Just as a dirty water pipe can contaminate the water that flows through it, so can dirty data pollute the insights derived from that data. To produce actionable insights, organizations must have a clean pipeline starting with accurate and timely data and consistent and controlled processes for cleansing, enriching, and governing that data.
Data quality issues can arise at any stage in the data pipeline. Poorly designed surveys can lead to inaccurate data collection. Inconsistent processes for coding and categorizing data can introduce errors. Lack of standardization across data sources makes joining or merging different datasets difficult. And finally, inadequate data governance can result in duplicate records, incorrect permissions, and other issues that prevent authorized users from accessing the data they need.
Data quality issues are not always easy to spot; sometimes, they only become apparent when trying to answer a specific question. However, by taking a proactive approach to data quality and finding the best observability tools—starting with a clean pipeline—organizations can avoid many of these problems and ensure that their data is fit for purpose.
Here are a few ways to ensure quality data.
When it comes to collecting data, it’s essential to use a source that you can trust. There are many FREE data sources (e.g., government websites, industry associations, etc.), but sometimes you have to pay for quality information. Don’t be afraid to spend money on a good research report or database subscription; it will be worth it in the long run.
Even the best sources aren’t perfect; sometimes, they contain inaccurate or outdated information. That’s why it’s crucial to filter your data before making any decisions based on it. There are a few different ways to do this, but one standard method is to compare the data against other sources and look for outliers—data points significantly different from the rest. If you come across an outlier, do additional research to determine whether it’s accurate; if not, discard it from your analysis.
Self-reported data—information people provide about themselves—is often biased and inaccurate. For example, people tend to over-report their income and under-report their weight because they want to appear wealthy and thin. As such, self-reported data should always be taken with a grain of salt and used in conjunction with other types of information (e.g., financial records) whenever possible.
Building a clean pipeline requires careful attention at every stage, from data collection to processing and storage. Here are some tips for ensuring quality at each stage:
- Survey design: When collecting primary data via surveys, it is essential to design the questions carefully to avoid biased responses. All inquiries should be unambiguous, with logically consistent response options. It is also necessary to test the survey on a small sample of respondents before deploying it widely.
- Data coding and categorization: Coding and categorization errors are familiar sources of aggregated data inaccuracies. It is essential to establish clear coding and categorization rules and follow them consistently when processing the data to avoid these errors. It is also helpful to have multiple people coding the same dataset so that any errors can be caught and corrected.
- Data Standardization: To join or merge different datasets, it is necessary first to standardize them to use the same codes, categories, etc. This process can be time-consuming, but it is essential for ensuring accuracy in the final dataset.
- Data governance: Finally, it is essential to implement governance rules to control data access and ensure its quality over time. These rules should specify who is responsible for maintaining the data, how often it should be refreshed, how changes should be documented, etc. By following these best practices, organizations can build a clean pipeline for their data—and ensure that the insights derived from that data are fit for purpose.
Organizations must have a clean pipeline with accurate and timely data while finding the best observability tools and consistent and controlled processes for cleansing, enriching, and governance. That said, before any actionable insights could happen, if companies want their strategy guided by quality insights instead of garbage in – garbage out methodologies, they would need close attention starting from stage one to storage.
Barry Lachey is a Professional Editor at Zobuz. Previously He has also worked for Moxly Sports and Network Resources “Joe Joe.” he is a graduate of the Kings College at the University of Thames Valley London. You can reach Barry via email or by phone.