis (EDA) processes are vital steps before data cleaning for several reasons: - Understanding Data: It allows you to understand the structure, patterns, and potential outliers in your data, providing insights into what needs to be cleaned. - Identifying Issues: Visualization often reveals data anomalies, missing values, or inconsistencies that might go unnoticed initially. - Efficiency: By comprehending your data better upfront, you can plan your cleaning strategies more efficiently, saving time and resources. - Decision-Making: It aids in making informed decisions on which data cleaning techniques.

Exploring your data visually is a crucial step in data analysis that helps you gain insights, identify patterns, and uncover relationships that might not be immediately apparent through raw data.

Another method to get feedback when cleaning the data is by leveraging interactive visualisation tools such as Tableau, Power BI or various Python libraries. These tools not only help you visualise your data but help interact with it in real-time. This will help you to drill down on the data that you are working on, allowing you to identify and understand data anomalies more effectively.

Histogram, run charts, scatter plots are useful while understanding trends and patterns. We can notice and establish relationships between parameters by using these tools.

Exploring data visually and statistically saves countless hours later in the analysis process. Tools like PowerBI, Tableau gives you precursory visuals on how many null values, errors, etc. are present in the file. These are pre-built in the tools. I have always deferred to python functions like describe, shape, info to get an idea of basic statistics. This is also a quick way to see what categorical vs is non-categorial in your data sources and can prompt you to fix it.

There are some general rules for clean data, such as managing null values, duplicates, fuzzy matching, etc.| However, data quality rules should also adhere to business rules. Does the business have problems reporting because aggregations aren't happening correctly? -Find out why, and make it a priority data quality rule Is it because the account names are different, for duplicate entries? -Find out how to consolidate. Some of these things aren't obvious just by looking at the data. You'll always deliver great value by working with the business to solve their issues.

Quality has many dimensions and means different things to different users and producers. It is clear that quality is embedded in the entire data-creation value chain, and therefor, it is important to have accepted standards on how each step should be done. Do not try to correct data and improve on its quality at the time you start using the results.

Data quality serves as the bedrock of my visualizations, ensuring the data's integrity and significance. Errors, incompatible formats, inconsistent labels, and unmerged data compromise the essence. Through data quality, I guarantee that the information I work with attains its true meaning. Without it, data remains a mere collection devoid of purpose.

The following steps have helped us in increasing the data quality for our users Data Profiling: Conduct an initial assessment to understand structure, content and data quality Define Data Quality Rules: Ensure completeness, consistency, accuracy, uniqueness, and timeliness. Data Transformation: Perform type conversion, normalization, and enrichment. Implement Checks in ETL: Conduct in-flight validation during and after transformation. Monitoring and Reporting: Create dashboards and set up automated alerts for errors. Documentation and Governance: Maintain a rule catalog. Continuous Improvement: Conduct periodic reviews for rule adaptation Following these key steps have been helpful in maintaining and increasing the data quality

Effective data cleaning is a critical step in the data preparation process, ensuring that the data used for analysis or other purposes is accurate, consistent, and reliable. Data quality rules play a pivotal role in this process, helping to identify and rectify errors, inconsistencies, and inaccuracies within the dataset. Incorporating feedback into the application of these rules can significantly enhance the quality of the cleaned data. Feedback serves as a valuable resource in data cleaning, offering insights into the specific issues and challenges that exist within the dataset. This feedback can come from various sources, including end-users, domain experts, data analysts, and automated data quality tools.

Given data cleaning is an iterative process, proper documentation should be maintained and alignment with all stakeholders be done to ensure right post-cleaning rules are applied. A heavily manual process in earlier time can now benefit from gen-AI tools to setup the rules as well as code to automate this as much as possible.

Testing your data cleaning steps is a critical aspect of the data analysis process. It ensures that your cleaning procedures are effective, accurate, and reliable, which ultimately leads to more trustworthy and meaningful results. 1. Data quality assurance 2. improvements 3. feedback loop

From a data transparency standpoint, documenting the data cleaning process provides stakeholders with a clear view of how data was transformed.

