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Craig Suckling, Worldwide Head of Data Strategy, Amazon Web Services

Describe your career to date 

I fell in love with technology at university while studying computer science. I had been a graphic designer before university, and when I learned to code, it became a new way for me to be creative. After graduating, I cut my data teeth as a management consultant, working on some of the largest enterprise data deployments around the globe at that time. 


After several years of large programme delivery, I moved back to my home country, Zambia, to lead KPMG’s IT advisory practice, and I got a taste for entrepreneurship by founding a digital marketing start-up using machine learning to personalise customer loyalty experiences. 


I then broadened my strategic thinking skills leading Accenture’s UK applied intelligence strategy consulting practice before going on to be head of data for IAG loyalty, where I used data and ML to create unique engagement occasions for 100 million customers across 16 airlines and 1,000 partner brands. 


This variety of experience gave me the foundation required for my current role at AWS. In this role, I have the privilege of working closely with thousands of the world’s leading organisations, helping them to reinvent and apply modern data strategies to their businesses to differentiate in a fast-changing world.

What key skills or attributes do you consider have contributed to your success in this role? 

Staying customer obsessed, challenging myself to think big, and striving to create services that start small but scale fast. Being customer obsessed is about starting with the most important challenges customers are facing, and using that to guide where I invest my energy to create solutions.


At Amazon, we call this working backwards because we start with the customer rather than starting with an idea for a product and trying to bolt customers onto it. Striving to think big, forces me to think outside of current constraints and look around corners to invent solutions that have a greater impact. Starting small to scale fast allows me to test and iterate on new ideas quickly, stay focused on customer priorities, and gain timely feedback before investing further in an idea.


What level of data maturity do you typically encounter across your client base and what tends to hold this back? 

I work with all kinds of organisations, from businesses just getting started with data, to complex global enterprises that are already advanced data leaders striving to break through the next wave of advancement.


The most common challenges organisations continue to face are changing mindsets in the business to use data as an asset, shifting culture to recognise that data is everybody’s responsibility, and tearing down walls in order to share insights across lines of business. 


Related to this is growing data talent and empowering teams to act independently across the organisation. In the same way that technology went through a revolution from monolithic platforms to microservice based architectures, data organisations now also need to restructure to become highly distributed, autonomous units that minimise dependencies to increase agility, and share openly to achieve uniformity.

What trends are you seeing in terms of the data and analytics resources your clients are demanding from you? 

I see three broad trends. Firstly, organisations need to remove bottlenecks to innovate with data at scale as business appetite for data grows. These businesses use product thinking to engineer data products that can be independently understood and applied across teams, they use technology to automate how data is produced and consumed across the business without central governance, and they federate accountability for data ownership to the business. 


Secondly, businesses are harnessing a wider spectrum of internal and external data to optimise decision making. For example, I see customers integrating third-party data such as weather or population data with internal data from digital channels, applications, and IoT devices to enrich decision making. These organisations also collaborate across industry in sharing data. 


Thirdly, businesses are increasingly using AI and ML to remove undifferentiated heavy lifting and create capacity to focus on differentiating value. The application of AI and ML is very broad, but some of the rapidly growing applications are in IT to remove effort in engineering, programming, and testing, and into business teams to interrogate data in natural language, automatically detect anomalies, and forecast opportunity.


What challenges do you see for data in the year ahead that will have an impact on your clients and on the industry as a whole? 

There are a number of broad economic, environmental, and social themes where the role of data remains key and continues to evolve. Economically, global supply chains remain volatile, and markets are unpredictable. Data practitioners need to invent increasingly automated and agile data pipelines, leveraging concepts like no-ETL, and quickly run multiple, complex simulations of possible scenarios with scalable capacity to make the best decisions fast. 


Environmentally we are all challenged to achieve a zero-carbon status, this requires sustainable friendly data architectures with optimised storage and compute to reduce carbon footprint. It also requires organisations to collaborate openly on data across industries to optimise emissions and energy use across value chains. 


With the increased adoption of AI and ML technologies, we need to continue inventing the right tools required to develop accurate, responsible, and fair AI at scale. We are also challenged to reinvent the lines of collaboration between human and machine as generative AI goes mainstream at a faster rate than any technology before it.

How are you developing the data literacy of a) your own organisation and b) your clients? 

At Amazon we maintain a strong culture of learning, reinforced by two of our leadership principles: “Learn and be Curious” and “Dive Deep”. We use collaboration mechanisms such as communities of interest, guilds, and wiki sites to connect, share ideas, and co-create solutions for data across the business, and we provide courses and certifications for our people to self-serve. We take the learnings for how we develop data literacy in AWS and use this to help our customers do the same to shift mindset, connect communities, and grow skills. 


To address the skills gap more broadly, our AWS Machine Learning University has delivered 2,000 scholarships, trained 310,000 developers on machine learning, and made available more than 150 courses on data, analytics, and AI and ML to customers. We also see business native tools such as Amazon QuickSight Q and Amazon SageMaker Canvas helping business users who might not have strong data literacy still easily apply data and ML into their organisations.


How are you tackling the challenge of attracting, nurturing and retaining talent? 

Amazon’s mission is to be the Earth’s most customer-centric company, and we strive to be Earth’s best employer. Diverse teams help us think bigger, and differently, about the products and services that we build for our customers and the day-to-day nature of our workplace. 


The biggest driver for attracting data talent is the ability to contribute to interesting work that makes a difference, and having the autonomy to experiment and innovate. We have a strong culture of innovation, where anyone who has an idea has the freedom to write a working backwards press release, share this with stakeholders, gather feedback, and if there is support, build out a solution for the idea.

Craig Suckling
has been included in:
  • 100 Brands 2020 (EMEA)
  • 100 Enablers 2021 (EMEA)
  • 100 Enablers 2023 (EMEA)