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FP&A at Standard Chartered Bank

On average, two people armed with Dataiku are doing the work of about 70 people limited to spreadsheets. Here’s how they got there.

The Challenge: From 10 to 400 Million Rows
of Data

FP&A at Standard Chartered Bank has had the systems in place to do balance sheet analytics, costs analyses, etc., for many years, but analysis was limited to 10 million rows of data. In addition to finding a solution that leveraged their existing infrastructure investments, the team didn’t want to have to go looking for another tool again in a few years when his team became mature enough to start doing machine learning on their data.

“We got our hands on Dataiku and we said — what can this thing do? Within three to four weeks we managed to turnover a 4.5 billion row table in a single operation. So within a very short period of time we basically achieved our original goal, which was processing data better. But Dataiku made us realize we could do so much more than that. We’ve got an army of people copy and pasting data, and this thing can load up very big stuff. Dataiku allowed us to have different conversations about data.”

Craig Turrell,

Head of P2P Data Strategy & Delivery

Standard Chartered Bank

The Solution: Collective Intelligence
with Dataiku

By 2021, one year into their Dataiku journey, the team was using Dataiku to run three major systems at the bank and to refresh daily Tableau dashboards for all the bank’s finances, a laborious task previously done in spreadsheets. The goal in the coming years will be to continue to upskill people with Dataiku to increase efficiency across more areas of Standard Chartered Bank.

“My goal is to change the way the bank reports. We’ve taken very good accountants, given them Dataiku, and they’re producing brilliant stuff.”

Craig Turrell,

Head of P2P Data Strategy & Delivery

Standard Chartered Bank

60%-70% TIME SAVED

Heraeus: Improving the Lead Pipeline With Generative AI

Heraeus is using the momentum from its Generative AI-driven lead list tool to experiment with the new technology in a governed environment and develop more use cases within the company.

The Challenge: Informal, Manual Processes

Before Dataiku, the process at Heraeus for identifying sales leads was very informal and manual (i.e., searching on Google, brainstorming, etc., which took a lot of time), so the goal was to automate this process via large language models (LLMs) using specific selection criteria. The team asked themselves, “Can we use Generative AI’s creativity to generate new sales leads?” The answer turned out to be yes.

The Solution: An (Effective) GenAI Use Case in Just 5 Weeks With Dataiku

Heraeus partnered with Dataiku to develop a prototype use case which identifies sales leads and fact-checks them thanks to external knowledge. Given that the former sales lead identification process was immensely manual and time-consuming, the estimated time saved with the new process leveraging LLMs in Dataiku is 60%-70%. Heraeus has been able to identify targets that they would not have been able to without the tool, demonstrating the efficiency of the use case and quality of the results.

Dataiku features like automation and the possibility to quickly deploy interfaces for end users with Dataiku applications and web apps enabled Heraeus to get this use case up and running very quickly — from initial discussion to implementation in five weeks — in order to focus on the use case and LLM without having to worry about the automation. The team was also very focused on Responsible AI, mitigating the risk of erroneous or obsolete information and always keeping a human in the loop for validation.

“Timing was fast and pivotal here. Dataiku made it possible in such a short timeframe and, from a technology capability point of view, we would not have been able to do it without them.”

Jannik Beers,

Commercial Excellence Manager

Heraeus

11%-36% CHEAPER

Vestas: Propelling Sustainable Energy Solutions

With 160 GW of wind turbines to keep in good condition around the globe, Vestas needs to ensure that it can ship the right parts to the right sites within the right window of time — all without incurring unnecessary costs.

The Challenge: To Express Ship, or Not to
Express Ship?

Vestas offers both regular and express shipping to its customers — but what happens when the company uses express shipping to service repairs that are not, ultimately, time-sensitive? The Service Analytics found that 52% of express-shipped materials were not being put to use for at least four months, meaning the cost of express shipping (which often involves transporting parts by air) is wasted.

The Solution: Changing Shipment Patterns Across the Company With Dataiku

Within just about one month, the Service Analytics team was able to leverage Dataiku to build and deliver a recommendation tool that would help dispatchers and planners know whether, for a given request, they should use express shipping. The solution includes a deployed machine learning model, APIs, and a stand-alone application for end-users.

Though the savings generated by the express shipping recommendation model will only fully materialize over time, when globally implemented, the solution is estimated to reduce express shipment costs by 11%-36%.

“The Dataiku team’s systematic approach towards converting business asks to requirements, assessing feasibility, identifying alternate approaches, and building a machine learning solution and deployment along with documentation helped us learn how to take a structured approach towards designing and building machine learning solutions. This will help our team further along our journey towards building more ML-based solutions.”

Mohamed Musthafa Shahul Mohamed,

Data Science Lead

Vestas

10x MORE MODELS

MandM Direct: Managing Models at Scale

MandM Direct uses Dataiku and Google Cloud Platform (GCP) to build and maintain a data science practice that operationalizes 10x more models versus a code-only approach, supporting a wide range of use cases.

The Challenge: Scaling Out AI Deployment

MandM Direct is one of the largest online retailers in the United Kingdom, delivering more than 300 brands annually to 25+ countries worldwide. Their accelerated growth meant more customers and, therefore, more data, which magnified some of their challenges and pushed them to find more scalable solutions.

For example, MandM’s first machine learning models were written in Python (.py files) and run on the data scientist’s local machine, and they needed a way to prevent interruptions or failure of the machine learning deployments.

The Solution: Breaking Data Silos & Democratizing AI
With Dataiku

The data team at MandM Direct is now able to deliver a variety of solutions to business problems, from adtech to customer lifetime value, whether that’s a dashboard, a more detailed piece of analysis, or a machine learning project deployed in production.

MandM now has hundreds of live models, all with visibility into model performance metrics, clear separation of design and production environments, and many more MLOps capabilities built into the platform.

“Having a platform like Dataiku allows our data scientists to focus on building cool things, not spending hours and hours on maintenance and making sure things are running. With workflows deployed in Dataiku, we save literally days of work every month.”

Ben Powis,

Head of Data Science

MandM Direct

$18 MILLION SAVED

SLB: Optimizing the End-to-End Talent Lifecycle

SLB's People Analytics team uses Dataiku to better equip its talent management teams globally (reducing the time invested in training by months and years) and improve talent retention (saving millions of dollars annually).

The Challenge: Bringing ML to HR

Every year, more than 500,000 people apply to work at SLB and, of those, only 2%-3% of them are selected into their talent pool. With superior talent and a vast data warehouse available to SLB’s talent management teams across the globe, the team wanted to use machine learning to solve some of their most pressing HR challenges.

The Solution: Enhancing Talent Retention

Just like any modern company, SLB is focused on improving employee retention. Using Dataiku, they have built data pipelines from troves of data (i.e., across salary information, vacation data, performance and career stagnation information) to notify talent managers across the company about at-risk populations so they can effectively take actions as early as possible. They also provide the talent managers with insights on how they can improve the environment for their employees, such as salary, skill, or schedule changes.

Each year, the cost of unplanned employee attrition costs SLB $80-$200 million but, with the predictive model in Dataiku, the company has been able to retain between $18-45 million of that total thanks to the work the People Analytics team has done to identify and maintain high-value employees.

“Dataiku is a true partner. It’s a great product that makes sense for our team — it’s not static, it has a great product vision and roadmap, and the best part is how willing the Dataiku team is to help us.”

Modhar Khan,

Head of People Analytics

SLB

100s OF DATA SOURCES

Moderna: Targeted, Actionable Medical Insights

Moderna's on-demand AI model designed with Dataiku revolutionized the company’s day-to-day operations by surfacing insights in days while also saving analysts 40+ hours per month.

The Challenge: Optimizing the Generative Medical Insight Analysis Process

For Moderna, the pursuit of medical insights is a crucial aspect of their mission to combat infectious diseases. Timely and targeted insights are essential, driving interventions and educational initiatives. However, the traditional approach of manual data collection, involving 200 to 300 monthly entries in free text, presented challenges of inefficiency, time consumption, and difficulty in scaling.

The Solution: Long-Term Value With Nitro

Nitro is Moderna's on-demand AI model designed to analyze medical insights and sentiments from hundreds of diverse data sources. Built to align with Moderna’s medical affairs strategy, Nitro leverages Dataiku’s natural language processing (NLP) capabilities to up the ante. The process involves a seamless integration of recipes, plugins, and Python code to generate insights, which are then visualized using Tableau.

Nitro's capabilities have revolutionized Moderna's day-to-day operations. Rapid response to shifting sentiments from healthcare providers (HCPs) is now possible, reducing the analysis time frame from months to mere days.

“Dataiku's platform plays a crucial role in optimizing the analysis process, ensuring that the insights generated are aligned with Moderna's goals and reflect the sentiments expressed by healthcare providers. This alignment ensures that Moderna can make informed decisions and develop strategies that effectively address the needs and concerns of HCPs, ultimately driving the success of their medical initiatives.”

-50% TIME SPENT

Aviva: Uniting Data Analysts & Auditors

Aviva’s internal audit division uses Dataiku to cut the time spent per audit by 50% — while increasing quality.

The Challenge: Finding the Balance

The 100-employee-strong Aviva audit team seeks to provide a smooth and efficient customer experience without sacrificing the accuracy of the audit process.

Prior to working with Dataiku, the team might work on an analysis for two months before delivering results. While there are advantages to this sort of timeline (most importantly, the time to give attention to detail and consider all possible points of failure), the team was not satisfied with the pace at which it was able to intervene and help out in audit decisions.

The Solution: Finding Gold With Process Mining

With the help of Dataiku’s plug-and-play process mining solution, auditors are now able to make better, more insight-driven decisions.

The value added by process mining has been significant for both analysts and auditors. On the analyst side the greatest benefit has been in terms of time saved — cutting their time spent by about 50%; whereas for the auditors it has been in terms of the quality of the output, allowing them to quickly focus on what matters.

“Process mining allowed us, on the analytical side, to increase efficiencies and deliver the work in half the time and, on the audit side, to increase the quality of the output, because they are able to zoom in on the potential discrepancies or deficiencies in the customer journey.”

Francisco Pavao Martins,

Senior Manager of Data Analytics

Aviva

9x FASTER

Action: Powering Data-Driven Decisions

Dataiku scientists at Action worked with Capgemini and Dataiku to develop more accurate and transparent forecasting models, faster, ultimately achieving a 900% improvement on forecast runtime.

The Challenge: Manual, Excel-Based Processes

Prior to working with Dataiku, Action was relying on Excel-built algorithms for their forecasting models. The process of deploying the sales forecasting model, which is run every week and realigned once a quarter, was over-complicated as a result.

