Thredd is revolutionizing business transformation by leveraging high-quality data products, self-service analytics, and AI adoption to empower teams and clients.
Diwakar Patwal
Thredd is transforming data and AI with three key pillars: delivering high-quality data products, enabling self-service analytics, and accelerating AI adoption.
Our goal = to empower teams and clients to unlock data’s potential.
We are charting into a bold territory for data and AI, focusing on three key pillars: delivering data products, enabling self-service analytics for our stakeholders, and accelerating AI adoption.
Our vision is to empower clients and internal stakeholders with data, tools and frameworks to unlock the value of operational data and leverage AI capabilities to unveil fresh opportunities and optimise emerging value to the business.
Data as a Product (not Service) Strategy.
Data as a Product treats data like a commercial product, focusing on delivering high-quality, reliable information that meets specific client’s needs. To build an effective data-as-a-product strategy, it’s essential to treat data as a valuable offering rather than a by-product of operations.
Our data products are built on three core principles: reliability, accessibility, and actionability. This approach ensures data is curated, discoverable, trustworthy, and valuable to our stakeholders.
The first step towards achieving these goals is to establish a robust data and analytics infrastructure which is currently being developed as part of the Technology Transformation initiative. This lays the foundation for building data products that enable our clients to quickly extract insights, accelerate decision-making and fostering a data-driven culture.
Ultimately, a well-executed data-as-a-product strategy empowers our clients to harness their data, reduce operational inefficiencies, and uncover new opportunities for innovation and growth. By treating data as a product, we’re not merely offering services to our clients – we’re empowering them with a competitive edge that can significantly enhance their long-term partnership with Thredd.
A successful Self-service analytics isn't just about tools—it's about creating an ecosystem where everyone can confidently access and analyse data. The foundation of our strategy lies in creating a well-structured data access layer which ensures consistent, trustworthy, governed and accessible data for all. This data access layer is built on top of our centralised data warehouse and allows us to develop and maintain curated datasets that adhere to standardised definitions and business metrics. By implementing data discovery and observability capability in this Data Access Layer, we create a single source of truth where business metrics are clearly defined and data lineage is transparent.
Once the foundational data access layer is in place, intuitive analytics tools elevate data insights to a new level, enabling users to build custom reports or dashboards through simple drag-and-drop interfaces, lowering the technical barriers to entry. We enrich these tools with pre-built templates that enable business teams to create their own analytics and dashboards completely from scratch or from the existing version-controlled, production-ready dashboards created by the data team.
This strategy will help transform our analytics culture by empowering employees to answer critical questions in real time, reducing the backlog of report requests and increasing data-driven decision making across departments.
As we plan our enterprise AI strategy for 2025, we're taking a pragmatic dual-track approach that maximises immediate impact while building for the future. This strategy acknowledges the different maturity levels and requirements of Generative AI versus traditional Machine Learning.
Our primary focus will be on accelerating Generative AI adoption through carefully selected proof-of-concepts. Gen-AI's unique advantage lies in its ability to deliver value without extensive data preparation or custom model training. By leveraging existing foundation models and focusing on prompt engineering, RAG architecture, and multi-agent frameworks, we can rapidly develop and deploy solutions that demonstrate both technical capability and business value. Key enterprise applications include AI-powered customer service automation, intelligent content generation for marketing, automated software development workflows, and advanced legal and compliance review systems.
While prioritising Generative AI for 2025, we remain committed to realising the value of traditional Machine Learning. Whilst our technology transformation project is underway, we'll identify high-impact machine learning use cases and build the supporting data infrastructure. This preparation phase is crucial as ML models require significant historical data and robust pipelines – elements currently being developed in our transformation journey.
This balanced approach allows us to leverage the current GenAI capabilities of our platform to deliver immediate benefits to the business while laying the groundwork for more complex custom ML solutions.
Our Data and AI strategy represents a balanced approach to create lasting business value. Through our three strategic pillars, we are building a foundation that serves both immediate needs and future opportunities. These pillars will guide our project prioritisation to deliver maximum business impact. We’ll explore practical applications of our Data and AI capabilities in upcoming blog posts — stay tuned!
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April 03, 2025
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