Future of Data Platforms!

Here’s a compelling vision for the future of data platforms from a business perspective.

The progression from asking questions about the past (historical analysis) to the present (real-time insights) is well-understood. Let's take an example of a D2C brand, this might look like:

• Past: “How many transactions did we have last quarter?” or "Which product had the highest return rate?"
• Present: "What's our current inventory level for our top-selling product?" or “Which marketing campaign is driving the most sales today?”

The challenge and opportunity now lie in using data to predict the future, guiding strategic decision-making. Imagine this D2C brand asking questions such as:

• Future: “What will our sales be like next month if we run this promotion?” or “Which customer segments are most likely to churn in the next 30 days?”

Various departments can leverage an AI lakehouse to ask future-focused questions:

• Marketing: "Which customer segments should we target with our next campaign for maximum ROI?"
• Sales: "What are the most promising leads for upselling or cross-selling opportunities?"
• Product: "What new features or product improvements will resonate most with our customers?"
• Finance: "How can we optimize pricing strategies to maximize revenue and profitability?"

The evolution of data platform architectures—from databases to data lakes and now AI- data lakehouses—reflects his journey.?

An AI lakehouse combines the best of data lakes (scalable storage for diverse data) and data warehouses (structured data and powerful querying) and layers AI capabilities on top.

For an enterprise like the D2C brand we were referring to, the ability to answer these future-focused questions can be a game-changer, enabling them to anticipate customer needs, optimize inventory, and make data-driven marketing decisions.

However, AI lakehouses bring new challenges in data governance. The massive amounts of data, coupled with AI's complexity, require robust solutions to ensure:

• Data Quality: Maintaining accuracy and consistency is crucial for reliable AI insights.
• Data Security: Protecting sensitive customer and business information is paramount.
• Model Explainability: Understanding how AI models reach their conclusions fosters trust and enables better decision-making.

Hence builders of AI Lakehouse need to keep these things in mind when choosing the tools to build an AI Lakehouse.

So AI Lakehouses are great, but where should one build them? It is here that Google Cloud stands out due to its vertical integration. It offers:

• AI Hardware (TPUs): Google’s custom-designed chips accelerate machine learning tasks, making AI-powered analytics on massive datasets faster and more efficient.
• Foundational Models: Pre-trained foundational models like Gemini, Gemma, BERT provide a starting point for advanced NLP and generative AI applications within the lakehouse.
• Unified Data Platform: BigQuery, Vertex AI, and other services seamlessly integrate, allowing data scientists and analysts to work within a single environment.

This end-to-end solution, coupled with robust governance tools, streamlines the process of building, deploying, and managing AI-driven applications on the lakehouse, giving businesses a real advantage in extracting actionable insights from their data.

How is your organization leveraging AI and data to answer questions about the future? Share your thoughts and experiences!