The process was also, by consequence, relatively slow. It took data experts several days to run the forecast each time, and hours more to realign it several times a year.

The Solution: Sales Forecasting With Dataiku

With Dataiku, which allows the team to test, modify, and redeploy their models routinely and with precision, they brought the runtime of each forecast down to between nine and 14 hours, and then further improved this to between six and nine hours — a time savings of almost 900%.

It’s no small thing for your business team to have a reliable sales forecast. As Dawid Kirsten, BI Consultant for Insights and Analytics at Action, stressed, these are not easy to generate without the right tools. “We have gone from an Excel-based guesstimate of what the forecast should be to 88% accuracy on a 52-week forecast [with Dataiku],” he said.

“If you use a visual recipe, it’s much easier to explain to the business side what’s happening to the data. ‘This is what’s being filtered. This is how it looks.’ You can take them through every step of the path, giving them confidence in the results.”

Dawid Kristen,

BI Consultant for

Insights & Analytics Action

1 MONTH FOR Generative AI

Doosan: AI at Scale, From Steel Production to Student Learning

Doosan accelerates its operations and educational content delivery using Dataiku’s advanced AI solutions, achieving transformative results in just one month.

The Challenge: Shortcomings in Prediction Accuracy and Content Creation Efficiency

Doosan faced substantial operational challenges that undermined its overall performance across sectors. In steel production, the struggle to predict molten steel volumes led to higher energy use and costs. Meanwhile, Doopedia, a digital encyclopedia developed by Doosan, experienced slow content creation and an inability to engage younger audiences, which hindered effective learning outcomes.

The Solution: Rapid, Accurate AI Deployments

In collaboration with Dataiku, Doosan implemented two transformative AI solutions, dramatically enhancing their operational and educational strategies. In the steel industry, a custom AI model now predicts molten steel volumes with profound accuracy, seamlessly integrating with existing systems to enhance production efficiency.

For Doopedia, Doosan leveraged the Dataiku LLM Mesh and OpenAI’s GPT-4 to reduce content creation time from days to minutes, significantly boosting engagement with younger audiences and enabling faster updates.

These AI-driven enhancements have not only optimized processes but also revolutionized educational content production and consumption, showcasing the extensive capabilities of AI in both industrial and educational settings.

“Working with Dataiku empowers us to imagine the full potential of AI and Generative AI, allowing us to embed it into Doosan’s business strategies and operations. It’s a crucial initial step in allowing us to imagine the concept of ‘AI Everywhere’ and bring it to life, driving agile innovation that’s practical and breakthrough.”

Robert Oh,

EVP, Head of Corporate Digital,

Doosan Corporation

100% TIME SAVINGS

Ørsted: Monitoring Market Dynamics With LLM-Driven News Digest

Dataiku was brought to Ørsted because their IT team had a vision to boost AI literacy and adoption via democratization of data science, machine learning, and AI.

The Challenge: Inefficient Access to Market Insights

As the green energy sector rapidly evolves, Ørsted faced significant hurdles in keeping its executives and broader workforce informed about the latest industry trends due to the sheer volume of daily news. The manual task of summarizing 300-500 articles daily was not only laborious but also led to inconsistencies in the information shared across teams, impairing strategic alignment and decision-making.

The Solution: LLM-Driven Summaries

In response to these challenges, Ørsted deployed an advanced LLM powered by Azure OpenAI on the Dataiku platform. This integration automates news digest generation, effectively filtering and prioritizing content tailored to Ørsted's needs. The system produces two versions: a concise executive summary that features the top 10 critical articles for senior management and a more detailed 360 edition for wider employee consumption.

This approach has quickly engaged over 500 employees, who adopted the new GenAI-driven news digest system in just the first week. Furthermore, this initiative not only streamlines information flow but also marks a significant milestone in Ørsted's digital transformation, showcasing their commitment to AI innovation and paving the way for future advancements in the energy sector.

“For us, democratization of AI is about increasing AI literacy in the organization, giving people the tools and platforms they need.”

Heidi Østergaard,

Head of Digital Innovation,

Ørsted

$6.5 MILLION ANNUAL BENEFIT

BMO: Revolutionizing Client Interaction With AI

In an era where timely and personalized customer service is not just preferred but expected, how can major financial institutions keep pace with the demands of their clients?

The Challenge: Engagement Hindered by Limited Insight Capture

Prior to AI integration, BMO struggled with manual processes for analyzing client interactions, resulting in missed opportunities for detailed insights. This hindered customer engagement and operational efficiency, impacting overall service effectiveness and business performance.

The Solution: NLP Revolutionizes Client Interaction

BMO tackled these challenges head-on by implementing an advanced AI solution developed in partnership with Dataiku. Leveraging sophisticated natural language processing, this system transformed the analysis of client communications, empowering the bank to swiftly discern patterns, forecast customer behaviors, and tailor responses with unparalleled precision. The use case resulted in the migration of nearly 300,000 calls annually from call centers to digital channels, empowering customers to self-serve and freeing up agent time for personalized financial assistance.

“Suddenly you're catching the things that you wouldn't have caught before, and you can also provide very targeted feedback.”

Eric Morrow,

Managing Director, Data Science & AI, DnA,

BMO

This technology not only expedited the analysis process (they can now analyze 400 times more calls than manual measures) but also significantly bolstered accuracy, providing BMO with invaluable insights to enhance client interactions and streamline operations. Consequently, BMO witnessed a remarkable $6.5 million annual increase in business benefits, solidifying its position as a leader in customer service innovation.

"AI is core to our bank strategy and at use across our business driving tangible outcomes every day."

Sandip Sahota,

Enterprise Chief Data and Analytics Officer,

BMO

75% LESS CODE

Efficient Deployment of Compliance Models in Financial Services

From an eight-month challenge to rapid deployment, a leading financial institution achieved revolutionary change by leveraging Dataiku, significantly reducing time-to-production for deploying compliance models.

The Challenge: Transforming Compliance Deployment Model for Financial Institutions

Previously, a leading financial institution struggled with prolonged deployment cycles using desktop-based, open-source solutions, taking an average of eight months to go live. This inefficient process resulted in low return on investment, lacked proper governance, and offered limited collaboration options, impeding effective performance monitoring and model maintenance.

The Solution: Agile Model Deployment & Elevated AI Governance With Dataiku

The institution established an MLOps team, adopting Dataiku to streamline the deployment process. Leveraging Dataiku's visual recipes and GUI tools, ML engineers have reduced pipeline production code by 75%, significantly accelerating deployment times. They also reduced time spent optimizing and refactoring model code for production by 86% by leveraging Dataiku visual ML capabilities.

“Dataiku has played a tremendous role in scaling our MLOps practice. Leveraging Dataiku allowed us to stand up a new MLOps team within a few short months and hire staff ready to deploy ML models after just a few days of researching technical documentation and Dataiku knowledge tutorials.”

This strategic shift has not only enhanced governance but also fostered robust cross-functional collaboration. By enabling sophisticated conditional pipelines, the move has led to a 900% increase in deployment efficiency and a 90% reduction in time-to-deployment.

“Dataiku is a quintessential analytics solution that encourages cross-functional team collaboration between analysts, scientists, engineers, and other technical professionals. The solution places a strong emphasis on the scaling of analytics or data science workloads quickly and efficiently, from inception throughout production.”

600% LESS DATA INGESTION TIME

Novartis: Streamlining Analytics & AI Across the Organization

Novartis revolutionized its data processes, ditching manual Excel updates for advanced analytics in Dataiku. This initiative drove remarkable efficiency gains, propelling them towards a future of pharmaceutical innovation and excellence.

The Challenge: Escaping Excel’s Limitations

Novartis confronted a critical challenge: manual weekly Excel updates. This inefficient process, combined with the absence of real-time updates and inadequate tracking, resulted in discrepancies and hindered risk identification in budget forecasts and field-force allocation. Recognizing the need for a transformative solution, Novartis assembled its data engineer and data science teams to streamline analytics and machine learning across the organization.

The Solution: Streamlining Analytics
and Machine Learning With Dataiku

Novartis's data teams initiated a mission to streamline analytics and machine learning. They utilized SARIMAX in Python for forecasting and turned to Dataiku for automation and insights.

The impact was immediate and profound. With Dataiku, Novartis achieved an extraordinary feat: reducing data ingestion time by an astounding 600%, accelerating the process from a week to just five hours. This remarkable efficiency gain revolutionized their analytics approach, fostering innovation and precision in decision-making across the organization.

“Novartis Pharmaceuticals found in Dataiku not just a solution implementation tool but a game-changer that aligned seamlessly with its vision to reimagine medicine. The platform’s features empowered the team to automate machine learning model solutions, create customized forecasts, and ultimately reach their business goals. In its quest to enter the AI- and data-driven future, Novartis Pharmaceuticals has not only reduced costs and saved time, but has moreover increased its teams’ trust in data-based decisions, fostering a culture of innovation and excellence.”

10,000 EMAILS/WEEK

Western Digital: Smarter Email Categorization With NLP

With an influx of up to 10,000 weekly emails, Western Digital's logistics team struggled with manual processes, straining resources and risking customer satisfaction.

The Challenge: Overwhelming Email Volume

The logistics team at Western Digital was overwhelmed by a high volume of weekly emails, covering various topics including shipping updates and invoicing. This process required significant manpower — two to three employees spent up to two weeks on sorting and responding — and often resulted in delayed or overlooked emails during critical peak periods. Such inefficiencies jeopardized customer satisfaction and highlighted the urgent need for an automated solution to improve responsiveness and operational efficiency.

The Solution: NLP-Driven Email Management

In collaboration with data scientists, Western Digital's logistics control tower team devised an automated email management system leveraging Dataiku. This cutting-edge solution seamlessly processes 10,000 emails per week with remarkable efficiency.

This NLP-based system categorizes, extracts, and analyzes emails with 80% accuracy, significantly reducing the need for manual sorting. Leveraging Dataiku's ML-assisted labeling, named entity recognition tools, AutoML, and MLOps features, the team accelerated model development and significantly enhanced operational efficiency.

Email traffic was reduced by 17% thanks to actions taken from data insights (that’s 100 employee hours saved per month). Moreover, seamless integrations with visualization platforms like Tableau have led to expedited decision-making processes, resulting in faster response times and heightened levels of customer satisfaction.

98% REDUCTION IN LEAD TIME

Aviva: Powering Insurance With Data, Machine Learning, & AI

Aviva has set a new standard in insurance efficiency, achieving a staggering 98% reduction in lead time through the integration of Dataiku, as its standardized, centralized, modernized infrastructure for data and analytics.

