When I was hosting a roundtable at the Government Data Summit in London in October last year, one of our public sector customers said citizens today expect Amazon kind of services from the government - where they’re getting best value for their time and tax money paid.
Recently I met the Chief Data Officer of a large public sector organisation in the UK and he was keen to hear more about Amazon’s transformation into a data-driven company and understand how they build, manage and share their data and artificial intelligence (AI) products at the global scale.
There is an evolving theme amongst UK public sector leaders where they’re looking at the private sector to learn and adopt best practices to accelerate innovation. Public sector leaders are also looking to navigate the shortage of advanced data and AI skills by adopting success in the private sector and leverage innovation driven by large scale players like Amazon to accelerate benefits for citizens.
In this article, I explore three key areas where government leaders can adopt and learn from the Amazon way.
Various public bodies are working towards unifying their data to create more meaningful experiences across the entire citizen journey to become more citizen-centric. Creating a citizen360 and single view of users is essential for government to drive hyper-personalised experience for citizens. However, public agencies are having limited success in this space.
Customer obsession is at the heart of everything Amazon does. Delivering superior hyper personalised experience is foundational for successful e-commerce business. Amazon uses AI and machine learning (ML) for delivering personalised experiences to its customers.
Learning from Amazon, the public sector bodies could adopt the Machine Learning (ML) applications and practices used by Amazon.com for real-time personalised recommendations with no ML expertise required to improve citizen engagement and drive better recommendations for engaging new users, government services, and content - even without historical data. These ML algorithms create higher quality recommendations, responding to the specific needs, preferences, and changing behaviour of citizens.
Government is also envisioning consistent experience for citizens across public agencies. These solutions easily integrate with various platforms, websites, apps, SMS, and email campaign systems, to provide unique citizen experiences across agencies, channels and devices, eliminating high infrastructure and resource costs.
Proactive fraud detection and prevention is critical for government, especially after embarrassing numbers published recently: according to the Chartered Institute of Public Finance and Accountancy (CIPFA), up to £49 billion is lost to fraud every year in the UK. These figures are detrimental to user confidence.
Amazon has built a robust defence against fraud and has been countering fraud in real-time for more than 20 years using AI and ML. Public agencies face the challenge of accurately detecting fraud and adapting to changing threat patterns, while scaling and saving money. Unfortunately, fraud operations teams don’t have enough experts to help them use ML to counter these fraud threats. Amazon uses fully managed service which does everything needed to create, deploy, manage, and scale an end-to-end AI-based fraud detection service in the cloud. This frees up staffs' time to focus more on problem solving than on building these technical solutions.
Government is looking to leverage cloud and modern technologies to build robust data foundations, help pro-growth strategy that drives the UK in building a world-leading data economy, and ensure trust in data use.
To support this strategy, various public sector agencies are on their journey to cloud. As part of that process, they’re migrating and modernising their data and analytics platforms on the cloud.
Amazon.com leverages cloud services that enable analytics and big data processing at enterprise scale. If we look at the complexity of running modern data platforms for Amazon, it is important to cater for a diverse user base (over 80,000 active users) and use-cases, manage hundreds of peta-bytes of data, over 38,000 active data sets including curated business data, with over 900,000 daily analytics jobs.
Amazon uses open systems architecture, which allows choice for compute technology. By being on the cloud, their infrastructure cost is transitioning from CAPEX to OPEX expenditure model. Data engineers work on innovation versus undifferentiated tasks related to managing and maintaining a traditional legacy data warehouse. They’re adopting more of serverless technologies, deploying serverless data pipelines that easily transform data used for resilience, ML, and other use cases. The increased adoption of serverless technology, zero-ETL and devOps best practices to deploy and manage infrastructure is minimising maintenance and usage costs.
Adopting learnings from these Amazon practices will help government to optimise their data platform on the cloud, drive cost efficiencies and free up data resources to work on high value innovation use-cases for citizens.
Becoming a data-driven organisation goes beyond technology and platforms - it requires efforts in transformation around people, skills, processes and culture.
Government agencies can adopt learnings from the Amazon Flywheel to drive this transformation. The concept of flywheel was popularised by author Jim Collins through his book ‘Good to Great’. He describes it as a self-reinforcing loop made up of a few key initiatives that feed and are driven by each other - helping to build a long-term business.
Inspired by this, Jeff Bezos incubated his initial idea for Amazon.com using the Amazon flywheel, an economic engine that uses growth and scale to improve the customer experience through greater selection and lower cost.
Government and public sector agencies undergoing data-driven transformation could accelerate their journey through adoption of the Data Flywheel. The Data Flywheel conceptualised learnings from the success of Amazon flywheel. The Data Flywheel consists of various components, and organisations must take time to identify and develop these for their transformation.
A typical Data Flywheel will have these components: business change, skills training, move data on cloud, run your data estate, build data driven apps, analyse performance, innovate with ML, realise benefits and others.
There is no one thing which powers the data flywheel, rather it moves when each component is working in synchronisation - generating greater power than the sum of its parts. Government could take the time to develop each of these components, implementing the most relevant technologies and procedures at every phase, as they spin the Data Flywheel to accelerate the journey towards becoming more data driven.
Learnings from the private sector could help government accelerate value for citizens as they continue to invest in these modern data and AI technologies.
You can join AWS for its Legacy Modernisation Week webinar series on 24-28 April to learn how to break free from legacy data infrastructure and fast track innovation.