{"id":52925,"date":"2025-08-26T12:14:38","date_gmt":"2025-08-26T12:14:38","guid":{"rendered":"https:\/\/dfgcon.si\/?p=52925"},"modified":"2025-09-11T12:21:35","modified_gmt":"2025-09-11T12:21:35","slug":"unlock-legacy-network-infrastructure-data-to-accelerate-operational-efficiency","status":"publish","type":"post","link":"https:\/\/dfgcon.si\/en\/unlock-legacy-network-infrastructure-data-to-accelerate-operational-efficiency\/","title":{"rendered":"Unlock Legacy Network Infrastructure Data To Accelerate Operational Efficiency (Case Studies Included)\u00a0"},"content":{"rendered":"

Unstructured data - locked data with untapped potential<\/h3>\n

For most network infrastructure operators, the goal is clear: introduce modern information systems and revise processes to improve efficiency, reduce costs, and improve ROI. Yet, a universal barrier often stands in the way\u2014decades of legacy network documentation. This data, critical to your operations, is often trapped in outdated, inconsistent, and unstructured formats. <\/span>When data remains locked in AutoCAD files, PDFs, scanned images, and other raster documents, operators lose the opportunity to unlock the true power of modern Network Information Systems (NIS). <\/span>Automations, advanced search, analytics, network tracing, etc., are simply not possible.<\/span>\u00a0<\/span><\/p>\n

Although the problem is widespread, it appears in different forms depending on each company\u2019s history, processes, tools, and specific assets. Addressing it requires a <\/span>structured analytical approach<\/span><\/b>: understanding the nature of the problem, defining the necessary processing steps, selecting and adapting suitable algorithms, identifying typical documentation errors, designing and integrating error-resolution mechanisms, and finally validating the data to ensure high data quality.<\/span>\u00a0<\/span><\/p>\n

Based on our projects and experiences with major network operators and utilities, we present five distinct, real-world projects that were successfully completed, and show our capabilities to find and deliver tailored solutions for a variety of different situations.\u00a0<\/span>\u00a0<\/span><\/p>\n

Case Studies Explained<\/span>\u00a0<\/span><\/h3>\n
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  1. National<\/strong> Telecom Provider: <\/span><\/b>Legacy unstructured physical network data (paper documents and digital DWG files) converted into a modern NIS establishing a true single system of record.<\/span>\u00a0<\/span><\/li>\n
  2. The Urban Electricity Grid Provider: <\/span><\/b>Existing vector-based electricity grid data cleaned and structured for seamless import into the operator\u2019s NIS.<\/span>\u00a0<\/span><\/li>\n
  3. The Municipal Electricity Utility Provider: <\/span><\/b>The unrefined and inconsistent data that was previously imported from different vector-based databases by the provider was structured, cleaned, and consolidated in the provider\u2019s database, together with the large number of background raster images.<\/span>\u00a0<\/span><\/li>\n
  4. Municipal Gas, Water And Electricity Utility: <\/span><\/b>Unstructured AutoCAD (DWG) vector-based files with embedded scanned raster images converted into structured NIS-ready data.<\/span>\u00a0<\/span><\/li>\n
  5. Energy, Telecommunications And Information Technology Company<\/span><\/b>: Over one million complex and varied network dimensions that remain locked in scanned images were extracted and structured to cancel manual work and prevent immense labor, cost, and error-proneness.<\/span><\/li>\n<\/ol>\n

    1. National Telecom Provider<\/span><\/h3>\n

    Project goal:<\/span><\/b> convert and migrate legacy unstructured physical network data (paper documents and digital DWG files) into a modern NIS to establish a true single system of record.<\/span>\u00a0<\/span><\/p>\n

    Key Project Characteristics<\/span><\/b>\u00a0<\/span><\/p>\n

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    • Massive data volume:<\/span><\/b> The documentation describing the physical network consisted of paper documents <\/span>(400,000 images - TIF and JPG files after scanning)<\/span><\/b> and more than <\/span>110,000 AutoCAD (DWG) files. <\/span><\/b>A vast number of documents<\/span> were not georeferenced.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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      • Missing and conflicting information:<\/span><\/b> The documentation, including maps, schematic diagrams, splice diagrams, and manhole layouts, contained overlapping and sometimes conflicting representations of the network. These discrepancies had to be carefully identified and resolved.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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        • Diverse standards:<\/span><\/b> The documentation lacked geometric and topological consistency. For example, AutoCAD files included:<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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            • Lines representing trenches not snapped to points representing manholes, poles, or cabinets.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
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                • Duplicate graphical elements layered over each other.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
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                    • Inconsistent symbols for the same network elements, often without associated attributes.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
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                      • Regional inconsistencies in documentation: <\/span><\/b>The presence of eight regional documentation departments, each applying different standards, further increased the complexity.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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                        • Existing high-level network inventory in a separate database:<\/span><\/b> Part of the high-level physical network inventory was already maintained in a separate database. Therefore, the converted data had to be aligned and integrated with this existing database.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

                          Solution Steps:<\/span><\/b>\u00a0<\/span><\/p>\n

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                          • Mass scanning of paper documentation<\/span><\/b> and preparation of workflows for large-scale processing<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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                            • Georeferencing <\/span><\/b>- <\/span>assigning spatial data to a real-world geographic coordinate system, ensuring that they match actual locations on the Earth.\u00a0<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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                              • Vectorization<\/span><\/b> of data and creation of digitized links and nodes, along with their properties \u2013 newly structured data stored in a local database using our <\/span>Interactively Assisted Converter (IAC)<\/span><\/b>.\u00a0<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
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                                • In a process of vectorization, <\/span>establishing an in-app ticketing system<\/span><\/b> to address gaps in data and resolve data conflicts collaboratively with the customer in real-time to ensure excellent data quality and consistency.<\/span><\/li>\n<\/ul>\n

                                   <\/p>\n

                                  2. The Urban Electricity Grid Provider<\/h3>\n

                                  Project goal:<\/span><\/b> Clean and structure existing vector-based electricity grid data for seamless import into the operator\u2019s NIS.<\/span>\u00a0<\/span><\/p>\n

                                  Key Project Characteristics:<\/span><\/b>\u00a0<\/span><\/p>\n