The Challenge: Data Fragmentation and Outdated Infrastructure

Before Dataiku, Aviva's analytics operations were hindered by disconnected data stores, reliance on outdated hardware, and disparate technologies across departments, leading to accessibility constraints and inflated costs. This fragmented data landscape and reliance on outdated infrastructure impeded efficient data analysis and decision-making processes.

The Solution:

Over the past five years, Aviva's IT platforms team has transformed their data strategy by adopting Dataiku. Starting with key transformation initiatives and later focusing on enhancing Dataiku's infrastructure and accessibility, Aviva has now enabled approximately 250 data scientists and 2,000 data and AI users to access Dataiku. This move has centralized data analytics and significantly cut costs, boosting overall efficiency.

“If you have the wrong tools in place, you can fly solo — you can get away with inefficiencies and hide your mess a bit. Dataiku changed our team atmosphere and culture for the better through sharing capabilities.”

Tom Spencer,

Head of Customer Data Science,

Aviva

One of the most significant benefits of leveraging Dataiku has been the ability to deploy data science projects overnight, slashing lead time for deployments by nearly 98% and accelerating time to market by 75%. This rapid deployment capability illustrates Dataiku's transformative effect on Aviva's analytics operations and business results.

“The most beneficial thing about Dataiku is having everything in one place, so you don’t have to go from one program to another to another and have them work all at the same time. Dataiku takes away that hassle.”

Ayca Kandur,

Data Scientist,

Aviva

2-3x FASTER

Medtronic: Using HR Data for Improved Employee Experiences

With Dataiku, Medtronic built advanced AI solutions to tackle the challenges posed by the "Great Resignation" and devised retention strategies that have already resulted in tens of millions saved to avoid turnover.

The Challenge: Battle Against Employee Turnover

Medtronic grappled with the financial strain of employee turnover, estimated to cost between 50% to 200% of their salaries. This upheaval disrupts teams and strains resources, underscoring the urgent need for a predictive solution.

The Solution: Dataiku Integration Saves Millions in Turnover Costs

Through seamless integration with Medtronic's HR data warehouse, Dataiku accelerates the creation of predictive models for employee attrition, training machine learning models 2-3 times faster than traditional Python methods.

“There were some things that made you think differently or … expanded our horizons on what might trigger [turnover] as well as just knowing that things are going to continue to be dynamic and change. And it reiterated the importance of staying close to your employee.”

Dataiku's intuitive interface and collaboration tools expedite model development and evaluation, enabling the delivery of thousands of risk retention predictions via enterprise-ready HR data. With potential savings in the millions, this innovative approach fosters organizational agility, instilling confidence in retention efforts while enhancing the employee experience at Medtronic.

“None of this work would have been accomplished in the time that we were able to do it if we weren’t working with Dataiku.”

David Riskheim,

Sr. HR Data Scientist,

Medtronic

90% REDUCTION IN WORKFORCE REQUIRED TO PROCESS DATA

One Acre Fund: Streamlining Processes to Help Remote Farmers

One Acre Fund, dedicated to empowering East African smallholder farmers, utilized Dataiku's integration to streamline operations and enhance support, contributing to efforts to overcome decentralization challenges and better serve farming communities.

The Challenge: Decentralization Issues and Climate Uncertainty

Supporting farmers across diverse regions, One Acre Fund encountered decentralization issues, particularly in smaller countries with strict data standards. In Ethiopia, managing vast datasets led to payment delays and customer dissatisfaction. Climate change further complicated matters, making it difficult to determine optimal planting times and reach farmers in remote areas.

The Solution:

The integration of Dataiku brought significant automation and benefits to One Acre Fund. This transition streamlined workflows, resulting in a remarkable 90% reduction in workforce required for data processing in Zambia, and improved data accuracy and processing speed, particularly in Ethiopia. Additionally, a Dataiku-powered chatbot provided precise planting recommendations, optimizing yields and generating substantial cost savings and revenue increases.

On average, farms saw a $5.58 annual increase, with even higher gains for early adopters. With Dataiku, 22 teams and over 100 active users have enhanced organizational effectiveness and empowered smallholder farmers. Dataiku's integration led to improved data management, faster processing times, and better decision-making. The chatbot, using machine learning, offers real-time planting advice, helping farmers maximize their output despite climate challenges.

5x MORE PROJECT CONTRIBUTORS

The Ocean Cleanup: Data Solutions Accelerate Ocean Plastic Removal

The Ocean Cleanup collaborates with Dataiku to enhance efficiency, collaboration, and operational impact, driving significant progress towards eliminating 90% of ocean plastics.

The Challenge: Navigating Data Hurdles

Despite using advanced tools like GPS devices, autonomous vehicles, and X-band radar, The Ocean Cleanup encountered challenges in effectively monitoring and analyzing oceanic plastic debris. Slow updates, computational inefficiencies, and data inconsistencies hindered transforming this data into actionable insights, highlighting the need for a centralized platform for enhanced collaboration and advanced analytics.

The Solution: Streamlining Data Management, Expanding Team Impact

Partnering with Dataiku’s Ikig.AI initiative, The Ocean Cleanup optimized data management, improved data handling, and centralized control. Dataiku revolutionized their data pipeline management, facilitating precise debris tracking and automating key processes, significantly enhancing data accuracy and efficiency. This collaboration democratized data science within the organization, enabling five times more team members to contribute to projects.

Furthermore, with intuitive visual recipes and extensive learning resources, Dataiku empowered The Ocean Cleanup’s team to drive data-driven initiatives forward. The outcomes include a fivefold faster campaign rollout and substantial time savings, advancing their mission to eliminate 90% of floating ocean plastics and establishing a new benchmark for technology in environmental challenges.

“The user-oriented, code-minimalistic approach provided by the Dataiku pipeline was a game changer both for our data pre-processing and post-processing steps.”

Bruno Sainte-Rose,

Lead Computational Modeler,

The Ocean Cleanup

4.23 MILLION MORE INDIVIDUALS REACHED

Davivienda: AI for Quality Operations & Financial Inclusivity

Davivienda transformed financial inclusivity and efficiency by teaming up with Dataiku, addressing segmentation and operational challenges head on.

The Challenge: Inconsistent Segmentation and Operational Inefficiencies

Davivienda faced major obstacles in expanding financial services and streamlining operations. Their DaviPlata initiative, designed to provide financial services to low-income Colombians, struggled with tracking users, which limited personalized service offerings. Moreover, inconsistencies arose as different bank departments applied their own rules for evaluating customers. For example, while credit risk management assessed top clients using credit scores, the credit placement department made decisions based on account balances, leading to operational conflicts and inefficiencies.

The Solution: Streamlined Customer Scoring

Dataiku's collaboration with Davivienda introduced a comprehensive customer scoring system that unified segmentation and prioritization, enhancing transparency and efficiency across departments. This system contributed to a ROI of $2.6 million by improving risk management and personalizing customer interactions. Additionally, the introduction of a recommender system expanded service access to 4.23 million more clients, significantly advancing financial inclusion for low-income Colombians.

Dataiku's predictive modeling for credit risk management also reduced default rates from 4.9% to 3.8%, leading to a 1.1% overall reduction in payment defaults. This AI-enhanced approach not only streamlined operations but also allowed for more strategic, data-driven decision-making, showcasing the transformative power of AI in financial services.

“Dataiku provided an intuitive interface that allowed us to access and manipulate large volumes of customer information, totaling approximately 20 million records. Dataiku provided us with a significant advantage by streamlining and simplifying data analysis, manipulation, and modeling tasks, resulting in a more sophisticated and effective solution to address the challenge of portfolio management and collections in our credit institution.”

96% IMPROVEMENT IN OPPORTUNITY COSTS

Air Canada: Democratizing & Accelerating Data & AI Projects

In their pursuit of refining post-campaign analysis, Air Canada's Customer and Loyalty Analytics Team innovated their approach by automating analysis with Dataiku, once all of their data was centralized with Snowflake.

The Challenge: Fragmented Data and Manual Processes

The Customer and Loyalty Analytics Team at Air Canada works closely with the Marketing Team to plan and execute marketing campaigns. Despite this collaboration, they face notable challenges in analyzing post-campaign performance to guide future strategies. These obstacles include scattered data stored in various warehouses, lack of automated analysis procedures, delayed insights after campaigns end, and limited capacity for manually analyzing all major campaigns. These obstacles hinder the team's ability to extract actionable insights and apply learnings for continuous campaign optimization.

The Solution: Post-Campaign Analysis Automation

To improve post-campaign analysis efficiency, Air Canada automated analysis using Dataiku, once all of their data was centralized with Snowflake. By streamlining processes and automating post-campaign analysis (PCA), they reduced time and effort. Outputs were consolidated into PowerBI for easy access, improving opportunity costs by 96% compared to their previous manual and unscalable marketing campaign analysis solution. Integration with Snowflake and PowerBI streamlined workflows, freeing analysts for higher-value tasks.

Moreover, leveraging Dataiku and Snowflake accelerated predictive modeling efforts, reducing work from weeks to hours. This democratization of intelligence empowers the team to utilize advanced analytics in marketing campaigns, enhancing effectiveness and speeding up insights for better decision-making.

“Dataiku enabled the orchestration of the processes, allowing us to automate, create the flow, and schedule our processes as opposed to having to manually re-code and treat these projects as ad-hoc analysis, which has reduced analysts' efforts to a few hours one day a week. The seamless integration with Snowflake (to easily access the data) and PowerBI (to easily showcase the outputs) also helped streamline the whole process.”

The Customer & Loyalty Analytics Team,

Air Canada

3x FASTER

Kapital Bank: Estimating Customer Income With Behavioral Scoring

The Challenge: Addressing a Market With Little to No Data History

Kapital Bank faced the challenge of providing loans and credits to small and medium-sized businesses and individuals without regular incomes, in a market with little to no data history. This lack of traditional financial data made it difficult to assess risk and determine creditworthiness for these underserved groups.

The Solution: Behavioral Scoring Model

Kapital Bank tackled the challenge of lending to clients with limited financial data by deploying a sophisticated behavioral scoring model through Dataiku. This model leverages transactional and government data to precisely estimate incomes, differentiating between official earnings and other sources.

Risk is further managed by integrating these estimates into a secondary model that assesses default probabilities, guiding prudent credit decisions within regulatory limits. Compared to previous legacy models, the behavioral scoring system operates approximately three times faster, thereby streamlining loan approval processes. This approach not only enhances operational efficiency but also ensures robust data security, expanding access to financial services for underserved markets.

