Building the Right Foundation: Modern Data Platform for AI Applications

This webinar, presented by Microsoft, explores the critical need for a modern data platform to successfully deploy AI applications. It covers how to assess your current data infrastructure, identify areas for improvement, and build a robust data foundation ready for AI-driven innovation.

The Foundation of AI: Data

AI applications are everywhere now, and they're only getting more common. But what makes them work? It's all about the data. If you want to build good AI, you need a solid data platform underneath it. This isn't just about collecting data; it's about making sure that data is ready to be used for smart applications.

Understanding Analytics Maturity

When we talk about AI-enabled applications, we're really talking about how mature your analytics are. This maturity depends on your modern data platform. It's like asking: "Where is my data? Is it good enough to support the analytics I need, whether it's AI, business intelligence, or machine learning?"

It's not just about having data; it's also about how you manage it. Are you governing your data? How are you handling access to it? These are all things to think about when you're looking at your data platform's maturity.

Key Takeaways

• Data is central to AI: Without good data, AI applications won't perform well.

• Analytics maturity depends on the data platform: A strong data foundation is key to advanced analytics.

• Data governance and access management are crucial: Knowing where your data is and who can use it is vital.

The AI Application Stack

AI-enabled applications are all about the user experience. But for that experience to happen, the application needs to run somewhere. This could be in the cloud, in your own data center, or a mix of both. To make this work, you need a lot of different pieces:

• AI Platform Frameworks: These are the basic tools that support AI.

• AI Development Stack: This is where you build and refine your AI models.

• Application Layer: This is the user-facing part, like web interfaces or other ways users interact with the AI. It's not just text, like with ChatGPT; it can be visual, speech-to-text, and more.

• Data Management: This is a big part of the picture, making sure data is handled correctly.

• Application Framework: The structure that holds the application together.

• Security: Everything needs to be secure, especially when dealing with sensitive data.

Why Modernize Your Data Estate?

To really get the most out of analytics, you need to think about modernizing your data. This means looking at the data you have and deciding if it's good enough for your analytics needs. If your data environment hasn't been updated in a long time, you might need to consider newer technologies like data lakes, scalable databases, or NoSQL databases.

Even if you've already modernized, you still need to keep up with new capabilities, like data fabric, which lets you access data wherever it lives. The world of AI and analytics is always changing, and your data needs to keep up.

The Importance of a Modern Data Platform

Why does all this matter? Here are some reasons:

• Increasing Data Volumes: Data is constantly being created, and there's more of it all the time.

• Real-time Data: Data often comes in real-time, and you need to be able to handle it quickly.

• Data Life Cycle: You need to think about where to put data, how to manage it, and how long to keep it.

• Security: It's important to control who can access sensitive data.

• New Deployment Models: How you store data is changing, with options like object storage, relational databases, and NoSQL.

• Changing Data Structures: Relational databases don't solve every problem anymore; you need to be able to handle data that changes its structure.

• Advanced Analytics and Machine Learning: These new AI tools require a robust data foundation.

We're moving from older ways of doing things to modern and innovative approaches. The goal is to support business needs, whether it's making operations more efficient or gaining a competitive edge.

The Intelligence Evolution: Discovery, Enablement, Automation

1. Discovery: This is about finding new opportunities through analytics. It's not about collecting all your data at once; it's about iterative processes that let you gather the data you need incrementally.

2. Enablement: Once you have the data, this step is about turning it into actionable results for business teams. This helps you make better decisions and, importantly, trust those decisions because you trust your data.

3. Automation: When you trust the process, you can automate it. This frees up people to work on more complex problems. It's about enhancing productivity and getting things done faster.

Roadmap for Success

• Strategize: Understand your main opportunities.

• Envision: Look at areas where you can focus.

• Exploration and Fast Start: Use accelerators to quickly test ideas and see the return on investment.

• Accelerate: Move these successful ideas into the wider organization, creating a repeatable foundation.

• Manage: Focus on security, compliance, and operational efficiency as you scale.

Real-World Examples

• Product Quality: Improving quality and reducing waste in manufacturing. This involved collecting data to measure improvement and determine how to reduce scrap.

• Product Recommender: Increasing sales by recommending related products. This required collecting and preparing data to train an AI that could make product recommendations.

Ultimately, it all comes back to data. A modern data platform helps you understand your data, trust it for decision-making, and apply the right tools to get value from it. This leads to value creation through data collection and management, and then value realization by using algorithms to drive decisions. Business intelligence, which uses reporting to visualize data, also plays a role, often running on cloud infrastructure like Azure to quickly achieve results.