Our client is one of the leading mobile network operators (MNO) in the United States.
Numerous instances of incorrect data in the inventory database vs. actual network deployment causing significant automation fallouts and manual processes.
- Engineering and planning processes requiring manual swivel chair resulting in delays and inefficiencies
- Service Assurance functions such as troubleshooting, planned maintenance require manual correlation between different network data sources and inventory system
- Producing reports of existing network assets (e.g. active, spare, pending, etc.) are inaccurate resulting in CAPEX inefficiencies
- Various automated provisioning and activation processes (e.g. Network Elements IP address assignment/release) are hampered with fallouts resulting in inefficient manual intervention
Using 1Data, network data from various sources (EMSs) and are ingested and
normalized for comparison to Inventory System data.
- Inventory System RDBMS containing 800K+ network devices and 3M+ circuits/paths
- Fronthaul network EMSs data for three different vendors in varying XML formats
- Backhaul network EMS data for two different vendors in RDBMS tables
- Location data from two different sources via API
Drools Rule Engine, with over 100 custom-defined rules, is used to generate a comprehensive list of inconsistencies between Network and Inventory system with accompanying corrective actions. Some examples are:
- Identifying equipment active in network, but not existing in Inventory System. Discovering the missing equipment location from another system and auto-creating them in the Inventory System
- Identifying missing circuits/paths in Inventory System which are exiting and active in the network and auto-creating them with standard naming
- Identifying equipment in Inventory System which are not named exactly as their network ID and Auto-Fixing them
- Identifying equipment in Inventory System which have been long disabled or removed from network and Recommending deletion
- 1Data Data Management Workflow is used to bulk-load corrections into the Inventory System and assign/track Recommended and Manual data fixes
- Within 2 weeks of deployment, Network vs. Inventory inconsistencies were reduced by 70%
- Service Assurance troubleshooting processes and MTTR were significantly improved because of data accuracy