“Behavioral scoring propels Kapital Bank’s innovation by surpassing income-based scoring and uncovering hidden customer segments based on transactional behavior. This customer-centric approach extends credit opportunities to individuals who were previously ineligible, giving us a competitive edge in the market.”

Methi Aslanov,

CDO,

Kapital Bank

1 WEEK TO REPORT ACTUARIAL RESERVING NUMBERS

Convex: Data Democratization at Scale With Dataiku & Snowflake

Convex’s actuarial team struggled with inefficiencies caused by scattered data across multiple systems, hindering accurate insurance cost estimates and delaying critical decision-making for managers and underwriters.

The Challenge: Navigating Data Chaos in Actuarial Reserving

The actuarial team at Convex faces challenges due to fragmented data spread across warehouses, applications, and spreadsheets. This fragmentation complicates the actuarial reserving process, which is crucial for estimating future insurance costs and informing senior managers, the board, and underwriters.

Before using Dataiku and Snowflake, inefficient and siloed processes hindered the team’s ability to conduct robust, repeatable analyses. This highlighted the need for an integrated tech stack to streamline data management and enhance process effectiveness.

The Solution: Dataiku and Snowflake Transform Reserving Efficiency

Dataiku and Snowflake tackle Convex’s data fragmentation and inefficiencies by enhancing integration and collaboration. Dataiku streamlines the end-to-end reserving process with automation and built-in checks, cutting the timeline from five months to just one week. It facilitates detailed analysis, supports machine learning, and centralizes data management.

Snowflake accelerates data integration, providing quick and seamless access. Together, they offer a user-friendly solution that significantly improves workflow, supports both technical and non-technical users, and resolves data fragmentation challenges, resulting in faster processing and cost-effective reserving.

JUST WEEKS TO DEPLOY CHURN MODEL

Zayo: Driving Operational Efficiency With Analytics & AI

To address challenges in churn prediction, revenue assurance, and order delivery, Zayo’s data science team focuses on enhancing cost efficiency and operational success.

The Challenge: Data, Revenue, and Delivery Process Inefficiencies

Zayo faced significant delays with their churn prediction model, which previously took 14 months to develop and required full-time maintenance, impeding their ability to manage customer churn effectively. The company also struggled to realize potential revenue due to difficulties in correlating service orders, contracts, and billing data, leading to operational inefficiencies and hindered revenue optimization. Additionally, Zayo required a scalable solution to accurately predict order delivery timelines within their CRM, which included 266 open text fields.

The Solution: Rapid Deployment and Revenue Optimization

Dataiku transformed Zayo's data strategy by centralizing frameworks, models, and datasets, optimizing core foundational data capabilities, and enabling rapid model development and deployment. The churn prediction model was built and deployed in just weeks, compared to the previous 14 months.

For revenue assurance, Dataiku's text extraction and OCR plugin automated the correlation of contracts with service orders and billing data, uncovering millions in unrealized revenue and significantly enhancing operational efficiency. Machine learning was also used to analyze 15 years of service orders and extract data from Salesforce’s open text fields, providing accurate predictions for customer service start dates and prioritization to realize financial returns sooner.

“The ability and usability of Dataiku is great because you don’t need to be an expert. You have to be data cognizant, but it’s not like other tools and platforms where if you aren’t trained for three weeks, you can’t figure it out.”

David Sedlock,

CDO,

Zayo

42% DECREASE IN TIME SPENT BUILDING CAMPAIGNS

Bayard: From SAS to Dataiku for a Modern Data Stack

Bayard transitioned from outdated tools like SAS to Dataiku for improved automation, monitoring, and visualization, resulting in greater efficiency, productivity, and the ability to focus on strategic projects like machine learning (ML) and customer segmentation.

The Challenge: Inefficient Data Workflows and Complex Marketing Campaigns

Bayard, a mid-sized French publishing company, struggled to manage its wide range of book collections and create personalized marketing campaigns across B2B and B2C channels, including stores, kiosks, and schools.

The data team of eight analysts relied on manual processes and traditional tools like SAS, which made campaign management time-consuming and inefficient. Without automation or ML capabilities, the team spent excessive time on repetitive tasks, preventing them from focusing on strategic, high-impact projects that could fuel growth.

The Solution: Automating Campaigns and Boosting Productivity

After adopting Dataiku, Bayard’s data team transformed its operations, significantly boosting employee productivity and more. By automating over 20 campaigns, they reduced campaign creation time by 42%, allowing a shift from manual tasks to strategic ML projects. Dataiku enabled Bayard's team, who had no prior experience with ML, to explore and implement advanced solutions, opening up new opportunities in data-driven decision-making.

Beyond automation, Dataiku’s monitoring capabilities helped the team react quickly to any issues with automated alerts, and its visualization tools made it easier to share insights with business leaders. These features supported smoother operations, better governance processes, and improved data quality across the board. With Dataiku, Bayard’s data team expanded its scope to include customer segmentation, cross-sell initiatives, and modernized their infrastructure by migrating from Oracle to AWS, making them more efficient and competitive.

“The strength of this tool is that it creates a community. Before this, we were not sharing findings or best practices. Now, we have made a habit of and set clear communication channels both within our data team and outside our team, with the larger organization.”

Claire Utiel,

Data & CRM,

Bayard

25% INCREASE IN DATA SCIENTIST PRODUCTIVITY

FLOA: Transforming Payment Solutions for Compliance and Efficiency

FLOA transformed its operations with Dataiku and SOUL, achieving real-time monitoring, enhancing compliance, and improving both customer and employee satisfaction.

The Challenge: Sustaining Excellence and Compliance Amid Rapid Expansion

As FLOA rapidly expanded its payment solutions across Europe, partnering with over 1,000 merchants, the company faced the challenge of maintaining operational excellence while navigating complex regulatory requirements. To meet the demands of a growing customer base and uphold standards in data protection, consumer rights, and Responsible AI, FLOA needed a scalable framework that supports best practices, enhances decision-making, and ensures compliance — all while delivering exceptional customer experiences in a highly regulated industry.

The Solution: Real-Time Monitoring and Advanced Machine Learning

Using the Dataiku platform, FLOA developed SOUL — a custom tool for real-time analysis of Dataiku projects that offers instant feedback through Microsoft Teams to ensure alignment with FLOA’s high standards. SOUL provides immediate status updates on model readiness, reinforcing FLOA’s commitment to reliable AI services. It also conducts maturity assessments before deployment, utilizing Dataiku features like project timelines, scenarios, and Python execution to streamline complex workflows. This infrastructure empowers FLOA’s data teams to explore advanced AI applications, including Generative AI, for deeper data flow analysis.

With Dataiku and SOUL, FLOA established consistent technical standards across projects, enabling swift onboarding for new data scientists and boosting productivity. Real-time feedback and project maturity checks have driven error rates to 0.05% in risk assessments, with response times under 150 milliseconds. Additionally, FLOA has seen a 25% increase in productivity, freeing senior team members to focus on strategic projects. These capabilities have allowed FLOA to engage in high-impact AI initiatives, improve ROI, and enhance both employee and customer experiences. Dataiku’s flexibility has also helped FLOA stay ahead of evolving regulatory requirements, driving innovation while optimizing compliance and operational efficiency.

"Dataiku is the backbone of AI services at FLOA, providing unparalleled time to market and quality of results, which is why it has been adopted by all data teams."

Pierre Beylard,

ML Engineer,

FLOA

75% SALES GROWTH AMONG ACTIVE VENDOR SEGMENTS

myAgro: Scaling Farmer Support Through Real-Time Insights

myAgro boosted sales by 75% with advanced client targeting, supported 900,000+ farmer participation in climate-smart training, and streamlined data for real-time decisions with Dataiku.

The Challenge: Mounting Data Challenges and Operational Inefficiencies

myAgro, an NGO focused on increasing smallholder farmers’ incomes, faced significant data challenges that hindered their impact. Their data was dispersed across multiple, disconnected systems, complicating data consolidation and delaying critical decision-making and field monitoring efforts. With a limited number of data experts, myAgro relied heavily on manual data processing through Google Sheets and offline data collection apps, a time-consuming approach that restricted their capacity for thorough analysis and strained their resources.

Despite extensive efforts to build dashboards to measure their impact, myAgro struggled to deliver the real-time insights needed for effective field monitoring and for helping field vendors target potential clients. Overwhelmed by data demands, they faced difficulties producing actionable analytics quickly and could not monitor essential activities like nutrition surveys and harvest measurements in near real-time. These challenges limited their ability to scale, make timely decisions, and maximize their impact on farmers.

The Solution: Automation, Real-Time Insights, and Predictive Analytics

With Dataiku, myAgro streamlined data management and analytics, boosting efficiency and scalability. Automating data pipelines eliminated manual processes, allowing the team to focus on their mission. Dataiku’s intuitive, low-code environment enabled staff at all levels to build data flows and create real-time dashboards, enhancing field data monitoring and decision-making.

The organization implemented a recommendation engine that uses historical data to predict which farmers are likely to make payments, resulting in a 75% increase in sales among active vendor segments. With Dataiku, myAgro anticipates demand, manages resources efficiently, and drives impactful decisions across the organization.

“One of the biggest advantages is how Dataiku empowers the Data Team with its low-code interface, enabling the team to create dashboards and workflows without the need to code nor to worry excessively with what's going underneath. And when coding is required, it’s possible within Dataiku itself with minimal configuration.”

Marcelo Mallmann,

Engineering Lead (Data),

myAgro

ATTORNEY HOURS SAVED PER COURT PROCEEDING

Roche: Boosting Case Law Analysis With Generative AI

Roche optimized its case law analysis with Dataiku, leading to faster insights, substantial cost savings, and improved operational efficiency in navigating patent oppositions and appeals.

The Challenge: Inefficiencies in Case Law Analysis

In the pharmaceutical sector, European patents are frequently subject to oppositions and appeals, requiring extensive legal analysis and a deep understanding of European Patent Office (EPO) Board of Appeals case law, which includes approximately 40,000 cases. This process requires a considerable investment of patent attorney hours and involves lengthy, costly searches through reference materials.

Before Dataiku, RRoche’s approach to case law analysis involved manual searches and in-depth reviews of complex legal documents for oppositions and appeals. This labor-intensive process absorbed substantial resources, limiting efficiency and delaying critical insights. Additionally, the absence of real-time data processing and continued reliance on manual searches diminish the team’s effectiveness, highlighting the urgent need for a streamlined solution.

