Why Data Quality Matters More Than Quantity in GIS

The Allure of Big Data

In the era of big data, there's a natural temptation to collect as much information as possible. More data points, more coverage, more attributes—surely more is better? In geospatial work, this assumption can lead organizations astray.

Quality Over Quantity

Consider a scenario: you're building a delivery optimization system and have access to two road network datasets. Dataset A contains 10 million road segments with 60% accuracy. Dataset B contains 2 million segments with 99% accuracy. Which would you choose?

For most applications, Dataset B wins decisively. Here's why:

Error Propagation

In spatial analysis, errors compound. A single incorrect road connection can cascade through routing algorithms, producing systematically wrong results. High-volume, low-quality data amplifies these effects.

Processing Overhead

More data means more storage, longer processing times, and higher costs. If much of that data is noise, you're paying to store and process garbage.

Decision Confidence

Business decisions based on geospatial analysis are only as good as the underlying data. Low-quality data leads to low-confidence decisions—or worse, confident decisions that are wrong.

Evaluating Data Quality

When assessing geospatial data quality, consider these dimensions:

Positional Accuracy

How close are the coordinates to true ground positions? For cadastral boundaries, sub-meter accuracy is often essential. For regional analysis, 10-meter accuracy might suffice.

Attribute Accuracy

Are the non-spatial attributes (names, classifications, dates) correct? A perfectly positioned building footprint with the wrong address is still problematic.

Completeness

Does the dataset cover your area of interest? Missing data can be worse than no data if it creates blind spots in your analysis.

Currency

How recent is the data? A dataset from 2020 may miss significant developments in rapidly changing areas.

Consistency

Is the data internally consistent? Do polygon boundaries align? Are classifications applied uniformly?

The TopoLab Approach

At TopoLab, we prioritize quality over volume. Every dataset undergoes rigorous validation before publication. We'd rather offer fewer datasets that you can trust than a massive catalog of questionable quality.

When evaluating data for your next project, remember: garbage in, garbage out. Invest in quality data upfront to save time, money, and headaches downstream.