Name: Craig Turrell
Title: Head of Digital, Finance Operations
Country: Poland
Organization: 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:
- Most Extraordinary AI Maker(s)
- Most Impactful Transformation Story
Business Challenge:
At Standard Chartered Bank with Financial Operations / Financial Planning & Analytics, we have been on a journey. This journey has taken us from a small team of financial analysts living 'ground-hog' lives trying to get information sources, integrated, and if time discovers value from it - but most of the time we could only publish the numbers and hope someone found it interesting.
This is our beginning in early 2019, and our progress from those early spreadsheet days to enterprise-class pipelining, analytical translation and the ongoing pursuit of everyday AI is well documented. But something that happened in early 2022 shocked us, we started to reach a realisation of the '10,000' hour learning challenge - an almost impossible hurdle that meant we could not scale. So no matter how advanced the tool we were using, it will be worthless because we did not have the talent and structures for managing and using it.
The 10,000 Hours
So the first question is the 10,000 hours - where did this come from? Well, the answer is in our belief there are digital unicorns; analytical engineer experts that have design and hands-on knowledge of the analytical full stack. From data ingestion to normalisation, feature engineering, metrics calculation, machine learning modelling, visualisation and automation - a person who was able to transfer a multi-sided analytical platform and design next-generation analytics.
We broke this down into three broad categories of skills: data pipelining and data structures, metrics+scenarios+machine learning and human-computer interaction/UI UX Design. Each of these required upskills and certification to establish credible skills in a centre of expertise business model. This impacted not only hand-on engineers but up the top of executive digital management - we had critical skills drift inhibiting us scale success.
Business Solution:
Dataiku helped us in three key ways:
- Ongoing evolution of the platform features
- Academy programme
- Partner ecosystem and interoperability
Platform Features:
The ongoing evolution of the Dataiku platform and incremental business value generated by each new release bring ready-to-use business solutions and analytical features which no longer need to be discovered, built and adopted across the team - but comes out of the box. For example, in recent version 11.0 the native time series features no longer requires our team to learn the theory, build a model/visualisation and share the feature - best practice is already there.
The ongoing development of business solutions and best practice templates will also be a game changer. Anything which accelerate the time-to-value and reduces the learning overhead is make significant and immediate value again. We can do more because Dataiku gives us that 'helping hand' to get to best practice.
Academy Program:
This is our driving licence for analytics. It is how we decide how to enable people on our production environment and guardrails in the use of development features and machine learning. The courses are well structured, the video content and use cases are on topic and aligned to real work situations and problems we face.
When were we struggling to learn what to establish a data and machine learning operations (dataOps / MLOps) Dataiku already had a new learning path for us to following and certify it. Even for senior digital executive, such as Craig Turrell (Head of Digital Operations), taking the courses and achieving certification helped close the skills drift and make better platform decisions.
Partner Ecosystem:
Having a broad range of plug-in extensions, interoperability options and cross platform solution not only provided an immediate solution but reduced learning burden 10,000 hours became 300 hours - accelerate time to value + ability to scale analytics & AI.
Impact:
Without Dataiku help:
- Original estimate of learning = 10,000 hours (data, machine learning and visualisation) across three technology stacks to expert level with Digital COE
- Estimated learning cost per engineering - 20,000 USD / 9-months of achieve full-stack delivery productivity
With Dataiku help:
- Following platform improvement in Dataiku, extension of academy programme, utilise of partner/Dataiku solution + 100 days of coding learning sprints = 200-300 hours
- Estimated learning cost per engineering - btwn 1000-500 USD / 2-month to achieve full stack productivity
Value Brought by Dataiku:
100 Days of Coding
The personal journey of Head of Digital FinOps, Craig Turrell, is the best example of not only the impact on upskilling, and enhancement to tech stack efficiency but the network effects of the platform. Dataiku is a multi-sided platform for artificial intelligence and advanced analytics - it is an ecosystem of data, services, standards, and tools upon which different analytical persons individually, but collectively create and extract value. It is co-creative analytical thinking and analytical network effects of the platform and learning environment.
This created an exponentially valuable effect on this critical skills problem as we were able to seamlessly share and co-create learning journeys, tutorials and 'hackathon' challenges in a community-driven learning marketplace enabled by the Dataiku platform and homogeneous data environment.
The 100 Days of Coding was a call to arms to ensure the skills of the most senior digital leader were on par with the rest of the team, not through words but mental sweating through the courses and certification process. Dataiku gave us the environment to build big, fast and intelligent systems - it enabled us to achieve amazing results that were irreversible and transformative.
But the 100 Days of Coding, the improvements in the product platform, the ongoing enhancement of Dataiku Academy and the contribution that partners are continuous extending and enriching the available solution to real business problems allowed us to do something beyond technical.
Dataiku reduced the cost and time to teach new ways of digital and democratise advanced analytics and machine learning ; it reduced the time it takes to become a digital unicorn. It allowed us the see how we got the to 'enterprise' leveraging Everyday AI.
And through socializing our journey on social media, we're now building momentum outside of Standard Chartered Bank!
