Case Studies.

Our Case Study database tracks 7,303 case studies in the global IoT Ecosystem.
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17 case studies
Improving Manufacturing Processes with Essilor
Dataiku
Seeing that one of their goals is to find ways to better answer consumer and business needs, the Global Engineering (GE) team was facing the challenge of improving processes and performance of the surfacing machines to significantly improve their production by using the increasing volume of data."We wanted a data science platform that would allow us to solve our business use cases very quickly. Thanks to Dataiku and its collaborative platform, which is agile and flexible, data science has become the norm and is now used more widely within our organization and around the world," said Cédric Sileo, Data Science Leader at Global Engineering, Essilor.
U.S. Venture Leverages Dataiku to Streamline Data Efforts and Save Thousands of Hours
Dataiku
U.S. Venture, a company operating in diverse industries such as automotive aftermarket, energy, and technology, faced significant challenges in managing and analyzing customer data due to its complexity. The company struggled with creating enterprise tools and processes that could eliminate silos and promote collaboration. The Data and Analytics team at U.S. Venture, established in 2018, initially focused on data warehousing and basic reporting. However, they soon realized the need for advanced analytics at scale. The team faced difficulties in maintaining models and disparate data sources, which could quickly become unmanageable without the right people and tools. Additionally, the team's data scientists and analysts were using a varied set of tools and coding mechanisms, leading to a lack of standardization and collaboration. The individual team members built their own components that lived in different places and were created via their own tools, saved on personal computers, with no visibility for other team members about where projects were and how they were created or functioned.
Revolutionizing Dynamic Pricing with Pricemoov and Dataiku
Dataiku
Pricemoov, a yield management solution provider, faced a significant challenge in handling and cleaning data from old SI systems, Oracle, or MySql. The data was dirty and required a full-time developer to perform long ETL (extract-transform-load) steps in PHP for cleaning. Once cleaned, the datasets were painstakingly entered into a model, as they were custom-built pipelines. The replication and deployment process for the next customer was taking weeks. This slow and inefficient process was hindering Pricemoov's ability to provide optimal pricing suggestions and solutions to its customers in a timely manner.
Revolutionizing Car Rental Industry: Europcar Mobility Group's Data-Driven Approach
Dataiku
Europcar Mobility Group, a global mobility service provider operating in 130 countries, was facing challenges in accurately predicting fluctuations in demand for car rentals at airports based on market changes. The International Air Transport Association predicted an increase of 2.35 billion annual passengers by 2037, particularly in the Asia-Pacific region, which would significantly impact Europcar's operations. To address this, Europcar aimed to build an application using data from various sources, including fleet traffic, passenger volume, reservation and billing data, and data on new airline routes. However, the data was scattered across different locations, in different formats, and was massive in volume, posing a significant challenge.
Trainline's Global View of Marketing Acquisition through IoT
Dataiku
Trainline, Europe’s leading independent train travel platform, faced a significant challenge in monitoring and improving their marketing acquisition. With paid campaigns running 24/7 and users interacting with those ads around the clock, static dashboards were no longer sufficient. The company needed a dynamic, real-time data solution to provide the most accurate marketing insights. They had a technical team within the marketing department tasked with creating aggregated, centralized dashboards focused on Trainline marketing acquisition efforts. However, this ambitious endeavor required data science skills and a tool robust enough to blend and support multiple data formats and sources to track acquisition according to certain parameters. The challenge was to find a tool that would allow the technical team to improve and upgrade their skills while also satisfying the marketing department’s requests quickly and efficiently.
Scaling Up Data Efforts With LINK Mobility
Dataiku
LINK Mobility, Europe’s leading provider of mobile communications, wanted to scale up their data efforts in 2017. Their primary offering is mobile messaging services, sending over 6 billion messages a year worldwide. These messages carry invoices, payments, and vouchers, associated with a variety of services. This generates a lot of data, and LINK Mobility saw an opportunity to expand their offerings to provide more data-driven insight to customers surrounding the delivery and performance of their messages and services. They were looking to expand to customer dashboards and send additional offers based on that data. However, with just a one-man data science team at the beginning of the project, LINK Mobility needed to find a tool that would allow them to scale up data requests coming from inside the company and provide data insights to customers without having to use two different tools or platforms.
Automated Dashboards in Customer Analysis: A Case Study of OVH
Dataiku
OVH, a global provider of hyperscale cloud, faced a significant challenge in analyzing user interactions on their website to inform product and operations decisions. The primary point of contact between OVH and its users was through their website, where customers could place orders and receive technical advice or support. The business analysts responsible for disseminating data and insights to inform on the commercialization and optimization of the website had built a dashboard with basic, high-level metrics like user behaviors and site traffic. However, the dashboard's utility was limited as it did not combine different data sources for a complete view, necessitating ad-hoc analysis. The analysts had little time for this, and the ETL (extract, transform, load) for the dashboard presented concerns for the data architects around data and insights quality. There was a lack of transparency around exactly what data was being transformed and how.
Heetch's Elastic AI Strategy Development with Dataiku
Dataiku
Heetch, a French mobility company, was struggling with the management of large quantities of data gathered from drivers, passengers, and global operations. As the company grew, the costs of their data warehouse were spiraling out of control and performance was suffering due to the increasing volume of data. They needed a solution that would allow anyone in the organization to work with large amounts of data while also ensuring optimized resource allocation. The challenge was to find a way to leverage big data with good performance and at reasonable costs, which required serious computational power, optimized resource consumption, and isolated environments for development and production. Managing all these aspects was becoming increasingly complex for the organization.
