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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

Get Your

Return on AI
With Dataiku

Get Your

Return on AI
With Dataiku

5 Steps to Unlocking ROAI

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Real-Life ROAI Stories

20x FASTER

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See How You Stack Up on ROAI

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Forrester:
Dataiku Drives 413% ROI
on Data & AI Projects

Forrester: Dataiku Drives 413% ROI

4

GET YOUR ROAI WITH DATAIKU

Use & Fully Leverage Your Existing Architecture/Ecosystem

Fully Leverage Your Existing Architecture

Accelerate the Pace of AI

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Get Your Return on AI With Dataiku

Get Your Return on AI With Dataiku

5 Steps to Unlocking ROAI

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96% IMPROVEMENT IN OPPORTUNITY COSTS

10,000+ NEW REVIEWS/WEEK

4.23 MILLION MORE INDIVIDUALS REACHED

20,000 RISK ASSESSMENTS IN <1 YEAR

10 PROCESS PATTERNS FOR SALES STRATEGIES UNVEILED

5x MORE PROJECT CONTRIBUTORS

50% TIME SAVINGS

80% REDUCTION IN MANUAL REVIEW TIME

+50% IMPROVED CONVERSION RATE

2 WEEKS TO GET MODELS INTO PRODUCTION

1 MONTH FOR Generative AI

100% TIME SAVINGS

55-75 HOURS SAVED/MONTH

$6.5 MILLION ANNUAL BENEFIT

75% LESS CODE

600% LESS DATA INGESTION TIME

10,000 EMAILS/WEEK

98% REDUCTION IN LEAD TIME

2-3x FASTER

90% TIME SAVED

30x FASTER

60%-70% TIME SAVED

11%-36% CHEAPER

10x MORE MODELS

$18 MILLION SAVED

20x FASTER

10% LESS WASTE

-50% DOWNTIME

7.5x FASTER

100s OF DATA SOURCES

-50% TIME SPENT

MORE TRUST

9x FASTER

IMPROVED SAFETY

1

Real-Life ROAI Stories

JUST WEEKS TO DEPLOY CHURN MODEL

42% DECREASE IN TIME SPENT BUILDING CAMPAIGNS

3x FASTER

1 WEEK TO REPORT ACTUARIAL RESERVING NUMBERS

96% IMPROVEMENT IN OPPORTUNITY COSTS

10,000+ NEW REVIEWS/WEEK

4.23 MILLION MORE INDIVIDUALS REACHED

20,000 RISK ASSESSMENTS IN <1 YEAR

10 PROCESS PATTERNS FOR SALES STRATEGIES UNVEILED

5x MORE PROJECT CONTRIBUTORS

50% TIME SAVINGS

80% REDUCTION IN MANUAL REVIEW TIME

+50% IMPROVED CONVERSION RATE

2 WEEKS TO GET MODELS INTO PRODUCTION

90% REDUCTION IN WORKFORCE REQUIRED TO PROCESS DATA

6 MONTH DISEASE TRACKING

1 MONTH FOR Generative AI

100% TIME SAVINGS

55-75 HOURS SAVED/MONTH

$6.5 MILLION ANNUAL BENEFIT

75% LESS CODE

600% LESS DATA INGESTION TIME

10,000 EMAILS/WEEK

98% REDUCTION IN LEAD TIME

2-3x FASTER

90% TIME SAVED

30x FASTER

60%-70% TIME SAVED

11%-36% CHEAPER

10x MORE MODELS

$18 MILLION SAVED

20x FASTER

10% LESS WASTE

-50% DOWNTIME

Get Your

Return on AI
With Dataiku

Get Your

Return on AI
With Dataiku

Real-Life ROAI
Stories

JUST WEEKS TO DEPLOY
CHURN MODEL

Forrester:
Dataiku Drives 413% ROI
on Data & AI Projects

Accelerate Your Journey
With an ROAI Consultation

The Challenge: From 10 to 400 Million Rows
of Data

The Solution: Collective Intelligence
with Dataiku

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

The Solution: Breaking Data Silos & Democratizing AI
With Dataiku

The Challenge: Data-Driven Geographic
Expansion Decisions

The Challenge: Calendar-Based Oil
Change Intervals

The Solution: Insights in Minutes (Not Days)
With Dataiku

The Solution: … to Unified & Automated
With Dataiku