Pre-emptive maintenance and investment planning for Industrial electric grid, daily life at Aurora

Utilizing smart data doesn´t necessarily mean big, multisystem integrations or massive program deployment solutions.

The initial situation

The power grid components have accumulated a throve of information during the previous decades and their condition has been systematically maintained with various tests, check-ups and repairs. Nevertheless the power grids are formed from thousands of such components and there is only so many field personnel available at one time. We at Aurora recognized the need for better utilization of accumulated data and field reports. Use of smart data and performing smart pre-emptive maintenance became of the recognized needs, that could be solved with smart data, analysis and machine learning

Data isn´t only available from databases, there is plenty of various Excel-office spreadsheets and other documentations, why it isn´t always so easy to gather a coherent view of the component maintenance status. This is the reason why we needed functional and complete view of components lifecycle and risk assessment with ability to find root causes of compromised integrity or the component failure. First goalpost we set for ourselves was the utilization of our own databases and reports. The primary goal was to combine existing databases and various outlier reports into data-warehouse, in order to combine the information into reliable database system and use this to enhance our business model as well as to serve as a tool for bettering the analytics and pre-emptive maintenance.


Aurora has various automated measurement and data-aggregation systems that gather information into separate databases, in addition there is document data-warehouse. Performing data-pruning and combining these databases into data-warehouse one location solution has been rewarding journey with it´s various hurdles. Thanks to many of our collaborators and expertise of personnel of Aurora workers, this project didn´t require large investments or replacement of existing systems with single provider solution.

Data-warehouse project improved Auroras data-security. It also improved availability of analytical services within the corporation. The changes in Data- and IT-architecture enabled the analytics team to design the analytical services as a proactive and real-time service rather than as que-schedule driven service.

The next steps for us is to continue producing real-time BI-tools, improve automatization of database organization and aggregation. With these projects we are aiming to uphold the current trend of ever developing proactive analytics and to continue with improving efficiency of our workforce.

Juha Alatalo, Head of ICT

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