SLB People Analytics: Harnessing Dataiku for Optimized Talent Management
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Machine Learning
Applicable Industries
- Education
- Equipment & Machinery
Applicable Functions
- Human Resources
Use Cases
- Inventory Management
- Time Sensitive Networking
Services
- Data Science Services
- Training
About The Customer
SLB is a leading provider of technology and services to the energy industry worldwide. Founded in 1926 and headquartered in Houston, Texas, the company has over 82,000 employees and more than 90 technology centers. SLB operates in 120 countries and is committed to unlocking access to energy for all. The company is known for pioneering new approaches and harnessing the latest advances in disciplines including AI, machine learning, IoT, and more. SLB is focused on diversifying their portfolio in emerging new energy markets, particularly low-carbon and carbon-neutral energy technologies.
The Challenge
SLB, a global leader in the oil and gas industry, was facing challenges in its People Analytics team. Despite being a technology-centric company, the benefits of technological advancements were not reaching all business units. The People Analytics team, created in 2018, was struggling with scalability issues. Data scientists and engineers were working in isolation, preparing and transforming the same data without sharing insights, leading to a delay in project completion. The lack of a common platform for project recycling was causing a loss of time to market, discovery, and high-value projects. The team was also grappling with the challenge of applying machine learning to their vast talent pool, which required investment in learning and training, compliance monitoring, and stakeholder engagement.
The Solution
SLB implemented Dataiku, a centralized data platform, to enhance team productivity, knowledge sharing, and project transparency. This platform allowed the team to structure their projects more transparently, enabling team members to collaborate more effectively. Dataiku's visual data pipelines made it easier to identify areas of improvement, vet projects, and compare model versions. The platform also boosted data literacy and upskilled both technical and non-technical employees. It promoted a culture of reuse, avoiding duplicate work and saving time by repurposing cleaned data into other projects. To address the challenges of applying machine learning, Dataiku provided material that catered to various competencies and profiles, reducing SLB's journey to data science at scale. The platform's clear reporting tools ensured project compliance to privacy regulations and bias elimination. Dataiku also facilitated connectedness across multiple teams, driving efficiency in project decisions and providing visibility on support needs.
Operational Impact
Quantitative Benefit
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