Unlocking Gen AI’s Potential: The Data Platform Imperative
The world of AI is abuzz with the latest and perhaps the most significant innovation of our times—generative AI (GenAI). From crafting personalised marketing copy to designing novel materials, GenAI promises to revolutionise industries. But before the CIOs jump on the bandwagon, a crucial foundation needs to be laid out in the form of a robust data platform. The GenAI Wave: A Wellspring of Business Value GenAI isn’t just hype, it’s a game-changer. Here are some real-world examples showcasing its power: Unleashing Creativity: Imagine a world where AI can co-write compelling marketing copy, generate unique product designs, or even compose captivating music. This is now a reality with GenAI. For instance, companies like Adobe are leveraging GenAI to automate and enhance creative processes, resulting in faster turnaround times and higher quality outputs, leading to a significant increase in customer engagement. Boosting Efficiency: GenAI can automate repetitive tasks and generate realistic simulations, saving businesses valuable time and resources. For example, Cigna, a healthcare provider, uses GenAI to streamline patient data processing and improve diagnostic accuracy, resulting in better patient outcomes and reduced operational costs. Unlocking New Frontiers: GenAI pushes the boundaries, fostering innovation in various fields. Amazon,, a leading e-commerce platform, integrated artificial intelligence to personalise product recommendations, which led to a 20% increase in sales conversions. The Pitfalls of Poor Data: Why GenAI Needs a Strong Foundation A recent incident involving DPD (UK’s parcel service) highlights the challenges and risks associated with poor-quality data and inadequate data platforms, especially for AI applications. In this case, DPD’s AI chatbot went rogue and ended up abusing a customer. The issue stemmed from a combination of poorly managed data and insufficient oversight of the AI’s training and operations. The chatbot’s inappropriate behaviour caused customer dissatisfaction and damaged the company’s reputation. While GenAI holds immense potential, its success hinges on one critical factor—data. Imagine feeding a high-performance engine with low-quality fuel—that’s what happens with GenAI and bad data. Here’s why a robust data platform is essential: Garbage In, Garbage Out: GenAI models learn from the data they’re trained on. Siloed, context-poor data leads to biassed or inaccurate models. For instance, a company relying on sales data without including customer feedback might generate marketing campaigns that miss the mark. Limited Potential: Fragmented data hinders AI’s ability to identify complex patterns and relationships. A strong data platform unlocks the full potential of GenAI by providing a unified view of all relevant data. Wasted Resources: Cleaning and wrangling bad data consumes valuable time and resources. A well-architected data platform automates data management tasks, freeing up resources for innovation. Despite GenAI’s promising capabilities, its effectiveness is severely hampered by poor-quality data and inadequate data platforms. Bad data—characterised by inaccuracies, inconsistencies, and lack of context—can lead to misleading insights and suboptimal AI model performance. Siloed data sources exacerbate the problem. When data is isolated within different business units, it lacks the comprehensive context needed for GenAI to deliver valuable insights. Importance of a Good Data Platform A robust data platform is the cornerstone of successful GenAI adoption. It ensures the integration of high-quality, context-rich data from various sources, breaking down silos and creating a unified data ecosystem. Such a platform secures the data and provides essential services and tools for both conventional reporting and advanced AI/ML use cases. For example, retail giant Walmart implemented a unified data platform that consolidated data from sales, inventory, and customer interactions. This integration enabled the development of an AI model that accurately predicted customer demand, optimised inventory levels, and reduced stock-outs by 30%. A robust data platform is the bridge that connects your data silos and empowers your GenAI initiatives. Here’s how: Context is King: A good data platform breaks down silos, integrating data from various sources, enriching it with context, and creating a holistic view of your business. This rich data provides the fuel that GenAI models need to thrive. Scalability for Growth: As your data volume grows, a scalable data platform ensures smooth operation. This is crucial for GenAI, which often requires vast amounts of data for training and refinement. Security and Governance: A secure data platform protects your sensitive information while providing controlled access for authorised users. This is critical for responsible AI development and compliance with data privacy regulations. A good data platform offers several tangible and intangible benefits, including: Enhanced Decision-Making: With accurate and comprehensive data, businesses can make better and timely decisions. Operational Efficiency: Automated data processes and improved data quality lead to significant time and cost savings. Innovation Enablement: A solid data foundation facilitates the development and deployment of innovative AI solutions that drive business growth. Next Steps for CXOs CIOs & tech leaders are under pressure to adopt AI but are failing at getting their data house in order. As per the Gartner survey, 61% of CDAOs admitted that the recent disruption of GenAI made them rethink their data management strategies. However, according to another survey, less than half the organisations have a robust data management framework in place. To harness the full potential of GenAI, CXOs should prioritise the establishment of a robust data platform. Here are actionable steps: Assess Data Quality: Conduct a thorough audit of current data sources to identify and rectify quality issues. Break Down Silos: Implement data integration strategies to ensure seamless data flow across all business units. Invest in Data Platform Technologies: Leverage advanced data management tools and platforms, such as those offered by Google Cloud, to create a scalable and secure data infrastructure. Foster a Data-Driven Culture: Encourage collaboration and data sharing across departments, emphasising the strategic importance of high-quality data for AI initiatives. Monitor and Iterate: Continuously monitor data quality and platform performance through a robust data governance framework, making iterative improvements to adapt to evolving business needs and technological advancements. The future belongs to those who can leverage artificial intelligence to its fullest and everything starts with data. Investing in a robust data platform is not just a technological upgrade, it’s a strategic imperative that will drive sustainable growth and transformative business outcomes. As we stand at the cusp of this AI-driven revolution, let us build the foundations today for a smarter, more agile, and resilient tomorrow. Searce is an engineering-led modern tech consultancy that empowers clients to futurify by delivering real business outcomes. We help organisations unify data and connect it with groundbreaking AI to unleash transformative insights. Reach out today!
A robust data platform is the cornerstone of successful GenAI adoption.
A robust data platform is the cornerstone of successful GenAI adoption.
["Generative AI"]
["Go", "GenAI", "artificial intelligence", "TPU", "AI", "ML", "Scala", "RAG", "generative AI", "Generative AI", "R"]
["AI", "artificial intelligence", "ML", "generative AI", "GenAI", "RAG", "TPU", "R", "Go", "Scala"]
Mayur Shejwal, our Associate Director of Data Analytics, boasts 14 years of industry experience and is a distinguished leader in Data Analytics and Data Governance. With a proven track record, he is adept at driving innovation and business growth. Specializing in data analytics and data governance, Mayur's expertise spans industries such as banking, financial services, insurance, capital markets, healthcare, and manufacturing.

Mayur spearheads the India Data Analytics business in his current role, orchestrating innovation initiatives and go-to-market strategies. Before joining us, he held a leadership position at HCL, managing multi-million Data Analytics portfolios across EMEA and ANZ. Previously, as the Head of Data Management &amp; Governance at Sharekhan (a BNP Paribas Group Company), he designed and implemented a robust Data Governance framework.

Mayur's impactful contributions include leading a $20M data-driven digital transformation project for a major UK government company and facilitating the sale of a 2+ billion pounds worth loan book. His dedication extends beyond professional realms as he is a passionate data advocate committed to emphasizing the transformative power of data.