Get Started

Celebrating Pioneering Achievements in Data Science and AI With the Dataiku Frontrunner Awards

Use Cases & Projects, Dataiku Company, Dataiku Product, Scaling AI Katrina Power

Following the success of its inaugural edition, the Dataiku Frontrunner Awards returned in 2022 to celebrate the pioneering achievements of all people who leverage Dataiku to harness the power of data.

This year, we’re proud to announce that we collected 80 innovative use cases and inspiring success stories from around the globe. Coming from both teams and solo practitioners across industries, the impressive set of submissions is reflective of the increasingly diverse ways in which individuals and organizations are embracing Everyday AI to drive their field forward. 

With entries submitted by customers, partners, nonprofits, academic institutions, and individual users, we’re grateful to all participants who took the time and effort to share their case studies with the Dataiku Community and provide insight into their trailblazing work. 

After careful deliberation by our jury, which was composed of Dataiku executives and independent industry experts, we’re pleased to finally reveal the winners and finalists for the 2022 edition of the Dataiku Frontrunner Awards. Watch the video below, and read on to learn more about their inspiring achievements!

Data Science for Good Award

The Ocean Cleanup

With 97% of floating plastic entering the ocean ending up onshore, Bruno Sainte-Rose and his team at The Ocean Cleanup were committed to building the world’s largest beach cleanup database. Using the interactive visualization features in Dataiku, this enabled them to better understand the geographical distribution of hotspots and build a global beach cleanup map, with the final dataset counting an astounding 915,000 points. 

ocean cleanup

A major benefit to the community, the database not only serves to increase global knowledge but will enable both The Ocean Cleanup and their fellow nonprofit organizations to increase their impact on removing plastic pollution. It also paves the way for them to build a citizen data science application, where anyone can enter data on the amount of plastic removed from nature, and acquire a more accurate picture of all the plastic cleaned worldwide. 

Finalists:

Responsible AI Award

Atlantic Technological University

Through their research on the application of machine learning (ML) algorithms to optimize learning analytics, Ikechukwu Ogbuchi and his colleagues at Atlantic Technological University aimed to gain a deeper understanding into various student groups, identifying early signs of students at risk of dropping out and developing the best ways of improving engagement and retention

Using Dataiku to experiment with ML models, they were able to derive new insights from patterns associated with historical anonymized student data. This allowed them to identify students who are at risk based on their patterns of interaction with their learning systems, and guide them to intervention programs in place such as student mentor support, additional classes, financial aid, counseling, and much more.

Finalist: FPT Software - Analyzing Employee Feedback to Improve Retention, Productivity Rates, and Workplace Satisfaction

Value at Scale Awards

Australia Post

At Australia Post, a key activity at the numerous facilities within their logistics network is the daily resource and staffing planning decisions made by shift production managers. Currently based on limited information, too few staffing hours can result in sub-optimal throughput and parcel delays, while too many can unnecessarily increase labor spend. 

To address this pain point, James Walter and his data science team developed a shift volume forecasting algorithm in Dataiku that provides facility operators with daily shift volume forecasts and translates this information into staffing requirements. Empowering managers to confidently make decisions regarding the need for overtime results in significant operational dollar value savings. 

Finalists: 

Special Distinction for Best MLOps Use Case: FLOA - Delivering Automated, Real-Time Credit Scoring at Scale

Partner Acceleration Award

Aviva & Wipro

Large organizations with digital capabilities have significant IT operational challenges, dealing with many large IT assets and multiple support teams. To address these and help streamline the existing processes, Mitesh Chandorkar and his team at Wipro, alongside the Aviva team led by Simon Sinfield, set up the Digital Operations Management Engine, which allows for historic incident management data and log details to be analyzed to better understand the current IT operations

Wipro partnered with Aviva for this project, with the aim of enhancing incident management, improving service availability, acting ahead of major disruption, increasing operational efficiency, enhancing customer experience, and reducing repetitive manual decision-making tasks. Using Dataiku as the backbone of the solution, they were able to produce numerous use cases with many more still in development, enabling them to meet their goals. 

