What are the most common data quality issues in location intelligence?

1 Incomplete data

One of the biggest challenges in location intelligence is finding and collecting enough relevant and reliable data to answer your questions. Depending on your goals, you may need data from various sources, such as census, surveys, mobile devices, social media, sensors, or maps. However, not all data is readily available, accessible, or updated. You may encounter gaps, inconsistencies, or inaccuracies in your data that limit your analysis and insights. To overcome this issue, you need to define your data needs clearly, use multiple sources to cross-validate and enrich your data, and apply data cleaning and standardization techniques to remove errors and outliers.

2 Inaccurate geocoding

Geocoding is the process of converting addresses or other location identifiers into geographic coordinates, such as latitude and longitude. It is a crucial step in location intelligence, as it allows you to map, visualize, and analyze your data spatially. However, geocoding is not always accurate or consistent, especially when dealing with large or complex datasets, multiple formats, or different languages. You may encounter errors or discrepancies in your geocoding results that affect your spatial analysis and interpretation. To avoid this issue, you need to use reliable and updated geocoding tools and services, check and validate your geocoding results, and correct any anomalies or mismatches.

3 Inappropriate scale

Scale is the level of detail or granularity of your data and analysis in relation to the geographic area or phenomenon you are studying. It is an important factor in location intelligence, as it can influence the accuracy, relevance, and significance of your findings. However, choosing the appropriate scale for your location intelligence project is not always easy or obvious. You may face trade-offs between resolution and coverage, or between simplicity and complexity. You may also encounter issues such as the modifiable areal unit problem (MAUP), which refers to the variation in results due to the aggregation or disaggregation of spatial units. To address this issue, you need to match your scale to your research question and purpose, use multiple scales to compare and contrast your results, and be aware of the limitations and assumptions of your scale.

5 Ethical and legal concerns

Location intelligence is not only a technical or analytical process, but also a social and ethical one. It involves the collection, use, and sharing of personal or sensitive data that can reveal information about people's identities, behaviors, preferences, or movements. It can also have implications for people's privacy, security, rights, or welfare. Therefore, location intelligence requires ethical and legal considerations and responsibilities, such as obtaining consent, protecting anonymity, ensuring security, complying with regulations, and respecting values and norms. To avoid or minimize this issue, you need to follow the principles and guidelines of ethical and responsible location intelligence, such as transparency, accountability, fairness, and trustworthiness.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?