Standard Chartered Bank - Building an Intelligent Data Operations for Financial Planning and Perform

LisaB Dataiker, Alpha Tester Posts: 208 Dataiker

Team members:
Craig Turrell, Head of Digital Centre of Excellence P2P, with:
Christopher Harvey
David Rogers
Rajesh A.
Karthik C.
Ramakrishnan D.
Mahesh Iyer
Priyanka Jaiswal
Rajasekar Kanniappan
Benjamin Koh
Vignesh Kp
Vivek Kumar
Naresh Babu
Joshua SamuelK
Santosh Satuluri
Suhas Talanki
Pushya Thimmaiah
Dheerendra Yadav


Standard Chartered Bank

We are a leading international banking group, with a presence in more than 60 of the world’s most dynamic markets. 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.

Awards Categories:

  • Organizational Transformation
  • AI Democratization & Inclusivity
  • Value at Scale


At Standard Chartered Bank, the Financial Operations Plan to Performance (P2P) division works on a broad array of core financial statement and performance management systems of the bank. We need to be able to look five years back and five years forward to identify abnormalities and trends, do balance sheet analytics, and conduct cost analysis to answer complex questions around how and where the bank is making profit, how the bank behaves, who should be hired and where they should be placed as related to cost profiles, etc. We provide the enterprise financial performance data fabric that drives the organisation financial operational and strategic thinking – the data and insight behind the decision.

Of course, we had the systems in place to do all of this for many years, but operationally we were limited to millions of rows of data. While it sounds like a lot, the reality is that teams could provide one or two levels of detail for the 10 core products of the bank, or core primary country markets, and look at basic account structure over about three months - and even at those dimensions, we had to start splitting analysis in pieces. To answer questions for example for cost trend across the entire bank, its cost centres, every account line and every cost centre runs in the nearly 10 trillion possible questions.

So we started digitizing reports for CFOs, but soon realized that this approach wasn’t going to influence the behaviors of the bank. We needed to find a way to impact the day-to-day work of financial analysts, making them more efficient and effective.

When diving into the issue, we found out it was primarily a question of volume to get from 10 million to 400 million rows of data, not a question of underlying infrastructure — in fact, we already had robust compute warehousing, but almost no one was using it. We needed to find a way to leverage that existing ecosystem.


In addition to finding a solution that leveraged our existing infrastructure investments, we didn’t want to have to go looking for another tool again in a few years when our team becomes mature enough to start doing machine learning on their data. That’s when we found Dataiku, and it solved volume straightaway. Within three to four weeks, we managed to turnover a 4.5 billion row table in a single operation.

But Dataiku made us realize we could do so much more than that. We had an army of people copy and pasting data and, since we were now able to centralize all treatment within the platform in a lightning-fast manner, Dataiku allowed us to have different conversations about data.

In the first nine months with Dataiku, the team churned out use cases from around FP&A. The next step was productionalizing their system and patterns, including ensuring there was discipline with data pipelines, SLAs, and more stringent DigitalOps processes. That’s the power of Dataiku: unbounding freedom, but also providing features to facilitate structure and processes. It made our vision possible and our strategy a reality.


The Digital MI team at Standard Chartered Bank, led by Craig Turrell, overhauled three major systems at the bank that produce summary financials and expose performance and planning dashboards to thousands of stakeholders across the bank. This project is a major achievement, automating laborious tasks previously done in spreadsheets, increasing the scale and frequency of analytics, and delivering self-service analytics capabilities in a governed, standardized way.
Key KPIs include:
  • Processing 10 million to 400 million+ rows of data, opening doors to future innovation.
  • Turning 2,500 hours down to a 10 minutes process, using governed process automation.
  • Increasing analyst productivity by a factor of 30 through replacing spreadsheet processes with governed self-service analytics.
  • Accelerating overall time-to-market, delivering their use cases in production in less than 9 months and turning idea-to-prototype time to under 12 weeks.

We’re also developing Standard Chartered Bank’s unique brand of data democratization or self-service analytics, with a center of excellence (CoE) owning the core structured intelligence of the bank. All enterprise-level data is centralized, with product owners for every dataset and defined governance. From there, the team builds specific experiences to deliver answers through core apps, and the ultimate “self-serve” flexibility comes from how people around the organization use those apps to solve business problems in their day-to-day.

The CoE at Standard Chartered Bank is currently made up of 16 people, but they will be expanding to 30 and expect to be hundreds in the next few years to support the growing demand and continue driving efficiencies around the business.

There are numerous communities across the bank leveraging Dataiku and building “digital bridges” to the CoE’s core structured intelligence. On average we estimate that, two people armed with Dataiku are doing the work of about 70 people limited to 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.

In the months and years to come, we will also move into more predictive analytics in the FP&A division, with a focus on predicting within the mid/short term (3 months to a year). The vision is around a “supermind,” or a smart group of independent agents working together to create a benchmark of intelligence (if, say, 10 machines independently make predictions, taking those predictions collectively will probably be close to reality). There will likely be interesting learnings to share in next year’s Awards submission!


  • VinceDS
    VinceDS Dataiker, Alpha Tester, Dataiku DSS Core Designer Posts: 45 Dataiker

    Working with SCB and Craig's team on this project has been a privilege, impressed by the speed and breadth of innovation delivered for Finance Ops processes!

  • tinaresh
    tinaresh Dataiku DSS Core Designer, Dataiku DSS ML Practitioner, Registered Posts: 3 ✭✭✭✭

    What a journey !

    4.5 billion row table in a single operation, it's not a joke and way to go..

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