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, 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.

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.

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.

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.

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.

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.

L’Oreal, the world’s largest cosmetics company, wanted to optimize the effectiveness of its teams worldwide by improving knowledge transmission at all levels of the group. To achieve this, L'Oreal deployed 'Yammer,' a social web platform developed by Microsoft, for its employees in 2012. Three years later, 23,000 L’Oreal employees were using the internal social network on a voluntary basis. However, to intensify the qualitative aspect of conversations within Yammer, L’Oreal Operations wished to identify conversation leaders and incite actions for business knowledge transmission.

BlaBlaCar, the world's first online carpooling booking service, faced a significant challenge in accessing and utilizing their data. The company's Business Intelligence (BI) teams were heavily dependent on IT teams for reporting and analytics. The process of data retrieval was time-consuming and repetitive, often taking days to deliver the requested data. The company's data sources were heterogeneous and scattered, making it difficult for the BI teams to access the data on demand. The challenge was to find a solution that could clean, consolidate, and centralize these data sources for easy and immediate access by BI teams globally.

A leading retailer in Europe with more than 3,500 stores and an e-commerce component was losing money due to being undercut by competitors on price. They also found that their customer base tended to wait until the end of seasons for huge markdowns and would only then buy certain seasonal products, which skewed their predictions for how to stock items in the future and perpetuated the pricing issue. In addition, they struggled to efficiently change prices and keep them consistent across stores and online - often, this resulted in inconsistent pricing, especially when individual store managers made their own decisions on sales. The retailer wanted to improve their pricing strategy by understanding what drove customer purchasing decisions for specific products and what prices would resonate best, easily understanding the price offered by all competitors in real time, and updating pricing consistently across stores and online.

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.

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.

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.

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.

Chronopost International, a member of the La Poste group, is a global provider of express shipping and delivery services. The company promises that all parcel deliveries in France will arrive by 1pm the following day after an order is placed. However, as demand continues to grow, especially during peak periods such as Christmas or Mother’s Day, Chronopost faced the challenge of ensuring they can always keep their promise and deliver parcels on time. The company needed a solution that would help them use and analyze historical data to optimize delivery operations and ensure delivery deadlines are met.

PriceMoov, a service that delivers optimal pricing suggestions and solutions to its customers, was facing a challenge with data originating from old SI systems, Oracle, or MySql. The data was dirty and required a fulltime developer to perform long ETL steps in PHP for cleaning. Once cleaned, the datasets were painfully entered into a model, as they were custom-built pipelines. And once finished, the replication and deployment process for the next customer was taking weeks. This long and arduous data preparation process was causing stale pricing recommendations.

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.

SendinBlue, a relationship marketing SaaS solution, faced a significant challenge in validating new customers and ensuring the quality of their databases. The company had to ensure that all contacts on the list were opted in, which required manual validation. This process was not only time-consuming and required a large workforce, but it also severely delayed account validation for customers, damaging SendinBlue’s reputation. As the customer base grew, manual validation became increasingly unfeasible. The company needed a solution that could automate the validation process and scale with the growing demand.

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.

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.

Insurance organizations are constantly exposed to fraud risks, including false claims, false billings, unnecessary procedures, staged incidents, and withholding of information. Santéclair, a subsidiary of several supplementary health insurance companies, was struggling with fraudulent reimbursements from both opticians and patients. They lacked a system that could effectively analyze the right data and adapt to increasingly sophisticated fraudsters. Instead, they relied on “if-then-else” business rules to identify likely fraud cases, which resulted in the manual audit team spending their time on too many low-risk cases. With the increase of reimbursement volume (more than 1.5M a year), they needed to improve their efficiency and productivity.

Coyote, a French leader in real-time road information, was facing a challenge in retaining its customer base and enhancing its service quality. The company wanted to optimize its loyalty program to encourage customers to increase device use. To achieve this, Coyote needed a technical solution that would enable them to segment its customer base by user profile, qualify incoming data, and quantify device use through anonymous data analysis. The company understood that the more data it collected, the better its service would be. Therefore, improving retention rates was crucial to enhance the service quality and acquire more users.

