Data workshop looks at data processes to close business gaps

Jun 21 2016

Geospatial data company AfriGIS’s second quarter Data and Spatial Workshop on 15 June 2016 looked at monitoring and reporting as the final elements in a spatial information strategy framework. Previous elements in the series included data, hardware, software and other aspects.


With data being its central focus, AfriGIS’s Christopher Ueckermann highlighted additions and updates to the company’s own datasets. These include the addition of 100 989 street addresses in the NAD, 6701 links added in the Street Centrelines dataset, 39 833 new Land Parcels, 511 new Sectional Schemes, 2930 added POIs, 593 Gated Communities, 2 new Towns, 160 Suburbs, and the addition of 200 SG Towns. (AfriGIS’s address data is SANS1883 compliant.) This does not include the updates and modifications which form part of each data release. Ueckermann also explained that the confidence level of data entries is updated at each round, which denotes their accuracy. There are ten confidence levels, ranging from the very accurate erf-portion level (highest, level 1) to accuracy only up to province level.


AfriGIS’s Christopher Ueckermann, Brian Civin, and Dicky Thomas


The company’s Charl Fouche spoke of the importance of spatial data process management in monitoring and reporting. Problems in data processes tend to occur at the outset and conclusion of processes, and he suggested a checklist of questions to ask along the way to avoid shortfalls.

The tenet of these are: establishing the business questions that the data should answer and evaluate it during the process, asserting the priority and urgency of the questions, and establishing clear communication with all stakeholders at each stage in a process. Fouche further highlighted the dangers of making assumptions about data, or neglecting data limitations. Ensuring the decision-maker using the data understands the assumptions, limitations, and the final results from the data are as important. The day concluded with his colleague, Dicky Thomas looking at two case studies as examples of how proper data process management can be used to avoid failures.

Source: PositionIT

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