Documenting the data cleaning process offers numerous benefits: Reproducibility: Ensures consistent results over time Transparency: Makes data preparation steps clear for validation Collaboration: Facilitates teamwork and understanding Troubleshooting: Helps identify and address data-related issues Knowledge Transfer: Aids onboarding of new team members Audit Trail: Records changes for compliance and accountability Decision Support: Provides context for future analyses Efficiency: Saves time by referencing documented procedures Training: Helps train newcomers effectively Data Governance: Aligns with data quality standards and governance policies, ensuring data integrity and reliability

The "why" of data cleaning should be clearly established at the get go. For example, should classification of households using consumption be abandoned in favour of classification based on living conditions? Should variables be dropped due to this? And if so why? These reasons must be documented accurately. It is important to maintain a clear and consistent reasoning while cleaning data, and recording every step of the process helps in making the process transparent. Additionally, documentation promotes collaboration and more importantly invites critiques from teammates that helps maintain data sanity and improves data quality.

The steps to cleaning a Data is dependent on the predicted outcome or the intend business questions the data will solve. Aside documenting, understanding the data is very important, you document what you can understand and present. It is important to understand the different data types, ask data related questiond, that will guide the cleaning process. Making assumptions are not realistic and still brings the data to an uncertain state, one common assumption in data cleaning is that outliers are data errors. However not all outliers are errors, in environmental data, extreme values can indicate important events like natural disasters such as earth quakes or record breaking weather conditions which are crucial data points.

Over the years, you get a sense of general issues with data quality (depending on the industry). Like whitespaces in strings, decimal notations (comma vs periods), delimiters etc. I have found that creating a generic data cleaning function in python or R which can address 90% of these common problems can come in handy. You have to modify/update this from time to time but most of the people in analytics deal with similar types of datasets and this practice can also be shared with your peers. All of my team in the past used SAP APO as our data source and our transactional data has similar problems which we were able to address with one common function in python.

If you ever have to make assumptions about data to make progress, it's always useful to communicate it to stakeholders and team members to get feedback and make sure the assumptions are right. I have found PACE method (plan, analyze, construct and execute) very useful and the emphasis is communication during planning phase so that you can take business context in your planning cycle.

Whilst cleaning the data we might come across 'edge-cases' that impact a small percentage (<1-2%) of the population. In such cases it is essential to take balanced call and avoid over-spending time while learning from it.

Before initiating the data cleaning process, it's essential to have a comprehensive understanding of its source, collection methodology, and contextual meaning. Thoroughly review accompanying documentation, metadata, and the credibility of data origins. Validate the data's accuracy, consistency, and relevance to your analysis objectives. If inconsistencies or gaps are identified, take steps to rectify them or meticulously document them for future reference. This meticulous approach lays a strong foundation for effective data cleaning and meaningful analysis.

I find it useful to have a hypothesis about what to expect from my experience and what I know about the real world that the data is representing. The most interesting parts of data cleaning are when one of my preconceived ideas are proven to be incorrect by the data and I get to learn something new. In the cases where the data is contrary to what I am expecting, it helps guide the correct data cleansing. A real world example of this from my work is that patients in our dataset are having 2.1 knee replacements per year on average, while humans only have 2 knees and knee replacements typically last 10 years. There was an error with the way the data was entered and this helped us correctly cleanse the data.

Learning from feedback when cleaning data is crucial to improving data quality and ensuring that your data analysis or data-driven decisions are accurate and reliable.

Feedback is not a critique of individuals or teams. Like all feedback, it highlights opportunities for improving data cleanliness, structure, and addressing errors. Clear communication and mutual understanding between supervisors and subordinates are essential for fostering a healthy work environment.

The cost-benefit analysis of data cleaning should consider the specific context and goals of the data science project. Here are few key considerations: Criticality of Data Accuracy: If the accuracy of the data is critical for the project's success, then investing more time and resources in data cleaning is justified Data Size vs. Quality: Assess the trade-off between data size and data quality. In some cases, it may be acceptable to sacrifice a small portion of the data to achieve higher quality Project Goals: The goals of the project should guide data cleaning decisions. If the project aims to build a high-stakes predictive model, rigorous data cleaning is essential. For exploratory analysis, the threshold for data cleaning may be lower

Always keep in mind the final objective of the exercise. If you are doing an ad-hoc analysis to recommend changes/strategy etc., directional outputs are more meaningful than precise results. I have been huge up on making the data pristine and losing valuable time in actually performing the analysis. Sometimes, you can document the outliers and call it out to stakeholders, rather than trying to let is skew your analysis.