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.

Coyote, the European leader of real-time road information, faced a significant challenge in maintaining the accuracy of speed limit data within their embedded maps. This data is crucial for the functioning of their IoT devices and mobile applications, which warn drivers of traffic hazards and conditions. The company needed an automated, algorithm-based solution that could correct speed limit data and leverage the high volume of incoming data from their IoT devices to generate actionable insights and predictions. This also required instilling a data-driven approach within the company, where decisions are based on real-world data rather than standard analytics reports.

Showroomprive, a leading e-commerce player in Europe, was facing a challenge with customer churn. The company was using static rules to trigger marketing actions, which were common to all customers and did not take into account the individual value of each client. This approach was not effective in preventing churn and improving customer loyalty. Showroomprive wanted to refine its client qualification process to anticipate, prevent, and reduce churn rates. The company aimed to detect clients with a high potential of no longer buying from the website based on individual purchase rates and refine the targeting of marketing campaigns for each potential churner to improve customer loyalty.

Dentsu Aegis is a media buying company that allocates advertisers’ budgets on campaigns across various media using targeted segmentation. When pitching their services to potential customers, the sales staff recommends specific segments that would be the best to target with a particular campaign to maximize return. After they make the sale, the teams need to be able to deliver on those promises and actually maximize return with effective segmentation. However, the department struggled to quickly provide segmentation recommendations to the sales team. The teams built a data lake to collect data from multiple sources, but actually using the data meant embarking on the painful process of writing new code (Python, Spark, or SQL) every time. Every time they had a project, team members had to write a query, get the results, analyze those results with another tool, and write more code to reprocess and use the data. Without an easy way to replicate past work, each project required them to start their process from scratch, no matter how similar two prospects’ or customers’ use cases were.

Essilor International, a leading ophthalmic optics company, was facing the challenge of improving the processes and performance of their surfacing machines to significantly enhance their production. The surfacing step in lens creation is complex and delicate, as it gives the lens its optical function. The company aimed to optimize this step to correspond to each person’s individual prescription and personal parameters. However, they were dealing with large, heterogeneous datasets from the surfacing machines and needed a scalable way to work with this data. The company was already using continuous monitoring technologies like IoT connected devices, but they wanted to take a step further by employing advanced algorithms and machine learning to take action from real-time insights.

The Law Society of British Columbia, a non-profit organization that regulates lawyers in British Columbia, was looking to increase the efficacy of their trust assurance audit program. The organization regulates 3,800 law firms and audits approximately 550 firms per year, which means that each firm is audited at least every four to six years. The Law Society has three decades of historical data, which enables them to categorize law firms according to their risk level: low, neutral, or high risk. The organization made the decision to focus on risk factors and, from there, work to adjust the audit schedule based on the risk category of each firm. The senior management team at The Law Society of BC firmly believes that AI and machine learning will play an important role in their responsibilities in the near future. They knew it was time to take advantage of their collected data and leverage technology to identify patterns and behaviors and increase effectiveness and efficiencies within Law Society programs.

Finexkap, a leading fintech providing digital solutions for B2B operators, marketplaces, and e-commerce in western Europe, was facing a challenge with its data science team. The team, consisting of only three data scientists, was using Python in notebooks and a bit of C# to automate processes, but they didn’t have any visual tools for building data pipelines or to conduct on-the-fly data analysis. This method was functional but extremely tedious, and in the long run, they realized it was not sustainable, especially with the company’s growth and plans for future products and expansions.

AramisAuto, a leader in France’s new and second-hand automotive sales industry, was keen on developing its own competitive advantage with data-driven projects. The company decided to internalize the design, development, and deployment of their own data-driven solutions and products. This decision was driven by the need to develop analytics projects internally using newly hired expertise such as business intelligence engineers and data scientists. Due to data sensitivity issues, outsourcing data analysis teams was not a viable option. These new team members needed to quickly get up-to-speed in terms of creating highly-scalable predictive models and applying that knowledge to a wide array of business case scenarios, including real-time deployment of data products.