India’s digital journey has been nothing short of remarkable, driven by a robust Digital Public Infrastructure (DPI) framework known as the India Stack. Over the past decade, the government, in collaboration with public and private entities, has built this digital ecosystem to empower citizens, improve governance, and foster economic growth.
The India Stack is a set of open APIs and platforms that provide a foundation for large-scale public service delivery and innovation. It enables governments, businesses, startups, and developers to leverage technology to offer services to millions of Indians, especially those in underserved areas.
The India Stack is viewed as a layered infrastructure, addressing identity, payments, data, and services.
Click here to download The India Stack: A Foundation for Digital India as a PDF
Four Pillars of the Digital Stack
The four layers of India Stack include:
- Presenceless Layer. Aadhaar enables remote authentication, providing a digital ID that requires only a 12-digit number and a fingerprint or iris scan, eliminating the need for physical documents. It prevents duplicate and fake identities.
- Paperless Layer. Reliance on digital records, using Aadhaar eKYC, eSign, and Digital Locker. It enables secure digital storage and retrieval, creating a paperless system for verifying and accessing documents anytime, on any device.
- Cashless Layer. Led by NPCI, this aims to universalise digital payments. UPI enables instant, secure money transfers between bank accounts using a simple Virtual Payment Address (VPA), moving transactions into the digital age for transparency and ease of use.
- Consent Layer. Enables secure, user-controlled data sharing through electronic consent, allowing data to flow freely. The Account Aggregator ecosystem benefits most, with AA acting as a thin data aggregation layer between Financial Information Providers (FIPs) and Financial Information Users (FIUs).
The Impact of the India Stack
The India Stack has played a pivotal role in the country’s rapid digitalisation:
Financial Inclusion. Aadhaar-enabled payment systems (AePS) and UPI have significantly expanded financial access, increasing inclusion from 25% in 2008 to 80% in 2024, particularly benefiting rural and underserved communities.
Boost to Digital Payments. The India Stack has fuelled exponential growth in digital payments, with UPI processing 10 billion monthly transactions. This has driven the rise of digital wallets, fintech platforms, and digitisation of small businesses.
Better Government Services. Aadhaar authentication has improved the delivery of government schemes like Direct Benefit Transfers (DBTs), Public Distribution System (PDS), and pensions, ensuring transparency and reducing leakages.
The India Stack: A Catalyst for Startup Success
The India Stack is fuelling startup innovation by providing a robust digital infrastructure. It enables entrepreneurs to build services like digital payments, eCommerce, and financial solutions for underserved populations. Platforms such as Aadhaar and UPI have paved the way for businesses to offer secure, seamless transactions, allowing startups like Paytm and BharatPe to thrive. These innovations are driving financial inclusion, empowering rural entrepreneurs, and creating opportunities in sectors like lending and healthtech, supported by global and domestic investments.
From Local Success to Global Inspiration
The impact of the India Stack’s success is being felt worldwide. Global giants such as Google Pay, WhatsApp, and Amazon Pay are drawing inspiration from it to enhance their global payment systems. Alphabet CEO Sundar Pichai plans to apply lessons from Google Pay’s Indian experience to other markets.
While India Stack has achieved significant success, there is still room for improvement. Strengthening data privacy and security is crucial as personal data collection continues to expand. The Digital Personal Data Protection Act aims to address these issues, but balancing innovation with privacy protection remains a challenge.
Bridging the digital divide by expanding Internet access and improving digital literacy, especially for rural and older populations, is key to ensuring that everyone can benefit from the India Stack’s advantages.
At a recently held Ecosystm roundtable, in partnership with Qlik and 121Connects, Ecosystm Principal Advisor Manoj Chugh, moderated a conversation where Indian tech and data leaders discussed building trust in data strategies. They explored ways to automate data pipelines and improve governance to drive better decisions and business outcomes. Here are the key takeaways from the session.
Data isn’t just a byproduct anymore; it’s the lifeblood of modern businesses, fuelling informed decisions and strategic growth. But with vast amounts of data, the challenge isn’t just managing it; it’s building trust. AI, once a beacon of hope, is now at risk without a reliable data foundation. Ecosystm research reveals that a staggering 66% of Indian tech leaders doubt their organisation’s data quality, and the problem of data silos is exacerbating this trust crisis.
At the Leaders Roundtable in Mumbai, I had the opportunity to moderate a discussion among data and digital leaders on the critical components of building trust in data and leveraging it to drive business value. The consensus was that building trust requires a comprehensive strategy that addresses the complexities of data management and positions the organisation for future success. Here are the key strategies that are essential for achieving these goals.
1. Adopting a Unified Data Approach
Organisations are facing a growing wave of complex workloads and business initiatives. To manage this expansion, IT teams are turning to multi-cloud, SaaS, and hybrid environments. However, this diverse landscape introduces new challenges, such as data silos, security vulnerabilities, and difficulties in ensuring interoperability between systems.
