Are you struggling with dirty data in your GIS system? It’s a common problem that can cause major disruptions in your organization. Fulcrum offers a comprehensive solution to eliminate dirty data and streamline your data collection process. Want to learn more about the top causes of dirty data and how to solve them? Download our infographic now to discover the five key issues and their solutions—check out the highlights below!
First, consider the issue with traditional data collection methods. Methods like paper maps and written GPS coordinates are prone to errors and inaccuracies. However, Fulcrum eliminates these issues by enabling field teams to collect data directly on their smartphones or tablets, ensuring precision and reducing human error.
Also, spreadsheets are often mishandled, leading to inconsistent and unreliable data. Fortunately, Fulcrum automates data capture and standardizes processes across your organization, maintaining data consistency and reliability.
Then, there’s the challenge of version control. Managing multiple versions of spreadsheets can lead to confusion and errors. With Fulcrum, you get a centralized platform where all GIS data is updated in real-time, ensuring everyone works with the most current information.
Furthermore, manually entering geocoded addresses or precise latitudes and longitudes often results in mistakes. To address this, Fulcrum’s platform automatically geotags data using the device’s GPS, providing accurate and reliable location information.
Finally, consider the integration problems. Integrating spreadsheet data into GIS platforms can be complex and error-prone. Yet, Fulcrum seamlessly integrates with GIS platforms like Esri ArcGIS and FieldTech tools, ensuring smooth data flow and accurate integration.
In conclusion, Fulcrum’s field data collection and process management platform empowers your teams to effortlessly collect accurate, rich data. To fully understand the common issues in GIS data and how Fulcrum can help, download our infographic below to discover the five reasons you have dirty data and how to clean it up.