Orange: Leveraging Dataiku for Sustainable Data Practice and Machine Learning
Dataiku
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.
Thrive SPC's Transformation: Leveraging Dataiku, Snowflake, and Snow Fox Data for Improved Clinical Home Care
Dataiku
Thrive Skilled Pediatric Care (Thrive SPC) is a healthcare organization dedicated to providing exceptional clinical home care to medically fragile children. Their mission is powered by innovative technology and in-depth data insights. However, when Thrive SPC acquired two different types of healthcare organizations, they faced a significant challenge: managing multiple electronic medical record systems with different data reporting mechanisms. These complex and disparate systems were impossible to manage individually and manually. Thrive SPC needed a way to prepare and store data in a reliable and accessible manner. The organization was also struggling with competing and confusing spreadsheets, which hindered the efficiency and organization of their data projects.
Vestas: Leveraging Dataiku for Sustainable Energy Solutions and Cost Reduction
Dataiku
Vestas, a global leader in sustainable energy solutions, faced a complex challenge in optimizing their shipment patterns to save costs. The Service Analytics team at Vestas had to consider not only external, customer-facing products, but also internal stakeholders across the Operations, Finance, Supply Chain, and Commercial teams. All of these teams worked together to answer big questions for the company such as how and when to deliver a turbine part from point A to point B. The team recognized that a more robust data operation could help them simplify and improve logistical challenges. They understood that data science-based solutions in predictive asset maintenance, field capacity planning, inventory management, demand and supply forecasting, and price planning would provide critical support to the internal customers of Vestas. However, until that point, the data team ran a traditional business intelligence (BI) based analytics operation, querying BI-dashboards, deriving insights, and building data products in a less automated manner.
Action's Journey: Leveraging Data Analytics for Efficient Business Operations
Dataiku
Action, Europe’s fastest growing non-food discount retailer, faced a significant challenge in managing the vast inflow of data from its over 2,300 stores across 11 countries. The company needed to track various aspects such as consumption patterns, product placement, and supply chain disruptions, which varied according to local, national, and international trends. The existing architecture was not sufficient to handle the data efficiently and provide accurate forecasting models for demand and sales in new and existing markets. The company also faced issues with data access and quality, costly and complex processes, lack of visibility and control, and operationalization and business impact. The use of Excel for gathering, sorting, manipulating, and modeling data was proving to be a bottleneck for the speedy and efficient deployment of data analytics and models.
Convex Insurance: Enhancing Collaboration and Decision Making with Dataiku
Dataiku
Convex Insurance, a company that heavily relies on data for decision making, was facing challenges with its traditional data handling methods. The company's diverse team of actuaries, architects, and business analysts needed a more efficient way to collaborate and extract value from their data. The use of spreadsheets was no longer sufficient due to the enormous volume of data and the complexity of the data pipelines. The company needed a solution that could accommodate the varying levels of technical expertise within the team and facilitate effective communication and collaboration.
JK Lakshmi Cement: Enhancing Operational Efficiency with Dataiku
Dataiku
JK Lakshmi Cement, a decades-old manufacturing firm in the cement industry, was facing significant challenges in improving operational efficiency and accelerating analytics reporting. The company was bottlenecked by a lack of data- and tech-savviness, with only a few people tasked with building reports for the entire organization. This limited the number of reports that could be created, hindering the company's ability to make data-driven decisions. The team was also struggling with scarce and underutilized data experts, and their data processes lacked operationalization and the ability to make a strong business impact. They were in need of a platform that could boost the efficiency of its coders and allow for cross-team collaboration with line-of-business users.
Data Transformation at Rabobank: A Case Study in Execution & Innovation
Dataiku
Rabobank, a leading Dutch bank, was faced with the challenge of keeping up with the rapid pace of technological change in the banking sector. According to a 2020 PwC report, 81% of banking CEOs expressed concern about the speed of technological change, more than any other industry sector. Rabobank, however, chose to embrace this change and transform their organization to move with the pace of innovation. The bank had been on their data journey since 2011, and while they had the support from both the executive level and the people implementing the technology and processes, they needed to further streamline their approach to data transformation.
Royal Bank of Canada: Streamlining Audits with Dataiku's IoT Solution
Dataiku
The Royal Bank of Canada (RBC) was facing challenges in its control testing process, which was manually intensive and only conducted periodically. The process involved selecting control tests, designing test procedures, sampling the resulting dataset/transactions, and checking samples for adherence to criteria. This process was repeated anywhere from annually to once every two years. The CAE Group, burdened by the administrative overhead, had less time to review and revise the outliers. The process was difficult to scale, as the platforms retreated into their silos, where they built and managed their own control testing process. This duplicated effort made consolidation into CAE Group’s holistic enterprise view a cumbersome, manual process. The challenges were both technical and organizational. Technical challenges included the need for platform analysts to onboard and update their models in production, support for the variability of different models and schemas of outliers, categorization of each control test, and managing data governance requirements. Organizational challenges included a shift in mindset for auditors, updating and onboarding existing control tests, and developing incentives for adopting the new platform.
SLB People Analytics: Harnessing Dataiku for Optimized Talent Management
Dataiku
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.

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