Finalists: 

Moonshot Pioneers Award

Unilever

As an organization, Unilever felt the need to become more nimble to keep their brands at the forefront of upcoming market disruptions through new product trends and serve consumers where their needs and wants are. To accomplish this and combat the associated challenges, Linda Hoeberigs and her team were tasked with creating an automated, end-to-end idea spotting, screening, and sizing solution. 

retail carts

Using a mix of data collection methods on the Dataiku flow, they wrangled datasets that included billions of ratings, reviews, and data points on consumer searches, as well as a plethora of social media data. Enriching it with Unilever proprietary concepts that were predicted using deep learning, this allowed them to assess over 100 billion potential product ideas to arrive at 500 million viable new ideas. The end result: a solution that ensures that Unilever’s new product ideas are truly data-driven by basing them exclusively on consumer wants and needs that come through in their data.

Finalists: 

Most Impactful Ikig.AI Story Award

ALMA Observatory

A major data-related challenge that the ALMA Observatory currently encounters relates to the quality checks of its observations, with the instruments used not free from sporadic problems and bugs, and elements such as weather conditions also at risk of negatively affecting the data acquisition process. If these issues are not detected early on, a significant amount of processing resources are wasted. 

This was the basis for the Dataiku Community’s first-ever volunteer project. Led by Ignacio Toledo, it brought together 14 users from across the globe to collaborate and learn from each other’s experimentation, supported by Dataiku data scientists and staff members from the observatory. One of the outcomes of the challenge: a webapp that’s since been deployed at ALMA, providing astronomers on duty with a tool to check the main quality indicators of the latest observations produced. Within three minutes of the observation, the data is visible within the webapp, enabling the astronomer to quickly notice any anomalies.

ALMA QAo Exploration

In the end, the challenge was a tremendous learning experience for all involved, with some highlights including gaining insight into the nitty-gritty of astronomical research, sharing ideas and progress, and uniting through the common goal of helping ALMA and learning from each other! 

Finalist: The Brilliant Club - Using Data to Support Less Advantaged Students to Access and Succeed at Competitive Universities

Excellence in Teaching Award

Columbia University

As a lecturer at Columbia University, Perry Beaumont encountered the challenge of sourcing real-world data in combination with enterprise platforms to help students bridge textbook learning with hands-on applications. As a result, he committed to working with university publisher Cognella to write a book that would present case studies in data science, titled “Business Case Studies in Applied Data Science: Supply Chains, eCommerce, and Consumer Lending.” 

Together with the Dataiku team, Perry put together a data science learning module for the book using a Google e-commerce dataset and the Dataiku platform. The project has already had a strong impact — early drafts of the book (and the module involving Dataiku in particular) have received tremendous student feedback, with some reporting that it helped them to stand out positively during a job interview and when being asked to participate on various employer teams created to examine analytics challenges.

Finalist: Dayananda Sagar University -  Developing Management Professionals With Data-Driven Problem Solving and Decision-Making Skills

Excellence in Research Award

Akira Insights

Energy producers are dependent on the accuracy of solar power forecasts to meet the grid supply and demand and make decisions on the operational front, with a penalty for not meeting the average power committed to the grid. To reduce cost and improve efficiency, Shivam Rai and his team at Akira Insights looked to develop a deep learning model that consumes sky images, weather data, and sensors data to produce the solar irradiance forecast.

Ultimately, they chose to leverage Dataiku to implement the solution, as it supported rapid experimentation and prototyping, allowed them to achieve a higher level of collaboration as a team, and simplified the whole workflow. The model created through their research represents a key step and can help contribute to the success of the mid-size and wholesale market segment in solar power. 

Finalist: Leidos, Inc. - Staffing Execution Evaluation and Prediction Capability Using Novel Combinations of Statistical and Machine Learning Approaches

Most Extraordinary AI Makers Award

Standard Chartered Bank

Within Standard Chartered Bank’s financial operations/financial planning & analytics department, their financial analysts have been on an upskilling journey. At the beginning of their pursuit of Everyday AI, they had a “10,000 hour” learning challenge in place, but they soon realized it’d become an almost impossible hurdle which meant they could not scale.

In response, Craig Turrell and his team fastened the learning curve, reducing it from 10,000 hours to “100 Days of Coding.” Leading by example, they embarked on the hands-on sprint themselves, triggering 20x to 40x savings for the organization and socializing the challenge on social media to build an even bigger momentum. In the end, they reduced the cost and time to teach new ways of digital learning, helped democratize advanced analytics and ML across the organization, and inspired many more practitioners to join in and uplevel their career.

Finalists: 

You May Also Like

Explaining AutoML: What It Is and How Dataiku Can Help

Read More

Succeed With AI at Scale With These New Year’s Resolutions Tips

Read More

5 Reasons Why Predictive Maintenance Is Overhyped

Read More