Standard Chartered Bank - Pursuing Data Analytics Excellence by Onboarding and Supporting Teams Across All Bank Divisions

Name:

Sianghio Ed Martin Asuncion
Nupur Sunil Mukherjee
Vikram Gupta
Wang Chun
Jun Yan Low
Gayathri Khanna
Madanagopal Elumalai
Harihara Ramasubramanian Venkatesan

Country: Singapore

Organization: Standard Chartered Bank

Standard Chartered Bank in Singapore is part of a leading international banking group, with a presence in 59 of the world’s most dynamic markets for more than 160 years and serving clients in a further 83. Our purpose is to drive commerce and prosperity through our unique diversity, and our heritage and values are expressed in our brand promise, here for good. For more information, please visit www.sc.com/sg.

Awards Categories:

  • Most Impactful Transformation Story

 

Business Challenge:

Standard Chartered Bank Group Data Technology is the central partner in enabling other divisions of the bank to access, prepare, analyze, and visualize data to get critical insights and arrive at key decisions, all of which will allow the bank to compete successfully in the digital economy.

In 2020, the Data Innovation Value Engagement (DIVE) team was formed under Data Technology as a dedicated team that will directly engage with business and other functions to understand their requirements, identify existing solutions within the bank, and explore what is available in the global market.

In a short time, the DIVE team has identified a very strong demand across the enterprise for a robust data analytics platform that can handle the data volume and provide the flexibility required to enable multiple roles and personas, from a business analyst to a managing director. The platform also needed to empower the growing interest in advancing AI/ML as a regular component of data analytics in the bank. License cost, support, and infrastructure requirements will need to be competitive. Lastly, the product also needed to be a better choice not only against its market competitors but also a better choice over building internal custom solutions from scratch.

 

Business Solution:

The platform’s data preparation capabilities, solid architecture, and pipeline engineering are considered the best, while the model development and API building features are considered an exciting new frontier to be further explored. Integration features and execution management have also proven to be a better choice than standalone connectors and code-based job schedulers.

Across the board, there was no doubt that Dataiku was the right solution for their data analytics needs. In less than three years, the DIVE team has continued to onboard and support many teams across ALL bank divisions in their quest for data analytics excellence. What started with just one team (Craig Turrell in P2P finance) has now grown as an Enterprise gold standard in data analytics.

Not only has Dataiku proven to be the best data analytics platform across the enterprise, but the support and expertise provided by the customer success and sales teams have also played a critical role in achieving the success many teams across the bank now enjoy. In partnership with Dataiku’s Customer Success team, the DIVE team:

  • Designed and operated the Citizen Data Science Training Program that familiarized our users with the tool and introduced them to the fundamentals of data science and ML in practice.
  • Created a dedicated training portal for the bank users with content tailored specifically to onboarding different user profiles through self-service modules based on Dataiku Academy.
  • Facilitated building an internal community where users can exchange ideas and help each other with analytical problems.
  • Provided demo and coaching sessions to users and prospective users interested in analytics.

All of this further increased interest in data engineering, analytics, and AI/ML across the enterprise. The numbers are astounding:

  1. A total of 518 SCB staff have undergone the CDS training since 2020, out of which 237 completed the training in 2022- indicating an exponential increase in interest and participation.
  2. A total of 727 Dataiku online training courses have been completed out of 994 enrollments.
  3. A total of 123 certifications have been obtained by SCB staff.
  4. Almost all participants signed up for the Dataiku Academy and community board.
  5. We still continue to receive requests for MORE sessions this year and next year.

The demand for more licenses and the need for training sessions serves as proof that the DIVE team has successfully contributed to increasing interest and demand for data analytics and AI/ML solutions!

 

Value Generated:

As champions of technology enablement, our ultimate goal is to provide long-term and cost-effective solutions to the rest of the bank. While I am not at liberty to give the exact dollar value of the dozens of projects that use Dataiku, I can confirm the following as our key measures of success:

  1. The adoption rate for the platform has more than doubled in the past year.
  2. Demand for training has increased by 400% since 2020.
  3. The retention rate of teams using Dataiku is still at 100%.
  4. All Lines of Businesses (LOBS) are now using Dataiku, and demand is still increasing!

 

Value Brought by Dataiku:

The biggest value we obtained from Dataiku is the strong partnership with the customer success and sales teams in finding ways to generate interest and identifying value in using their product across multiple teams who have raised interest.

Without their support and expertise, it would have been much harder to influence the other LOBs to adopt Dataiku and further their journey in the world of Data Analytics and Engineering.

Other key benefits are as follows:

  1. Improved transparency and auditability of data process flows.
  2. Centralization of data and workbench where all team members can collaborate.
  3. Major upskilling of the workforce through custom and standard resources.
  4. Cost efficiencies in automation projects implemented in Dataiku.
  5. Drastic efficiency improvement of the data analytics stack.
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Publication date:
03-09-2023 01:03 PM
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Last update:
‎09-15-2022 03:03 PM
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