Decisions, strategies, and innovations increasingly rely on the analysis and interpretation of vast amounts of data. This data-centric mindset is vital for geospatial information solutions, which depend on accurate, high-quality spatial data to map, analyse, and interpret geographical information.
Trust in spatial data is built on hard facts. Maintenance schedules and regular dataset updates provide good indicators of reliability. Without a history of data and version control, outdated information can lead to significant problems, undermining trust. “Most of our clients require data at confidence levels 1 or 2 for precise applications, such as emergency response or detailed analytics,” Roos adds. “However, for broader analyses, lower confidence levels might be sufficient. It’s about matching the data quality to the specific needs of the client.”
“We had a case where a company was attempting to sell its client base, claiming a certain number of customers,” Roos recalls. “Upon evaluating their data, we discovered that over 50% of it was inaccurate or duplicated. The actual number of customers was significantly lower than claimed. This evaluation prevented the buyer from making a costly mistake based on false data. It’s a perfect example of why data quality is so important.
Technological advancements, such as AI and machine learning, are significantly improving data quality by automating data cleaning and geocoding processes. These technologies also help identify patterns and anomalies in large datasets.
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