The Solution: Automated Case Law Analysis With Dataiku

Roche improved case law analysis by implementing Dataiku in a pilot project called "chatWhitebook," which facilitates semantic search and full-text analysis of appeal cases. By leveraging citizen development and Generative AI, the team created a tool that provides critical insights directly to patent attorneys, enhancing efficiency. Key Dataiku features, such as the LLM Mesh, Knowledge Bank, and Prompt Studios, streamlined data processing and enabled complex search and retrieval tasks, ensuring accurate insights.

This integration transformed Roche Group Patents' operations, allowing attorneys to quickly create experimental use cases without extensive IT support. This agility enables legal experts to concentrate on strategic tasks rather than tedious data processing. With lower entry barriers to data analytics and AI, Roche is well-positioned to gain deeper insights from court cases and potentially increase success rates in oppositions and appeals. By streamlining case law analysis, Roche anticipates annual savings of $100,000 to $250,000 through reduced attorney hours.

“The entry barrier to starting with AI and data analytics tools is remarkably low when using the Dataiku platform, making it accessible even for beginners/non-coders. Using the Dataiku platform and leveraging self-service or citizen development, one can achieve rapid results without the need for additional IT experts.”

Andres Buser,

Chapter Lead New Modality IP,

Roche

10x MORE COST EFFECTIVE TO RUN CAMPAIGNS

System1: Aligning Business and Data Science for Faster Campaign Wins

System1 turned data science challenges into strategic wins by leveraging the Dataiku platform, which enabled seamless collaboration, accelerated workflows, and drove measurable business impact.

The Challenge: A Disconnect Between Data Science and Business Objectives

System1, a leading customer acquisition platform, encountered challenges in aligning its data science work with business outcomes, which led to delays and inefficiencies. The data science team, focused on solving technical challenges, often worked separately from the media buyers responsible for planning and executing campaigns.

This separation created a gap where data science wasn’t fully integrated into the early stages of campaign strategy, creating a cycle of trial and error as results were passed back and forth for approval. As System1 grew, this disconnect became a significant barrier to scaling innovation at the speed needed to stay competitive.

The Solution: Transforming Collaboration Between Teams

Partnering with Dataiku enabled data scientists and media buyers to work together in a shared workspace from the outset of campaign planning, promoting alignment on business goals and reducing reliance on trial-and-error. With Dataiku’s visual recipes, System1 improved transparency, allowing business users to see and understand data processes visually. This minimized back and forth, accelerated workflows, and helped System1 generate 107 campaigns in just 10 minutes — a task that previously took a week for a team of three.

The Dataiku LLM Mesh seamlessly integrated large language models into System1’s workflows, enhancing Generative AI initiatives and providing end-to-end visibility from raw data to AI-generated outputs. System1 also integrated Snowflake as its data warehouse, with Dataiku serving as a “remote control” for data transformations. This setup significantly reduced data engineering burdens, cutting prototype development timelines from six months to just 10 days. Together, Dataiku and Snowflake streamlined System1’s operations, enabling faster, data-driven decisions and reinforcing System1’s leadership in the digital marketing space.

“We use Dataiku like a remote control for our Snowflake server, allowing us to transform data with easy building blocks. This approach helps us solve multiple problems at once and keeps accelerating our progress.”

Graham Yennie,

Senior Manager, Machine Learning Engineering,

System1

40x HIGHER RETURNS ON AD SPEND

BCLC: Optimizing Media Spend and Driving Marketing ROI

The British Columbia Lottery Corporation (BCLC) enhanced its marketing intelligence and efficiency by adopting Dataiku, modernizing its data architecture, and implementing innovative Marketing Mix Models (MMMs).

The Challenge: Maximizing Marketing ROI in a Competitive Landscape

Post-COVID, BCLC faced fierce competition for entertainment dollars, prompting the need to optimize its marketing budget and improve return on investment (ROI) using MMMs to estimate the impact of marketing on sales.

Previously, BCLC relied on external agencies for MMM insights, but these models covered only 40% of their marketing spend, leaving critical gaps in understanding advertising impact. In response, BCLC’s internal team began developing their own MMMs for key products like Lotto Max and Lotto 6/49. However, limited infrastructure and a labor-intensive process made scaling difficult, paving the way for the adoption of Dataiku to automate and streamline their MMM initiatives.

The Solution: Streamlining Processes, Integration, & Automation

BCLC adopted Dataiku and modernized its data architecture by migrating to Snowflake and integrating AWS and Azure services. This modernization enabled the efficient delivery of three new MMMs with limited resources. Key steps included comprehensive training through the Dataiku Academy and integrating internal and external data sources, which streamlined processes and significantly reduced data preparation times.

Dataiku’s automation capabilities allowed BCLC to generate real-time insights and update models frequently with minimal manual effort. Bringing MMMs in-house resulted in substantial cost savings and agile media planning, leading to strategic reallocations — some media investments decreased by 70%, while others increased by 180% — resulting in returns on ad spend up to 40 times higher. Dataiku’s flexibility empowered BCLC’s analytics team, enhancing collaboration and positioning them to drive efficiency and growth across the organization.

12x INCREASE IN PRODUCTIONALIZED PROJECTS

Prologis: Reaching Operational Excellence With Dataiku

Prologis needed a solution to streamline AI deployment, integrate seamlessly with its existing tech stack, and empower non-technical users to drive data-driven decisions across the company.

The Challenge: Barriers in AI Deployment

Before adopting Dataiku, Prologis data scientists built models on local machines with data extracts and then handed them off to the analytics team for deployment. System dependencies and packaging challenges slowed this manual process, causing inefficiencies and leaving models outdated before they could be operationalized, limiting their business impact. AI efforts were also siloed within a small group of skilled data scientists, creating a bottleneck that restricted wider AI adoption and slowed data-driven decision-making across the company.

The Solution: Scaling AI and Expanding Access

Prologis adopted Dataiku to streamline AI deployment and overcome the limitations of their initial JupyterHub setup. While JupyterHub allowed for some code integration, the need for coding skills still limited participation. Dataiku’s AutoML and no-code capabilities removed this barrier, empowering business analysts to build and deploy models.

With Dataiku, Prologis achieved a 12x increase in productionalized projects, growing from five to over 60 active AI/ML initiatives. Through seamless integration with Snowflake, Dataiku enabled Prologis to handle large-scale data processing and optimize AI workflows across the entire lifecycle — from data ingestion to model deployment and dashboard creation— within a unified platform. This powerful combination ensured flexibility and efficiency, allowing Prologis to rapidly iterate on complex use cases and deliver data-driven insights at scale. Thanks to Dataiku’s flexibility, 85 users beyond data scientists now contribute to AI initiatives, driving efficiency gains in areas like revenue management and operational forecasting.

“At Prologis, we are selective about the technologies we embed deeply into our operations. Dataiku and Snowflake are two foundational pieces of our AI strategy.”

Jennifer Garcia,

Lead AI/ML Engineer,

Prologis

6,000+ HOURS SAVED

Akamai: Transforming Data Discovery With LLMs

The Challenge: Siloed and Outdated Data

The IT Data Intelligence group at Akamai, responsible for simplifying data processes, ensuring secure access, and delivering governed, high-quality data, faced several challenges. Keeping table documentation up to date was a challenge, with many tables left undocumented. The tedious and time-consuming task often slipped to the bottom of the to-do list.

Employees also struggled to efficiently access and use enterprise data due to fragmented systems and unclear guidance across data owned by different teams. This left business users, application owners, and audit and compliance teams unable to easily find the right data or understand its context. This fragmentation and outdated documentation created inefficiencies, diminished trust in data quality, and led to missed opportunities for data-driven decision-making.

The Solution: Enhancing Data Access and Documentation With LLMs

Akamai partnered with Dataiku to tackle fragmented systems and outdated documentation. The platform’s flexibility in supporting both on-premise and cloud environments, along with its full AI and machine learning lifecycle capabilities, provided a centralized platform to streamline data processes. Its combination of full-code and low-code tools enabled seamless collaboration across technical and non-technical teams, driving efficiency.

Using Dataiku, Akamai implemented two key solutions: an LLM-powered Data Stewardship Agent and a Data Discovery Chatbot. The Stewardship Agent automated table descriptions, saving 1,500 hours annually and $37,500 in costs, while ensuring up-to-date documentation. The Chatbot enabled natural language queries, improving data access and saving over 6,000 hours annually. Together, these innovations enhanced data literacy, strengthened trust in data quality, and empowered faster, data-driven decisions across the organization.

“Implementing our LLM-powered chatbot as a one-stop data discovery solution addresses core challenges such as fragmented information, lack of guidance, mistrust in data quality, and time-consuming processes. By enhancing data literacy, improving efficiency, and supporting informed decision-making, our solution empowers organizations to fully leverage their data assets and drive business success.”

Nirali Dedaniya,

Data Engineer,

Akamai Technologies

MONTHS TO 5 WEEKS TO DEPLOY MODELS

John Lewis Partnership: Ensuring MLOps at Scale With Deloitte & Dataiku

John Lewis Partnership (JLP) transformed its AI operations, reducing model deployment timelines from months to just five weeks through a strategic partnership with Deloitte and Dataiku.

The Challenge: Barriers to AI Deployment

JLP faced several challenges in their journey to operationalize machine learning (ML) and AI models. Despite a talented team of over 20 data scientists, their models were stuck in the experimental phase, unable to be deployed into production. This bottleneck prevented JLP from scaling their AI initiatives, realizing financial benefits, and achieving a full return on investment.

A lack of MLOps capabilities — such as tools, governance frameworks, and best practices — further compounded the issue, while integrating these processes into JLP’s existing operating model posed additional complexities.

The Solution: Scaling AI With Deloitte and Dataiku

To overcome these challenges, JLP partnered with Deloitte and adopted Dataiku as their foundational platform. Together, they addressed the lack of MLOps capabilities by implementing a tailored operating model, robust governance frameworks, and structured tools to streamline the AI lifecycle.

With Dataiku, JLP operationalized 12 production-grade AI models, leveraging the platform’s pre-built templates and model deployment features to reduce time-to-value. Deloitte provided upskilling programs for JLP’s data scientists, empowering them to manage models independently while ensuring smooth operations with service support and runbooks. This collaboration transformed JLP’s AI strategy, enabling faster deployment, improved efficiency, and long-term scalability.

DAYS TO HOURS TO COMPLETE DATA ANALYSIS

Perdue Farms: From Data Chaos to Clarity With Dataiku

Perdue Farms transformed its operational efficiency by automating USDA data collection and reporting with Dataiku, reducing tasks that once took days to just hours and saving over six hours monthly.

