What are the best tools and techniques for data profiling and cleansing in PDM?

Data profiling scrutinizes database info to confirm its precision, fullness, uniformity, and dependability, aiding product teams in spotting data issues and understanding patterns for better governance. A key method is column statistic profiling, which evaluates data like sales figures to spot trends and anomalies. Another technique, data type and format profiling, ensures consistent formatting, such as making sure birthdates in healthcare records use a uniform date format. Challenges include handling vast, ever-changing data and merging information from various sources to ensure consistency and quality.

To keep product data in check, there are some specialised and handy tools that make a big difference. Informatica and Talend are awesome for catching errors and setting up rules to keep things consistent. IBM InfoSphere is great if you need to dig deep and find patterns in huge datasets. For quick cleanups, OpenRefine is perfect, especially if you're working with spreadsheets. And if duplicates are a headache, Data Ladder helps merge those entries fast. These tools are popular because clean data makes everything run smoother and leads to better decisions!!

Data profiling examines the structure, content, and metadata of data to understand its characteristics. It identifies issues like missing values, duplicates, and inconsistencies, crucial for ensuring data quality. This process assesses data dimensions such as completeness and accuracy. Profiling can be manual or automated using tools like SQL queries, data quality software, or specific tools like Talend Data Quality, Informatica, and IBM InfoSphere. These tools help streamline the profiling process and enhance data integrity.

I prefer Microsoft excel for data profiling because my dataset is not that big. Summary Functions: Excel provides various summary functions (e.g., COUNT, SUM, AVERAGE, MIN, MAX) that can give you basic statistics about a column of data. Data Sorting and Filtering: Sorting and filtering data in Excel can help you identify patterns and anomalies. Data Validation: Use Excel's data validation feature to set rules for data entry. This can help ensure that data adheres to predefined criteria. Conditional Formatting: Conditional formatting in Excel allows you to visually highlight certain conditions in your data. Charts and Graphs: Excel provides a variety of chart types that can help visualize data patterns.

The techniques that help me the most when profiling data are: 1. Column Analysis: Evaluate the characteristics of each column in a dataset, such as data type, value distribution, and frequency of values. 2. Uniqueness Analysis: Identify unique values in data columns to check for duplicates or redundancy. 3. Pattern Analysis: Detect patterns within the data to understand formatting rules and to identify anomalies. 4. Range Analysis: Determine the minimum and maximum values within columns to validate that data falls within expected ranges. 5. Relationship Analysis: Explore relationships between data columns to identify foreign keys and dependencies.

Interestingly, the growth of (generative) AI has allowed a whole new generation of tools to jump into this field. We see specific models (and companies) tackling specific niches in this field, focused purely on cleansing Product Data. An (short) list: - AICA DATA: a South African ML & GenAI company focused on efficiently cleansing and mapping data in the MRO field - YZR.ai: a French consulting firm and tech start-up focussing on (food) retail and FMCG All of these specific niche companies are agile and flexible, being able to use the most advanced models in the field.

In my experience, data cleansing is vital for interpreting open-ended survey responses accurately. For instance, when analyzing customer feedback on a new product, we might encounter varied spellings or slang for the same feature. By standardizing terms and correcting typos, we ensure that all mentions are captured correctly, revealing true customer sentiments. This cleaned data then guides product improvements, making sure we're truly listening to our users' voices.

Data cleansing is essential because it ensures the information we rely on for making decisions and understanding trends is accurate and trustworthy. When we clean up our data, we're basically tidying up the details that help us avoid mistakes and make smarter choices. This process is super important not just for keeping things running smoothly but also for making sure we meet industry standards and regulations. Plus, when data from different sources plays nicely together because it's been properly cleaned, it makes everyone's job easier and helps the whole organization work more efficiently. In short, taking the time to cleanse data means we can be more confident in the decisions we make, helping us stay competitive and effective.

Data cleansing improves the quality and usability of your data by correcting, standardizing, and enriching it. This process involves removing or replacing missing values, duplicates, and errors; applying consistent formats; and enriching data with information from external sources. Data cleansing can be performed manually or using automated tools such as data transformation software, data integration software, or specific data cleansing tools like Trifacta, OpenRefine, and Data Ladder. These tools help streamline the cleansing process, ensuring your data is accurate and reliable for analysis.

in my experience data cleansing involves correcting or removing inaccurate, incomplete, or irrelevant data from a dataset. we can use, 1. Standardization: Ensure data follows a consistent format (e.g., date formats, units of measure). 2. Deduplication: Identify and merge duplicate records to maintain data integrity. 3. Validation: Cross-check data against predefined rules or reference datasets to identify and correct errors. 4. Correction: Manually or automatically fix identified errors in the data. 5 .Enrichment: Enhance data quality by adding missing information from reliable external sources. to cleanse the data ensuring a better data quality at the end.

