Case study

Towering over Telcos.

Fixing the market expert gap by providing easy to use solutions and an extra pair of hands.


The company lacked experts who could deal with the amassed issues sprawled across their data analytics’ devices and needed external help to fix the codes manually and alleviate longstanding infrastructure gaps across various projects and solutions.


Our expert worked on a problem to problem basis, fixing the most urgent issues on the go and articulating how selected solutions work to the engineers, showing them how to prevent obstructions. The employees could focus on further development that brings business value.

Technology & Tools

Machine Learning
Deep learning


 A leading telecommunications company, media house and ICT provider in the Nordic and Baltic regions. They enable digital societies we live in with essential digital infrastructure and digital services.


The issues not only obstructed day to day work causing repetitive, redundant tasks and providing unreliable data. They delayed development, making it hard to capitalise the inputs. There were also other resulting losses, because the velocity of issues caused consumer dissatisfaction, and warranty / damage claims in many cases. As it grew more regular it not only produced monetary but man-power costs. 

To counter the skills gap the company had amassed data analytics programmes and devices that require limited know-how and hired teams of engineers who were familiar with using these. They were not equipped to deal with their maintenance and problems and issues, which required understanding the logic behind the mistakes and fixing them manually within the codes. In time the issues grew in number and depth causing the company to look for expert knowledge. Yet without having to book a selected project with a consulting company or hiring a new employee, both would require substantial time and money. Our expert was introduced to help recapture the value.


Entering after 2 years of development, presented an array of projects to fix. It required a mixed approach. Our expert first addressed the critical issues and those escalating, which were pointed out by the manager on an ad hoc basis. These were often caused, because of some bigger programme issues, but quite often started as small bugs, which were too complicated for the staff to fix and they enhanced the problem instead of fixing it. At times these were easy fixes, others complex, some required a new approach, because the issue was not based on the implementation. To truly power the professionals to achieve better results, our expert spent a couple of days with selected data scientists, showing them how to fix the issues and how the smaller interventions work. For projects with more allocated time, our expert acted as an architect by simplifying the paths to meet the modern needs, fixing them from A to Z to root out issues completely and enable expansion. 

Such ad hoc case-to-case support was provided by the expert for 3 years, unlocking  projects and helping data scientists with troubleshooting their models and deploying them to production. To leverage their potential. Over this period there were also frameworks that had to be crafted and implemented from zero. This end-to-end approach encompassed a number of projects related to the infrastructure improvements as well as those responding to the market situation. The projects implemented during the collaboration period include  the anonymization framework created to fill the GDPR requirements, cluster-wide performance audit and tuning, Cloudera cluster upgrades, and supporting dozens of internal Data Scientists and Data engineers.


All the issues were successfully resolved, and help in navigating their devices enabled the team to realise the value of their solutions.

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