What are the common data quality pitfalls and mistakes to avoid when using GDS?

1 1. Inconsistent data sources

One of the main benefits of GDS is that it can connect to various data sources, such as Google Analytics, Google Sheets, BigQuery, and more. But this also means that you need to ensure that your data sources are consistent and compatible with each other. For example, if you use different time zones, currencies, or definitions for your metrics and dimensions, you may end up with inaccurate or misleading data in your reports. To avoid this, you should use the same data sources and settings across your GDS projects, or apply data transformations and calculations to harmonize them.

2 2. Missing or incomplete data

Another common data quality issue in GDS is missing or incomplete data. This can happen for various reasons, such as data collection errors, data processing delays, data source limitations, or user permissions. For example, if you use Google Analytics as a data source, you may encounter sampling issues, data freshness issues, or data access issues depending on your account settings and report configuration. To avoid this, you should check your data sources regularly for any errors or gaps, and use data blending, filters, parameters, or custom fields to fill in or exclude any missing or incomplete data.

3 3. Incorrect or outdated data

A third data quality pitfall in GDS is incorrect or outdated data. This can happen when your data sources or reports are not updated or refreshed properly, or when your data is corrupted or manipulated by human or technical errors. For example, if you use Google Sheets as a data source, you may encounter errors in your formulas, typos in your data, or changes in your data structure that affect your GDS reports. To avoid this, you should validate your data sources and reports regularly for any errors or anomalies, and use data caching, scheduled refreshes, or manual refreshes to ensure that your data is up to date and accurate.

4 4. Irrelevant or redundant data

A fourth data quality mistake in GDS is irrelevant or redundant data. This can happen when you use too many or too few data sources, metrics, or dimensions in your reports, or when you use the wrong ones for your analysis or audience. For example, if you use multiple data sources that contain the same or similar information, you may create confusion or duplication in your reports. Or if you use metrics or dimensions that are not relevant or meaningful for your business goals or questions, you may create noise or clutter in your reports. To avoid this, you should select your data sources, metrics, and dimensions carefully and strategically, and use data aggregation, segmentation, or visualization techniques to highlight the most important and relevant data.

5 5. Unclear or misleading data

A fifth data quality pitfall in GDS is unclear or misleading data. This can happen when you use inappropriate or inconsistent data formats, labels, or visualizations in your reports, or when you do not provide enough context or explanation for your data. For example, if you use different formats or scales for the same or similar metrics, you may create confusion or misinterpretation in your reports. Or if you use complex or ambiguous labels or visualizations for your data, you may create misunderstanding or miscommunication in your reports. To avoid this, you should use clear and consistent data formats, labels, and visualizations in your reports, and use titles, subtitles, annotations, or tooltips to provide enough context and explanation for your data.

6 6. Unverified or untrusted data

A sixth data quality mistake in GDS is unverified or untrusted data. This can happen when you use data sources, metrics, or dimensions that are not reliable, accurate, or authoritative, or when you do not provide enough evidence or support for your data. For example, if you use data sources that are not verified or certified by Google or other trusted providers, you may create doubt or skepticism in your reports. Or if you use metrics or dimensions that are not aligned with industry standards or best practices, you may create criticism or rejection in your reports. To avoid this, you should use verified or certified data sources, metrics, and dimensions in your reports, and use references, citations, or benchmarks to provide enough evidence or support for your data.