Example Data Cleansing Workflow: Set Data Quality Goals: Outline criteria like accuracy and completeness that define high-quality data for your business. Analyze Current Data: Use tools to automatically assess and report on issues like missing values and inconsistencies. Record Issues: Document all identified data problems and prepare summaries for team review. Clean Data: Correct inaccuracies, eliminate duplicates, and standardize data formats. Review Cleaned Data: Ensure all corrections meet your initial quality goals. Log Changes: Keep records of adjustments made and their effects on data quality. Regularly Update Processes: Adapt and repeat the workflow based on new data inputs and business needs.

1. **Define Data Quality Objectives**: Establish what standards your data needs to meet. 2. **Data Profiling**: Assess the current state of your data to identify quality issues. 3. **Document Findings**: Record the findings and issues discovered during profiling. 4. **Data Cleansing**: Address the identified issues to improve data quality. 5. **Validate and Monitor**: Check and continuously monitor the results to ensure data remains at the desired quality level. 6. **Track Changes**: Document changes and their impacts on the system. 7. **Repeat**: Regularly revisit these steps to adapt to new data or changes in existing data.

For the manual workflow - Frequency of data cleansing depends on how often data gets changed and the number of data stewards. These two parameters are inversely proportional to each other; if data gets changed very often then we need more data stewards to update the changes on the database and vice-versa.

Initial Assessment: Profile the data to understand its structure and quality issues. Define Rules and Standards: Establish data quality standards and validation rules based on profiling results. Data Cleansing: Apply cleansing techniques to correct identified issues. Re-Profiling: Profile the cleansed data to ensure issues have been resolved. Continuous Monitoring: Implement a process for ongoing data profiling and cleansing to maintain data quality over time. but these steps have are not a one time use operations or processes, one has to keep returning to them to ensure the profiling is continuous

1. **Plan and Scope**: Align your activities with your Product Data Management (PDM) goals and available resources. Define clear objectives for what needs to be achieved. 2. **Define Roles**: Clearly outline the roles and responsibilities of all stakeholders involved to ensure accountability and efficient workflow. 3. **Use Appropriate Tools**: Employ a mix of manual and automated tools and techniques tailored to the nature and size of your data. This helps in handling diverse data challenges more effectively. 4. **Document Processes**: Standardize and document your processes, methods, and rules. This ensures consistency and repeatability in your data management practices.

Following best practices ensures that these processes are effective, efficient, and contribute to overall data integrity. Here are some best practices for data profiling and cleansing: 1. Understand data sources and data lineage 2. Establish data quality metrics and success criteria 3. Implement data governance with validation rules and checks 4. Educate data users. Encourage data stewardship and responsibility 5. Document the decisions made during the process

Automation: Automate repetitive profiling and cleansing tasks to reduce errors and increase efficiency. Documentation: Document profiling and cleansing processes, rules, and standards for transparency and consistency. Collaboration: Involve stakeholders from different departments to ensure comprehensive data quality standards. Regular Audits: Conduct regular data quality audits to identify and address new data quality issues promptly. Scalability: Ensure that profiling and cleansing processes can scale with the growth of data volume and complexity. Training: Train staff on data quality tools and techniques to ensure they can effectively profile and cleanse data.

Data profiling and cleansing are like giving your data a health check-up and a spa day. Profiling lets you peek under the hood to see what's going on with your data—its shape, its quirks, and its little issues. Cleansing then steps in to tidy up the mess—fixing mistakes, getting rid of duplicates, and making sure everything is consistent. This isn't just about keeping your data neat; it's about making sure that when you make decisions based on this data, you're standing on solid ground. By keeping your data in top shape, you're setting up your projects for smoother runs and better outcomes.

Here are some benefits of data profiling and cleansing: - 1. Identifying Data Quality Issues 2. Facilitating Data Governance 3. Preventing Downstream Issues 4. Supporting Informed Decision-Making 5. Optimizing Resource Utilization 6. Enhancing Operational Efficiency

1. **Enhanced Reliability**: Improves the accuracy and reliability of your data, ensuring that decisions are based on the most accurate and current information available. 2. **Increased Consistency**: Standardizes data across sources, making it easier to integrate, analyze, and utilize effectively across various business functions. 3. **Improved Usability**: Cleansed data is easier to work with, increasing the usability for data analysis and reporting tasks. 4. **Error Reduction**: Reduces the risks associated with errors and inaccuracies, thereby minimizing potential losses or misinformed decisions based on faulty data.

Improved Data Quality: Higher accuracy, consistency, and reliability of data, leading to better decision-making. Efficiency Gains: Automated profiling and cleansing reduce manual effort and time spent on data preparation. Cost Savings: Reduces the costs associated with poor data quality, such as errors in analysis, compliance issues, and operational inefficiencies. Enhanced Compliance: Ensures data meets regulatory and internal compliance standards, reducing the risk of fines and penalties. Better Insights: High-quality data enables more accurate and actionable insights, driving business growth and innovation.

In my experience, using Excel for data preparation and cleaning, alongside PIM and DAM systems, works best. Excel is great for organizing and manipulating data, while PIM ensures consistency across channels, and DAM manages digital assets. This combo streamlines the process and boosts data quality.