A unified data strategy is crucial to overcome these challenges. By ensuring platform consistency, robust security, and seamless data integration, organisations can simplify data management, enhance security, and align with business goals – driving informed decisions, innovation, and long-term success.
Real-time data integration is essential for timely data availability, enabling organisations to make data-driven decisions quickly and effectively. By integrating data from various sources in real-time, businesses can gain valuable insights into their operations, identify trends, and respond to changing market conditions.
Organisations that are able to integrate their IT and operational technology (OT) systems find their data accuracy increasing. By combining IT’s digital data management expertise with OT’s real-time operational insights, organisations can ensure more accurate, timely, and actionable data. This integration enables continuous monitoring and analysis of operational data, leading to faster identification of errors, more precise decision-making, and optimised processes.
2. Enhancing Data Quality with Automation and Collaboration
As the volume and complexity of data continue to grow, ensuring high data quality is essential for organisations to make accurate decisions and to drive trust in data-driven solutions. Automated data quality tools are useful for cleansing and standardising data to eliminate errors and inconsistencies.
As mentioned earlier, integrating IT and OT systems can help organisations improve operational efficiency and resilience. By leveraging data-driven insights, businesses can identify bottlenecks, optimise workflows, and proactively address potential issues before they escalate. This can lead to cost savings, increased productivity, and improved customer satisfaction.
However, while automation technologies can help, organisations must also invest in training employees in data management, data visualisation, and data governance.
3. Modernising Data Infrastructure for Agility and Innovation
In today’s fast-paced business landscape, agility is paramount. Modernising data infrastructure is essential to remain competitive – the right digital infrastructure focuses on optimising costs, boosting capacity and agility, and maximising data leverage, all while safeguarding the organisation from cyber threats. This involves migrating data lakes and warehouses to cloud platforms and adopting advanced analytics tools. However, modernisation efforts must be aligned with specific business goals, such as enhancing customer experiences, optimising operations, or driving innovation. A well-modernised data environment not only improves agility but also lays the foundation for future innovations.
Technology leaders must assess whether their data architecture supports the organisation’s evolving data requirements, considering factors such as data flows, necessary management systems, processing operations, and AI applications. The ideal data architecture should be tailored to the organisation’s specific needs, considering current and future data demands, available skills, costs, and scalability.
4. Strengthening Data Governance with a Structured Approach
Data governance is crucial for establishing trust in data, and providing a framework to manage its quality, integrity, and security throughout its lifecycle. By setting clear policies and processes, organisations can build confidence in their data, support informed decision-making, and foster stakeholder trust.
A key component of data governance is data lineage – the ability to trace the history and transformation of data from its source to its final use. Understanding this journey helps organisations verify data accuracy and integrity, ensure compliance with regulatory requirements and internal policies, improve data quality by proactively addressing issues, and enhance decision-making through context and transparency.
A tiered data governance structure, with strategic oversight at the executive level and operational tasks managed by dedicated data governance councils, ensures that data governance aligns with broader organisational goals and is implemented effectively.
Are You Ready for the Future of AI?
The ultimate goal of your data management and discovery mechanisms is to ensure that you are advancing at pace with the industry. The analytics landscape is undergoing a profound transformation, promising to revolutionise how organisations interact with data. A key innovation, the data fabric, is enabling organisations to analyse unstructured data, where the true value often lies, resulting in cleaner and more reliable data models.
GenAI has emerged as another game-changer, empowering employees across the organisation to become citizen data scientists. This democratisation of data analytics allows for a broader range of insights and fosters a more data-driven culture. Organisations can leverage GenAI to automate tasks, generate new ideas, and uncover hidden patterns in their data.
The shift from traditional dashboards to real-time conversational tools is also reshaping how data insights are delivered and acted upon. These tools enable users to ask questions in natural language, receiving immediate and relevant answers based on the underlying data. This conversational approach makes data more accessible and actionable, empowering employees to make data-driven decisions at all levels of the organisation.
To fully capitalise on these advancements, organisations need to reassess their AI/ML strategies. By ensuring that their tech initiatives align with their broader business objectives and deliver tangible returns on investment, organisations can unlock the full potential of data-driven insights and gain a competitive edge. It is equally important to build trust in AI initiatives, through a strong data foundation. This involves ensuring data quality, accuracy, and consistency, as well as implementing robust data governance practices. A solid data foundation provides the necessary groundwork for AI and GenAI models to deliver reliable and valuable insights.
The White House has mandated federal agencies to conduct risk assessments on AI tools and appoint officers, including Chief Artificial Intelligence Officers (CAIOs), for oversight. This directive, led by the Office of Management and Budget (OMB), aims to modernise government AI adoption and promote responsible use. Agencies must integrate AI oversight into their core functions, ensuring safety, security, and ethical use. CAIOs will be tasked with assessing AI’s impact on civil rights and market competition. Agencies have until December 1, 2024, to address non-compliant AI uses, emphasising swift implementation.