The Challenge: Streamlining Complex USDA Reporting Processes

Perdue Farms faced significant challenges in managing the complexity of manually collecting and reporting USDA data. The food safety team spent countless hours each month gathering information, maintaining multiple spreadsheets, and generating reports.

This labor-intensive process was not only time-consuming but also prone to errors, delaying insights and limiting the team’s ability to focus on strategic tasks like data analysis. Without a scalable, automated solution, making critical data available for timely decision-making was a persistent struggle.

The Solution: Automating Data Collection and Reporting With Dataiku

By adopting Dataiku, Perdue Farms automated and simplified its entire data collection and reporting process. API calls were set up to automatically collect USDA data, eliminating the need for manual downloads, while business users leveraged SharePoint to upload internal datasets. Dataiku seamlessly ingested, cleaned, transformed, and joined these datasets, ensuring consistent and accurate reporting.

The centralized data was made available in Perdue Farms’ data lake for holistic reporting. Dataiku’s integration with Microsoft Azure and Snowflake allowed Perdue to scale their data architecture while ensuring future flexibility. By automating workflows and reducing errors, USDA data and internal datasets became available for reporting much faster, empowering the team to make timely, informed decisions.

“One of the most impactful aspects of Dataiku is how it has empowered our business users. With its intuitive interface and automation capabilities, users no longer need advanced technical skills to contribute to the data pipeline.”

Mike Barnas,

Tech Lead,

Perdue Farms

92% REDUCTION IN QUERY EXECUTION TIME

Jahez: Transforming Missed Opportunities Into Revenue Growth

Jahez, the Middle East’s leading food delivery platform, transformed missed search opportunities into revenue growth, achieving a 45% increase in orders from newly added restaurants and generating 40% of total revenue from these restaurants in just four months.

The Challenge: Missed Search Opportunities

With 55% of its orders coming from search, Jahez recognized the untapped potential in its search data. Customers often searched for restaurants not yet listed on the platform, resulting in missed revenue opportunities.

Tracking and prioritizing these high-demand but unlisted restaurants manually was time-consuming and inefficient, especially with millions of weekly searches. This process limited the sales team’s ability to onboard sought-after restaurants quickly, making it difficult to keep up with customer demand and shifting food trends. Jahez needed a scalable, data-driven solution to efficiently capture this demand and drive growth.

The Solution: Using NLP and Automation to Prioritize High-Demand Restaurants

By partnering with Dataiku, Jahez automated its approach to analyzing search data. Using Natural Language Processing (NLP), the platform processed millions of search queries to identify high-demand, unlisted restaurants. The system grouped similar search queries, filtered out irrelevant results, and ranked missed search opportunities based on demand. This provided the sales team with a clear, actionable view of which restaurants to prioritize for onboarding.

With Dataiku integrated into Snowflake, Jahez achieved a 92% reduction in query execution time, allowing for faster and more scalable processing of search data. Dataiku’s structured workflows and automation capabilities streamlined the data science process, enabling the team to efficiently manage large datasets and scale operations seamlessly. This data-driven approach allowed Jahez to onboard high-value restaurants quickly, resulting in significant operational efficiency and revenue growth.

“Dataiku had a significant impact both technically and operationally. Technically, it improved project readability by breaking down pipelines into organized zones. With the power of Snowflake engines, we achieved almost instant results, with no network overhead. This integration greatly increased productivity, saving hours of development time. Operationally, SMO streamlined the manual process of finding restaurants to onboard. Now, the sales team has a clearer vision of which restaurants to target."

Nouf Alroqi,

Data Scientist,

Jahez

10x FASTER DATA TRANSFORMATION AND MODELING

Travis Perkins plc: Using Data Science to Improve Employee Safety

Travis Perkins plc, the U.K.’s largest distributor of building materials, leveraged Dataiku to enhance branch safety, achieving faster data transformation by 10x and an 800% improvement in data onboarding efficiency.

The Challenge: Identifying and Prioritizing Workplace Safety Risks

In the high-risk environment of construction materials distribution, safety demands a proactive approach. Travis Perkins plc sought a scalable solution to identify and mitigate health and safety risks across its branches. The Health and Safety (H&S) team faced limited resources and time, which made it difficult to determine branch inspection priorities. Manual processes further hindered their ability to analyze large and disparate datasets effectively.

The company also sought to improve cross-team collaboration and empower business teams with actionable safety insights. Without a centralized system, the team could only react to safety risks, limiting their ability to prevent incidents and optimize safety measures.

The Solution: Building the Safety Beacon With Dataiku

Travis Perkins partnered with Dataiku to create the Safety Beacon, an intelligent system that leverages data science to correlate incidents with branch characteristics. The data science team combined historic health and safety audit data with transactional information and developed an algorithm to rank branches on a 1-to-10 risk scale. They used Dataiku’s advanced data preparation and visualization tools to clean and process large datasets, factoring in variables like branch location, product types, and operational characteristics.

The team deployed a dynamic dashboard in Looker that delivers real-time risk scores to H&S managers and branch leaders. Monthly alerts notify teams of rising risk levels, enabling swift action to allocate resources and improve safety. With Dataiku’s AI and ML capabilities, the Safety Beacon transformed data workflows, enhanced incident prevention, and increased trust in the models. Dataiku accelerated the transformation and modeling process by 10x and improved data onboarding efficiency by 800%.

“We see Dataiku as an accelerator for our data science and AI journey.”

Robert Barbour,

Group and Insights Director,

Travis Perkins plc

5 DAYS TO 5 MINUTES FOR DATA TRANSFORMATION

Catella: Transforming Real Estate Investment With Dataiku

Catella, a leader in European real estate investment, revolutionized its data processes with Dataiku, reducing data transformation time from five days to just five minutes and creating centralized, intelligent apps to drive strategic decision-making.

The Challenge: Overcoming Spreadsheet Silos in Real Estate Investment

Catella’s federated structure and reliance on thousands of disconnected spreadsheets created significant challenges in data management. Version control issues, inefficient collaboration, and slow data processing hindered their ability to transform vast amounts of operational and market data into actionable insights.

Without a unified platform, standardizing data processes and enabling efficient analysis of investment opportunities was time-consuming and error-prone. These inefficiencies limited their ability to identify high-growth opportunities and hindered scalability in addressing critical topics like climate resilience. To maintain a competitive edge, Catella needed a centralized, scalable solution to streamline data workflows and enable data-driven decision-making.

The Solution: Centralized Intelligence and Scalable Governance With Dataiku

By adopting Dataiku, Catella revolutionized its approach to data insights, reducing data transformation time from five days to just five minutes. The platform enabled rapid ingestion and analysis of data from Salesforce, Snowflake, and property management tools, while also eliminating spreadsheet silos to ensure consistency, quality, and traceability.

Catella created interactive web apps to predict future rent and sale prices using AI models, which analyze diverse data sources to identify high-growth opportunities and enhance investment pipelines. Additionally, a web app for asset management provides user-friendly dashboards, data filtering, and property insights, improving collaboration and decision-making across teams. With these innovations, Catella achieved instant ROI, eliminated external consulting spend, and leveraged machine learning to tackle key challenges like climate resilience.

“As Catella embraces digital innovation, Dataiku serves as a key enabler, driving value realization.”

Sasanka Bhargava,

Transformation Lead,

Catella

80% BETTER PRODUCT CONSISTENCY

Sumitomo Rubber: Optimizing Manufacturing With Automation & AI

Sumitomo Rubber Industries, a global leader in tire and rubber manufacturing, used Dataiku to automate its vulcanization process, saving hours, improving product consistency by 80%, and generating up to $100,000 in business value.

The Challenge: Inefficiencies & Quality Control Challenges

Sumitomo Rubber faced critical challenges in its tire manufacturing process, particularly during vulcanization. Operators manually input production data and compare reports to determine optimal manufacturing phases, a time-consuming process prone to human error.

Without real-time data processing, maintaining consistent product quality became difficult. These inefficiencies slowed production, increased error risks, and hindered efforts to meet high manufacturing standards. The company needed a scalable, AI-driven solution to streamline operations and ensure uniformity across product sizes.

The Solution: Transforming Vulcanization With Dataiku

Sumitomo Rubber partnered with Dataiku to automate the vulcanization process and enhance manufacturing efficiency. The team built a system that automated anomaly detection and key calculations using wavelet averaging methods to determine optimal parameters.

By integrating Dataiku with the Thingworx platform, the system provided real-time feedback for immediate production adjustments. These innovations eliminated manual data input, reduced errors, and improved product consistency by 80% across tested sizes. Dataiku also empowered non-technical team members to participate in analytics projects, fostering a collaborative, data-driven culture. The project delivered up to $100,000 in business value while positioning Sumitomo Rubber for future AI innovations in manufacturing.

“One of the key changes is the speed at which we can derive insights from our data. Dataiku’s ability to streamline workflows, integrate various data sources, and leverage machine learning models means that insights are generated faster and with greater accuracy. This has improved our decision-making process across departments, allowing for more informed and timely business actions.”

Shuichi Kaneko,

Data Product Manager,

Sumitomo Rubber

<2 DAYS TO BUILD WORKING GENAI AND LLM PROTOTYPES

Johnson & Johnson: Transforming Vision Care With Generative AI

Johnson & Johnson (J&J) Vision partnered with Dataiku to foster innovation through a generative AI training and hackathon, empowering teams to develop working prototypes in less than two days and advancing their mission to revolutionize eye health.

The Challenge: Cultivating Innovation and Generative AI Expertise

J&J Vision aimed to strengthen its commitment to innovation by adopting generative AI and large language models (LLMs) across the organization. While Dataiku already served as a common platform for analytics and data science professionals, the Vision team wanted to deepen their technical expertise, encourage collaboration, and foster a culture of innovation.

The organization needed to bring together global, cross-functional teams to translate generative AI concepts into practical solutions that could enhance patient care, improve operational efficiency, and deliver tangible business outcomes. Achieving this required a structured approach to learning, combined with opportunities for hands-on experimentation and rapid prototyping.

The Solution: Generative AI Hackathon With Dataiku

J&J partnered with Dataiku to design a two-day event that combined theoretical learning with hands-on application. The program started with foundational sessions that equipped participants with a comprehensive understanding of generative AI and LLMs. Hands-on workshops then allowed teams to practice applying tools and techniques to real-world challenges.

