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Orange: Leveraging Dataiku for Sustainable Data Practice and Machine Learning
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Machine Learning
Applicable Industries
- Equipment & Machinery
- Telecommunications
Applicable Functions
- Procurement
Use Cases
- Predictive Maintenance
- Time Sensitive Networking
Services
- Data Science Services
- System Integration
The Challenge
Orange, a leading telecommunications company, was facing challenges in its client services department's data science team. The team was primarily performing ad-hoc analysis and had limited capacity to work on complex machine learning-based projects. The challenges were twofold: tooling and hiring. The existing tool was proprietary and could only be used by statisticians or data scientists, making data access difficult and hindering project initiation. It was also not equipped to support machine learning-based data projects. On the hiring front, Orange struggled to attract fresh, ambitious data scientists due to the tooling challenge. Young data scientists preferred jobs where they could work with open-source tools like Python or R. New hires had to learn the legacy tool, which took months before they could start being productive.
The Customer
Orange
About The Customer
Orange is one of the largest operators of mobile and internet services in Europe and Africa and a global leader in corporate telecommunication services. Over the past few years, Orange has been working to increase its capacity to leverage data in all areas of the business. Despite being a non-digital native business, Orange has been able to improve the overall level of data competency at the organization by choosing the right technology and empowering more people to work with data, both through hiring and upskilling.
The Solution
Orange initiated a bottom-up change, starting with the client services data team. The team aimed to work on more advanced machine learning projects. To achieve this, they decided to empower analysts to work on simple data analysis projects independently, freeing up the data team for more complex tasks. This approach infused data practices throughout the client services organization, not just within one team. Orange chose Dataiku, a platform for Everyday AI, as their tool of choice. Dataiku's flexibility, openness, and dynamism appealed to Orange. It allowed the data team to work on machine learning projects, enabled new data scientists to be productive quickly, and allowed analysts to work independently without alienating veteran employees. With Dataiku, Orange was able to transition smaller BI projects to the business and work on machine learning use cases like call load detection and triage, and preventing unwanted charges.
Operational Impact
Quantitative Benefit
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