How will this impact global AI adoption? Ecosystm analysts share their views.
Click here to download ‘Ensuring Ethical AI: US Federal Agencies’ New Mandate’ as a PDF.
The Larger Impact: Setting a Global Benchmark
This sets a potential global benchmark for AI governance, with the U.S. leading the way in responsible AI use, inspiring other nations to follow suit. The emphasis on transparency and accountability could boost public trust in AI applications worldwide.
The appointment of CAIOs across U.S. federal agencies marks a significant shift towards ethical AI development and application. Through mandated risk management practices, such as independent evaluations and real-world testing, the government recognises AI’s profound impact on rights, safety, and societal norms.
This isn’t merely a regulatory action; it’s a foundational shift towards embedding ethical and responsible AI at the heart of government operations. The balance struck between fostering innovation and ensuring public safety and rights protection is particularly noteworthy.
This initiative reflects a deep understanding of AI’s dual-edged nature – the potential to significantly benefit society, countered by its risks.
The Larger Impact: Blueprint for Risk Management
In what is likely a world first, AI brings together technology, legal, and policy leaders in a concerted effort to put guardrails around a new technology before a major disaster materialises. These efforts span from technology firms providing a form of legal assurance for use of their products (for example Microsoft’s Customer Copyright Commitment) to parliaments ratifying AI regulatory laws (such as the EU AI Act) to the current directive of installing AI accountability in US federal agencies just in the past few months.
It is universally accepted that AI needs risk management to be responsible and acceptable – installing an accountable C-suite role is another major step of AI risk mitigation.
This is an interesting move for three reasons:
- The balance of innovation versus governance and risk management.
- Accountability mandates for each agency’s use of AI in a public and transparent manner.
- Transparency mandates regarding AI use cases and technologies, including those that may impact safety or rights.
Impact on the Private Sector: Greater Accountability
AI Governance is one of the rare occasions where government action moves faster than private sector. While the immediate pressure is now on US federal agencies (and there are 438 of them) to identify and appoint CAIOs, the announcement sends a clear signal to the private sector.
Following hot on the heels of recent AI legislation steps, it puts AI governance straight into the Boardroom. The air is getting very thin for enterprises still in denial that AI governance has advanced to strategic importance. And unlike the CFC ban in the Eighties (the Montreal protocol likely set the record for concerted global action) this time the technology providers are fully onboard.
There’s no excuse for delaying the acceleration of AI governance and establishing accountability for AI within organisations.
Impact on Tech Providers: More Engagement Opportunities
Technology vendors are poised to benefit from the medium to long-term acceleration of AI investment, especially those based in the U.S., given government agencies’ preferences for local sourcing.
In the short term, our advice to technology vendors and service partners is to actively engage with CAIOs in client agencies to identify existing AI usage in their tools and platforms, as well as algorithms implemented by consultants and service partners.
Once AI guardrails are established within agencies, tech providers and service partners can expedite investments by determining which of their platforms, tools, or capabilities comply with specific guardrails and which do not.
Impact on SE Asia: Promoting a Digital Innovation Hub
By 2030, Southeast Asia is poised to emerge as the world’s fourth-largest economy – much of that growth will be propelled by the adoption of AI and other emerging technologies.
The projected economic growth presents both challenges and opportunities, emphasizing the urgency for regional nations to enhance their AI governance frameworks and stay competitive with international standards. This initiative highlights the critical role of AI integration for private sector businesses in Southeast Asia, urging organizations to proactively address AI’s regulatory and ethical complexities. Furthermore, it has the potential to stimulate cross-border collaborations in AI governance and innovation, bridging the U.S., Southeast Asian nations, and the private sector.
It underscores the global interconnectedness of AI policy and its impact on regional economies and business practices.
By leading with a strategic approach to AI, the U.S. sets an example for Southeast Asia and the global business community to reevaluate their AI strategies, fostering a more unified and responsible global AI ecosystem.
The Risks
U.S. government agencies face the challenge of sourcing experts in technology, legal frameworks, risk management, privacy regulations, civil rights, and security, while also identifying ongoing AI initiatives. Establishing a unified definition of AI and cataloguing processes involving ML, algorithms, or GenAI is essential, given AI’s integral role in organisational processes over the past two decades.
However, there’s a risk that focusing on AI governance may hinder adoption.
The role should prioritise establishing AI guardrails to expedite compliant initiatives while flagging those needing oversight. While these guardrails will facilitate “safe AI” investments, the documentation process could potentially delay progress.
The initiative also echoes a 20th-century mindset for a 21st-century dilemma. Hiring leaders and forming teams feel like a traditional approach. Today, organisations can increase productivity by considering AI and automation as initial solutions. Investing more time upfront to discover initiatives, set guardrails, and implement AI decision-making processes could significantly improve CAIO effectiveness from the outset.