The event culminated in a hackathon where teams built working prototypes that addressed vision care, surgical vision, and operational challenges. Leveraging the Dataiku LLM Mesh and other platform capabilities, participants developed applications that spanned areas such as consumer insights, contracts, sales, and operations. These efforts demonstrated the transformative potential of generative AI, while fostering collaboration and accelerating innovation. Within two days, the teams successfully created prototypes that showcased practical applications of generative AI, driving both immediate and long-term value for J&J.

“Events like this, along with Dataiku’s platform, serve as fuel for the fire of progress.”

Adrian Panduro,

Director of Global Data & Data Science Vision,

Johnson & Johnson

76% REDUCTION IN FORECAST PRODUCTION TIME

Clayco: Revolutionizing Cash Flow Forecasting

Clayco transformed its cash flow forecasting by partnering with Dataiku, achieving a 76% reduction in production time and enabling scalable, data-driven insights across the organization.

The Challenge: Transforming Manual Forecasting Into Scalable Insights

Before embracing data science, machine learning (ML), and AI, Clayco relied on manual, ad-hoc forecasting methods in spreadsheets. These methods lacked scalability, actionable insights, and validation, which made them insufficient for addressing the company’s complex financial needs. Predicting cash on hand (COH) posed a significant challenge. Without enough cash to cover upfront project costs or short-term financial obligations, Clayco drew on a line of credit. Keeping excess cash in reserve, however, limited opportunities to invest in future growth.

The limitations of simple spreadsheet formulas, inconsistent usage across teams, and the absence of validation or monitoring created a fragmented process. To overcome these challenges, Clayco’s data science team focused on building a scalable, agile, and reliable COH forecasting model. They also prioritized creating the foundational infrastructure needed for future data science initiatives, starting with a team of just two data scientists and an intern.

The Solution: Revolutionizing Forecasting With Dataiku

Clayco partnered with Dataiku to transform its forecasting process by replacing manual methods with scalable, data-driven models. In just four months, the small team built an XGBoost model to predict invoice collection times and developed a custom survival model to flag atypical cases. They automated scoring, retraining, and monitoring using Dataiku’s scenarios feature, while Snowflake integration enabled seamless collaboration between data scientists, engineers, and business analysts. GitHub support ensured reliable deployments with minimal downtime.

The improvements were immediate. Accountants, who previously spent a month creating cash flow forecasts, now generate them weekly, increasing efficiency fourfold. With these insights, teams address problem cases more effectively. The project’s success inspired other departments to adopt similar forecasting use cases, showcasing the value of a reusable, scalable data science framework. By implementing Dataiku, Clayco adopted a robust data strategy, enhancing decision-making and driving sustainable growth.

“Connecting Dataiku and Snowflake promoted collaboration between data scientists, data engineers, and business intelligence analysts.”

Harry Qiu,

Data Scientist,

Clayco

75% REDUCTION IN DATA PREPARATION TIME

Security Bank Corporation: Enhancing Bank Operations With AI

Security Bank Corporation (SBC) struggled with inefficient liquidity risk management and fragmented risk calculations, but Dataiku helped consolidate models, automate processes, and improve forecasting accuracy, operational efficiency, and compliance.

The Challenge: Inefficient Time Deposit Models and Risk Management

SBC, a leading financial institution in the Philippines, encountered operational challenges that hindered efficiency. Their time deposit models for liquidity risk management were inefficient, and manual model deployment consumed valuable resources. Additionally, fragmented risk management calculations and the lack of automation and standardization made scaling operations difficult.

The previous models, built by an external service provider using aggregated data, offered suboptimal forecast accuracy and failed to manage liquidity risks effectively. These challenges increased operational costs and slowed decision-making. In response, SBC sought a more robust, internally-controlled solution and turned to Dataiku, the Universal AI Platform, to address these challenges.

The Solution: Optimizing Operations With Dataiku

SBC shifted from aggregated data to deal-level data, leveraging Dataiku’s machine learning capabilities to consolidate twelve separate time series models into one high-performing model. This change significantly improved forecast accuracy and reduced maintenance costs. Dataiku also automated the expected credit loss (ECL) calculation process across 15 portfolios, reducing manual data preparation and calculation times by 75%. The automation streamlined processes, allowing risk analysts to focus on higher-value tasks, such as model optimization, scenario analysis, and ensuring regulatory compliance.

Finally, Dataiku automated routine model deployment tasks, reducing model run-time by 75%. This efficiency boost enabled SBC to deploy models faster, improving collaboration and ensuring standardized workflows across teams. As a result, SBC gained improved operational efficiency, reduced costs, and ensured regulatory compliance, positioning the bank for continued growth and innovation in the competitive financial services industry.

The automation provided by Dataiku allowed us to deliver model results faster and more efficiently to our business partners. By centralizing our processes and automating routine tasks, we not only improved productivity but also enhanced collaboration among team members. The platform’s capabilities enabled us to maintain high standards of accuracy and reliability in our models, leading to better business outcomes.

Robin Kamille Ramos,

Data Scientist,

SBC

22.5% INCREASE IN OPERATIONAL EFFICIENCY

Airline Pilot Club: Transforming Pilot Training With AI

The Airline Pilot Club (APC) transformed pilot training with Amelia, an AI system powered by Dataiku, improving operational efficiency by 22.5% while aligning with modern, data-driven training approaches to meet the industry's evolving needs.

The Challenge: Pilot Training Inefficiencies and Industry Pressures

The aviation industry faces multiple challenges, including a global pilot shortage, high training costs, and the increasing need for faster, more continuous learning. Traditional pilot training methods, with their rigid curricula, struggle to keep up with the dynamic demands of modern aviation.

The introduction of Evidence-Based Training (EBT) and Competency-Based Training and Assessment (CBTA) has highlighted the need for data-driven, personalized approaches to pilot training. However, implementing these methodologies has proven difficult for flight schools and airlines, as they often lack the tools to analyze and act on real-time training data.

The sheer volume of data from flight simulators, instructor evaluations, and real-flight performance has become overwhelming. Without the right technology, training organizations face fragmented insights, inefficiencies, and missed opportunities to improve pilot performance and ensure compliance with aviation standards. This complex landscape prompted the APC to seek an innovative, AI-powered solution that could address these issues and transform pilot training.

The Solution: Revolutionizing Pilot Training With Amelia

To tackle the complex challenges of pilot training, the APC developed Amelia, a scalable AI-powered platform built with Dataiku. Amelia integrates generative AI and machine learning to process pilot training data from multiple sources, providing real-time insights, personalized feedback, and aligning with Evidence-Based Training (EBT) and Competency-Based Training and Assessment (CBTA) principles.

By automating time-consuming tasks, such as generating personalized reports, Amelia reduced administrative overhead by 12.5%. It also consolidated fragmented data, improving operational efficiency by 22.5%. AI-driven tracking of pilot competencies resulted in a 25% increase in recruitment accuracy. With Dataiku's governance and compliance features, Amelia ensures full adherence to aviation regulations, minimizing compliance risks and strengthening client relationships. Amelia’s comprehensive approach has transformed pilot training into a high-quality, cost-effective solution, meeting the dynamic needs of the aviation industry.

"This success goes beyond financial ROI, solidifying APC’s position as an industry innovator. Dataiku’s scalability allows APC to replicate this success globally, contributing to the broader adoption of AI-driven recruitment and competency-based training in aviation."

Cedric Paillard,

COO,

APC

WEEKS TO MINUTES FOR PROCESSING TIME

The Challenge: Inefficiencies in Data Science and Production Workflows

As a leader in aviation propulsion, Rolls-Royce continuously seeks to maximize the value of its data assets. To scale data science capabilities and accelerate innovation, the company aimed to streamline workflows, enable broader AI adoption, and enhance operational efficiency. However, fragmented processes and manual data handling made it difficult to extract timely insights, creating an opportunity to modernize key engineering and manufacturing operations.

Two areas stood out for transformation. In printed circuit board (PCB) production line optimization, manually curated data and disconnected calculations introduced inefficiencies that slowed production and increased the risk of errors. In S-curve cumulative analysis, extracting and visualizing key engine performance indicators required navigating multiple platforms and spreadsheets, making it difficult to generate timely, actionable insights. Recognizing these challenges, Rolls-Royce set out to build a more scalable and automated approach.

The Solution: Automating and Optimizing With Dataiku

To modernize its workflows, Rolls-Royce partnered with Deloitte and implemented Dataiku, transforming data science and analytics into a more agile, automated, and accessible process. For PCB production line optimization, Rolls-Royce developed the PCB Line Optimizer, a low-/no-code application that streamlined the trolley configuration process for robotic PCB manufacturing. By automating data curation and eliminating disconnected calculations, the tool reduced processing time from hours to minutes, improving both speed and accuracy.

For S-curve cumulative analysis, Rolls-Royce consolidated its entire workflow — from data extraction to visualization — into a single platform within Dataiku. This replaced static spreadsheets with interactive dashboards, reducing errors and providing real-time insights. By centralizing and automating these critical processes, Rolls-Royce enhanced collaboration, accelerated decision-making, and empowered teams at all skill levels to leverage data science effectively.

"This success goes beyond financial ROI, solidifying APC’s position as an industry innovator. Dataiku’s scalability allows APC to replicate this success globally, contributing to the broader adoption of AI-driven recruitment and competency-based training in aviation."

Mark Bass,

ME Community Lead, Electronics,

Rolls-Royce

75% ACCURACY IN PREDICTING CUSTOMER SEGMENTS

Picard: Predicting Customer Lifetime Value for Better Outcomes

Picard transitioned from legacy SAS systems to Dataiku, modernizing data operations, improving collaboration, and achieving 75% accuracy in predicting customer segments.

The Challenge: Modernizing Data Operations

Picard historically relied on SAS for data operations, and the system performed effectively for many years. However, increasing data volumes and complex analytics demands began to challenge its scalability. Attracting and retaining top talent also became difficult, as newer data professionals favored flexible, modern tools like Python and R.

Fragmented workflows and incompatible tools further hindered collaboration across teams. The existing infrastructure lacked robust support for machine learning and predictive analytics, both crucial for modern retail strategies. To maintain its competitive edge, Picard needed a unified, scalable data platform to modernize data operations.

The Solution: Migrating From SAS to Dataiku

Picard began migrating from SAS to Dataiku in 2020 and completed the transition within a year, delivering significant benefits. The team leveraged Dataiku Academy, enabling self-paced learning and certifications. They also engaged external experts to smoothly transfer over 15 years of legacy workflows and provided personalized coaching to help team members adopt the new platform with confidence. By centralizing workflows in Dataiku, collaboration improved across previously siloed teams.

