Dataiku Case Studies Orange: Leveraging Dataiku for Sustainable Data Practice and Machine Learning

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	Orange: Leveraging Dataiku for Sustainable Data Practice and Machine Learning Dataiku

Orange: Leveraging Dataiku for Sustainable Data Practice and Machine Learning

Dataiku

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
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. Read More
About 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. Read More
Customer Name	Orange Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

Customer Name

Orange

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.

Impact #1	The adoption of Dataiku has led to significant operational improvements at Orange. The tool has enabled the data team to work on more advanced machine learning projects, thereby increasing the overall level of data competency within the organization. It has also empowered analysts and business users to work independently on data analysis projects, infusing data practices throughout the client services organization. This has not only freed up the data team's time for more complex tasks but has also led to a more robust data practice within the company. Furthermore, the tool has facilitated faster onboarding of new data scientists, allowing them to be productive quickly. The success in machine learning use cases has also allowed the team to grow, further enhancing Orange's data capabilities.

Impact #1

The adoption of Dataiku has led to significant operational improvements at Orange. The tool has enabled the data team to work on more advanced machine learning projects, thereby increasing the overall level of data competency within the organization. It has also empowered analysts and business users to work independently on data analysis projects, infusing data practices throughout the client services organization. This has not only freed up the data team's time for more complex tasks but has also led to a more robust data practice within the company. Furthermore, the tool has facilitated faster onboarding of new data scientists, allowing them to be productive quickly. The success in machine learning use cases has also allowed the team to grow, further enhancing Orange's data capabilities.

Benefit #1	More than 100 analysts and other business users across Orange are now empowered to work with data using Dataiku.
Benefit #2	The data team grew from six data scientists three years ago to more than 25 people on the data science/analytics and data tooling side.
Benefit #3	Key performance indicator (KPI) dashboards that previously took up to a month or more to build and update now take one week, maximum.

Benefit #1

More than 100 analysts and other business users across Orange are now empowered to work with data using Dataiku.

Benefit #2

The data team grew from six data scientists three years ago to more than 25 people on the data science/analytics and data tooling side.

Benefit #3

Key performance indicator (KPI) dashboards that previously took up to a month or more to build and update now take one week, maximum.

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Overview

Orange: Leveraging Dataiku for Sustainable Data Practice and Machine Learning

Operational Impact

Quantitative Benefit