What are the most effective ways to optimize a product for data cleansing?

Embed a dashboard in your product for real-time data quality metrics like completeness and accuracy. Analysis on the Go: As data enters the system, it's checked against quality standards. Visual Alerts: Use color codes (e.g., green=high quality) to highlight areas needing attention. Team Collaboration: Members can flag issues or suggest improvements, creating a feedback loop. Data Trends: Show historical trends to pinpoint recurring issues or see improvements. Benefits: Spot errors instantly. Empower teams for proactive data quality upkeep. Ensure consistent understanding of data health.

While setting high data quality standards is important, being excessively stringent can sometimes hinder progress. It's crucial to strike a balance between maintaining high standards and being pragmatic. Not every piece of data needs to be perfect. Sometimes, 'good enough' data can help you move forward and make informed decisions faster.

Data cleansing is not just a one-time task, this is an ongoing task and takes a continuous effort to keep improving data throughout its lifecycle. Try to capture high-quality data first and ensure the data entered into the system is relevant. Standardise the data quality metrics and automate the transformation rules and validation checks. Don't indulge yourself in making everything perfect, important thing is to balance between data quality, practicality and resource expenditure.

Setting Data quality standards are definitely important and must have. Some other measures can be defining sources for data collection and setting process for collecting the same. Also setting certain rules, ex: how should missing data be treated will be also useful.

This can be achieved by automating and standardising a certain format while collecting the data and before ingestion of data, like standardising the numerical values to avoid the alphabet for the same field. However, these rules should not be like a stone line, refine the data validation rules based on real-world needs and usage. Set a feedback loop between users and consumers, this will improve data quality and errors in data. User feedback can help to identify issues that might be missed by automation and validation rules for ongoing data quality enhancements.

Setting data validation rules is definitely an important step in data cleansing. Including format validation and setting rules on how data should be treated when found not conforming to data or when it’s partial will not only help in data cleansing but also from security perspective.

Agree, automation not only helps in reducing time and also can introduce a systematic way of data cleansing and reduce manual errors. However as said above, this requires setting up clear processes and rules for handling all possible exceptions.

Yes, documenting always helps. This not only enables Product managers but developers too . New Developers will have a reference to understand why things are done the way it is done and apply improvements and/or modifications to address new cases that may arise

Agree. Although many techniques id Data cleansing is well known, with continuous evolving of data collection protocols, data sources , varieties and variation in data too is changing. Collaboration always helps to improve data cleansing process and in achieving better results. ML algorithms are anyways as good as they get the data.

Continuously improving data cleaning practices in product management is essential for maintaining data accuracy and making informed decisions. It involves regular auditing, validation, and cleaning of data sources to ensure they remain reliable. By investing in robust data cleaning processes, product managers can enhance the quality of their insights, reduce the risk of making decisions based on flawed data, and ultimately drive better product development and user experiences. This practice aligns with the broader trend of data-driven decision-making, which is becoming increasingly crucial in the world of product management.

When taking on a new product where data sets and data pipelines are already setup, spend time building an understanding of what steps have already been taken to ensure data accuracy and cleanliness. Bad data will always lead to bad outcomes and walking back decisions you've made or reverting changes are likely to have a higher cost than doing regular data auditing and inspection to make sure data capture and reporting is accurate. If you find flaws, it should validate your efforts to look for them rather than be used to presume that larger problems are amiss. Flaws and errors will happen. We audit and inspect to find and fix them to make better decisions.

Clean data is like a bedrock on which informed decisions stand, which may have repercussions or results. This has a direct impact on the reliability, effectiveness, and usability of data. Make your data the catalyst that eases the analysis, and insights and boosts the confidence of stakeholders to make clear decisions.