Using Dataiku, Picard developed a sophisticated Customer Lifetime Value model, accurately predicting customer revenue over the next 12 months. The team updates this model monthly, using it to inform CRM strategies, budget planning, and customer segmentation. Notably, Picard achieved 75% accuracy in predicting customer segments after their first purchase. Additionally, Dataiku now powers weekly customer segmentation updates, optimized store clustering, and advanced geomarketing analyses, positioning it at the core of Picard’s data-driven growth.

6x FASTER TO DEPLOY MODELS TO PRODUCTION

OHRA: Redefining Insurance & Efficiency With AI

OHRA streamlined AI deployment and doubled automation rates for pet insurance claims using Dataiku while ensuring efficiency, governance, and customer satisfaction.

The Challenge: Slow, Manual Processes Hindering Efficiency

As OHRA expanded its AI capabilities, the company needed a faster, more scalable approach to deployment and automation. Deploying AI models required extensive coordination between data teams, actuarial teams, and IT, which slowed down production and created inefficiencies. The growing demand for AI-driven insights pushed OHRA to find a solution that streamlined processes while upholding trust and compliance.

At the same time, OHRA aimed to improve its pet insurance claims process. While some automation existed, handling claims still required significant manual effort for data validation. The team wanted to increase automation, speed up processing times, and allow claims handlers to focus on complex cases — all while ensuring a smooth experience for customers.

The Solution: AI-Driven Automation and Scalable Governance

OHRA adopted Dataiku to build an integrated AI ecosystem that accelerated deployment and improved collaboration across teams. Standardized data and model components, reusable plugins, and embedded governance cut deployment time from one year to just two months. This transformation enabled teams to test, refine, and deploy AI models faster while maintaining trust and meeting regulatory requirements.

For pet insurance claims, OHRA leveraged AI-powered automation, including Optical Character Recognition for invoice processing, machine learning for data categorization, and LLM-driven policy analysis. Automation rates doubled, with 80% of claims now processed automatically. Claims moved through the system faster, customer satisfaction increased, and employees focused more on complex cases that required human expertise. This shift improved efficiency and strengthened OHRA’s position as a leader in AI-driven insurance.

“Dataiku makes developing, checking, and bringing data products to production much more fun. Using the visual interface and recipes eliminates a lot of tedious code work and gives the team more time to spend on what’s actually important: helping our business and customers.”

Antal Nusselder,

Lead Data Scientist,

OHRA

1150% INCREASE IN PROJECTS SUPPORTED BY FEATURE STORES

Maybank Malaysia: Redefining Customer-Centric Banking

Maybank Malaysia transformed customer engagement by leveraging Dataiku’s feature stores and AI automation, achieving a 3x rise in product uptake and a 10x reduction in model maintenance effort.

The Challenge: Fragmented Data and Inefficient Processes

Maybank Malaysia sought to enhance customer engagement and personalization but faced two major obstacles. First, siloed data prevented teams from building a comprehensive 360° view of customers. Without a centralized data repository, different departments repeated feature engineering tasks, leading to inefficiencies and delays. Data workflows lacked standardization, making it difficult to scale AI-driven insights across the organization.

Second, collaboration bottlenecks slowed down Maybank’s go-to-market process. The lack of streamlined tools for cross-team coordination extended turnaround times for personalized campaigns. As a result, delivering timely, relevant customer recommendations became increasingly challenging, limiting the bank’s ability to drive engagement and business growth.

The Solution: Scaling AI With Feature Stores and Automation

Maybank Malaysia implemented feature stores using Dataiku to unify and streamline its data infrastructure. These stores centralized customer data, allowing teams to efficiently build and reuse features, eliminating redundant work. Dataiku’s automation capabilities further optimized workflows, enabling real-time campaign tracking, self-service analytics, and faster iteration on AI models. By the end of the year, feature stores supported over 700 projects, a 1150% increase from the start of 2024.

Maybank Malaysia also leveraged Dataiku to scale personalization. The bank deployed an AI-powered Next Best Offer (NBO) model, consolidating multiple siloed models into a single, scalable system. This approach led to a 3x increase in product uptake among targeted customer segments and a 10x reduction in time and effort for model maintenance. With Dataiku’s governance and automation features, Maybank accelerated decision-making, improved collaboration, and strengthened its position as a leader in customer-centric banking.

“Dataiku as a platform has been instrumental in democratizing and unlocking data access and insights with empowering our teams at Maybank. We've seen incredible collaboration and upskilling across the organization. As we look to the future, Dataiku's robust platform is key to scaling our data initiatives, enabling us to unlock even greater value from our growing data assets and empower an expanding community of data users.”

Muhammad Haseeb Masud Qureshi,

Head, Group Data Platform & Architecture

Maybank Malaysia

50% TIME SAVED BUILDING ATTRIBUTION MODELS

Good Apple: Scaling Analytics and Unlocking New Value With Dataiku

Good Apple uses Dataiku to automate workflows, reduce attribution modeling time by 50%, and scale customer segmentation, driving high-value insights across industries.

The Challenge: Manual, One-Off Analyses Slowing Growth

Good Apple, a specialized media agency, faced challenges scaling analytics processes despite having a solid foundation with Snowflake. Customer segmentation and attribution modeling remained manual and resource-intensive, with custom one-off projects that lacked reusability. As client demand grew, the small analytics team struggled to keep up while expanding model complexity without extensive coding.

To address these issues, Good Apple needed a platform that would automate workflows, improve scalability, and enable their diverse team to transform manual processes into repeatable, high-value products.

The Solution: Automating Processes and Enhancing Collaboration

In 2019, Good Apple selected Dataiku to streamline workflows and scale their analytics capabilities. Dataiku’s visual flows and low-/no-code environment allowed analysts from different backgrounds to collaborate seamlessly, accelerating project execution. Automating customer segmentation and attribution modeling led to more sophisticated models built in less time, helping Good Apple scale their solutions across industries. This transformation resulted in a 50% reduction in time spent building attribution models.

With the Universal AI Platform’s explainability features, Good Apple built trust with internal teams and clients by offering transparency into model decisions. Furthermore, by automating workflows and integrating with Snowflake, Good Apple streamlined data processes, allowing data scientists to focus on strategy and deliver consistent, high-value insights more efficiently.

“With Dataiku, we’ve made attribution modeling a consistent, repeatable process. Now, we’re running it at least quarterly for several clients, biannually or annually for others, and even monthly in some cases. The throughput has improved dramatically, and we’ve gained more experience doing it, which has made the process even more efficient.”

Collin Joseph,

Director of Data Science

Good Apple

15+ EXCEL DATA SOURCES AUTOMATED

Ampol: Transforming Decision-Making & Value Chain Optimization

Ampol leverages Dataiku to streamline commercial analytics, automate margin modeling, and enhance decision-making, improving operational efficiency across its fuel value chain.

The Challenge: Disjointed Reporting and Manual Processes

Before adopting Dataiku, Ampol faced a series of challenges in their financial and commercial analytics processes. The company struggled with disjointed reporting workflows across the fuel value chain, involving multiple manual steps and reliance on outdated Excel models. These inefficient processes impacted the ability to provide accurate, real-time financial insights, delaying decision-making and hindering operational efficiency.

With a growing need for more accurate and timely insights, Ampol required a solution to streamline data workflows, improve data accuracy, and enable faster decision-making processes, particularly in finance and trading operations.

The Solution: Integrated Margin Modeling and User-Facing WebApps

Ampol partnered with Dataiku to solve these challenges through two key initiatives. First, the finance and data and analytics teams collaborated to create integrated margin models in Dataiku, replacing static Excel models with automated, dynamic pipelines. This transformation significantly reduced data preparation time and enhanced data traceability and quality control, providing near real-time margin insights.

Second, Ampol’s Commercial Analytics team built web-based Edit Tools within Dataiku to streamline commercial analytics. These tools automated data validation, allowed real-time user editing, and ensured data security through Role-Based Access Control (RBAC). These improvements enabled faster, more accurate decision-making, improved collaboration across teams, and boosted operational efficiency by replacing manual processes with automated workflows.

"Dataiku has helped us streamline our data workflows and improve operational efficiency, allowing us to make better decisions with real-time insights."

Mrig Debsarma,

Data Science and Data Engineering Manager,

Ampol Analytics and Data Science Center of Excellence

10x INCREASE IN THE RATE OF BOND PRICE UPDATES

Trumid: Revolutionizing Credit Trading & Real-Time Bond Pricing With AI

Trumid leveraged Dataiku’s AI-powered automation to build Fair Value Model Price (FVMP™), achieving a 10x increase in bond price update speed, 81% no-touch RFQ execution, and enhanced transparency — revolutionizing real-time credit trading.

The Challenge: Building a Real-Time Bond Pricing Model in a Complex Market

Trumid, a leading electronic credit trading platform, needed a real-time predictive bond pricing model to enhance pricing accuracy and intelligence for corporate bonds. Traders and portfolio managers required a solution that could process vast amounts of data, adjust pricing for illiquid securities, and align valuations with broader market conditions, including interest rates and credit spreads.

The system also had to deliver comprehensive supplementary data, such as confidence scores, bid-offer spreads, and pricing intervals, helping clients decide whether to trade automatically or manually. Without a reliable and unbiased model, Trumid risked slower trade execution and limited transparency in a fast-moving market.

The Solution: AI-Powered Pricing Intelligence With Dataiku

Trumid partnered with Dataiku to build Fair Value Model Price (FVMP™), an AI-driven bond pricing model that integrates direct market observations, proprietary data, and contributed datasets into a fully automated, two-sided market. Using the platform’s advanced feature engineering and machine learning (ML) capabilities, Trumid continuously processes vast amounts of data to adjust bond pricing in real-time. The model updates every 30 seconds — 10x faster than previous versions that refreshed every five minutes — giving clients access to the most up-to-date market conditions.

With the platform’s ML tools, Trumid trained, validated, and deployed the most effective pricing models based on real-world performance. This system streamlined operations, enabling 81% of eligible request-for-quote (RFQ) line items to execute “no touch” in Q4 2024, reducing manual intervention. Additionally, Dataiku’s centralized governance features enhanced model transparency, allowing Trumid to track model performance, detect anomalies early, and ensure greater reliability and client adoption. As a result, Trumid accelerated trade execution, boosted operational efficiency, and strengthened its competitive edge.

“Dataiku’s centralized system has also helped to augment transparency across all our data pipelines and ML models, boosting robust model governance and aiding early anomaly detection.”

Mutisya Ndunda,

Head of Data Strategy and AI,

Trumid

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