What I’ve Learnt from Nearly Two Decades of Working in Data and AI (Part 1)

An Introduction to Blog Series

Introduction

Have you ever wondered how your favorite pizza bakery chain knows exactly what you’re going to order next?

Imagine walking in, and before you even say a word, they’re already preparing your favorite pizza.

It’s almost like they can read your mind!

Today, we’re going to start with our journey to uncover the secrets behind this magic.

We’ll explore how data and AI come together to help businesses like myPizza Bakery not just understand their customers, but also predict their needs.

Through this series of blog posts, let’s dive in and discover the fascinating world of data and AI!

Over the last two decades, I’ve had the opportunity to work on most aspects of data and AI.

And let me tell you, the landscape is evolving so fast.

But here’s the thing — there are a few concepts that, in my experience, are more important and timeless than others.

These key ideas can give anyone a full, well-rounded understanding of the field.

So, in this blog series, I’m going to condense all of that knowledge and experience into a set of core lessons.

The goal here is simple: to give you a solid understanding of the data and AI space.

And whether you’re just starting out or have been working in this field for years, I truly believe these insights will be useful for everyone.

Now, I’m not just saying this — I’ve lived it.

I’ve been through the grind, and I know what works, what doesn’t, and what really matters.

And honestly, writing this series will help me too.

It’ll let me revise, revisit, and structure my own learning in a more organized way.

But here’s a question I know you might be asking: How are we going to relate these concepts to the real world?

Well, let’s pick something we can all connect with.

Do you love pizza?

I’m pretty sure I know the answer to that one!

So, let’s dive in, and trust me, you’re going to see how relatable data and AI can be.

Let’s get started!

The Use Case — “myPizza Bakery”

Alright, so to keep this series simple and engaging, here’s what we’re going to do.

I’m going to use a hypothetical pizza bakery chain, and we’ll call it myPizza Bakery.

This fictional business is going to be our go-to example throughout the series.

By using myPizza Bakery, we can connect all these data and AI concepts to real-world, practical scenarios.

So, instead of dealing with abstract theories that might feel a little too disconnected.

We’ll bring everything back to things you can relate to — like making pizzas, managing inventory, or even predicting customer preferences.

The idea is simple: by connecting these concepts to everyday operations.

You’ll have a much clearer understanding of how data and AI actually work in practice.

Sounds good? Let’s move ahead!

Let me give you a sneak peek at what we’re going to cover in this series, and of course, we’ll explain everything through our myPizza Bakery example.

1. Data Fundamentals

Now, data is really the foundation of everything when it comes to AI.

If we don’t understand data, we can’t build or analyze anything.

So, in this section, we’ll break down what data actually is, the different formats it can come in.

Think of everything from customer orders to sales to daily operations at myPizza Bakery.

All of that is data.

Before we can use that data for anything meaningful, we need to ensure it’s good quality and flows smoothly through the business.

This is where we’ll dive into concepts like the DIKW pyramid (which stands for Data, Information, Knowledge, Wisdom) and the data lifecycle.

Both of which are crucial to understanding how data works behind the scenes.

Make sense? Alright, let’s continue!

2. Data Engineering

So, once we have a good understanding of what data is, the next step is figuring out how to store it, process it, and integrate it into our systems.

This is where data engineering comes into play.

Think of it just like how myPizza Bakery manages its inventory or tracks sales reports.

The bakery needs a solid process to keep everything organized and running smoothly, right?

Well, data engineering is pretty similar — it’s about building pipelines to move data efficiently from one place to another.

These pipelines are called ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform).

They help us make sure the right data gets to the right place in the right form.

And here’s the thing — without solid data engineering, we wouldn’t be able to handle all the data the bakery generates, especially as it grows.

Just like how managing a small bakery is simpler than running a chain of stores, managing a little data is easy.

But as the business (or data) grows, we need better tools and processes to keep it all under control.

Got it? Awesome! Let’s keep going!

3. Data Architecture

Alright, so now that we’ve got systems in place to store and process data, the next thing we need to think about is making sure these systems can scale as myPizza Bakery grows.

This is where data architecture comes in.

Data architecture is all about designing systems that can handle large amounts of data efficiently and allow for flexibility as the business expands.

Think about it — when myPizza Bakery grows from a single shop to a chain in multiple cities, we need systems that can keep up with all the new customer orders, inventory, and sales data pouring in.

This is where modern concepts like data lakes, Data Mesh, and Lakehouse architectures come into play.

These are flexible, scalable ways to store and manage data.

So, just like how we’d set up bigger ovens or more kitchen space as the bakery expands, we need data architecture that can handle the increasing load as the bakery opens new locations in different cities.

Make sense? Great! Let’s keep moving forward!

4. Data Governance

So now we’ve got more data, and our systems are getting bigger.

But with this growth comes a big responsibility — we need to make sure that all of our data is secure, private, and high-quality.

This is where data governance steps in.

Just like how myPizza bakery has rules for food safety and hygiene, we need rules for how we handle data.

Data governance ensures that we’re taking care of customer data responsibly, following the necessary regulations, and keeping the data clean and accurate.

Think about it — if we don’t have proper governance in place, the bakery could end up facing legal issues or, even worse, lose the trust of its customers.

And in business, trust is everything, right? So just like you’d protect your secret pizza recipe, we need to protect and manage our data properly.

Got it? Awesome, let’s move on!

5. Data Analytics

Alright, so now that we’ve got our data secure and well-organized, what’s next?

Well, the next step is to turn all that data into insights — this is where data analytics comes in.

Think about it like this: myPizza Bakery collects a ton of data, but raw data by itself doesn’t tell us much, right?

With data analytics, we can look at that data and start to see patterns.

For example, we could analyze which pizzas are selling the best, or even predict the busiest times of the week.

This isn’t just about looking at numbers — it’s about helping the bakery make informed decisions.

Should we stock up on more ingredients for the Margherita pizza?

Or maybe we need more staff on Saturday evenings?

Analytics gives us the answers, helping us improve operations and run the business smarter.

See how powerful that is? Let’s keep going!

6. Data Science

Now, let’s take it one step further.

Data science is where things get really interesting. It goes beyond just looking at data; it’s about using advanced techniques to dig deeper.

So, what does that mean for myPizza Bakery?

Well, with data science, we can do things like cleaning the data — making sure it’s accurate and usable — then we can build models and use statistics to find even deeper trends.

For example, we could use data science to predict future demand — like how many pizzas we’ll need to make next weekend.

Or we could figure out why delivery times might be varying from one order to the next.

It could even help us find ways to improve customer satisfaction by looking at feedback data.

Basically, data science helps the bakery take all that raw data and turn it into actionable insights.

That can help us plan ahead and optimize different parts of the business. Pretty cool, right?

7. Machine Learning

Data science naturally leads us into the world of machine learning, which is like the next level.

With machine learning, we’re not just analyzing data anymore — we’re actually building models that can learn from the data and get better over time.

So how does this apply to myPizza bakery?

Well, we could use machine learning to optimize delivery routes.

Imagine the system learning the fastest way to deliver pizzas based on real-time traffic.

Or, it could suggest new pizza flavors to customers based on their previous orders, kind of like how Netflix recommends shows.

It could even automate the ordering process, predicting when we’ll need more ingredients based on sales patterns.

This is where the real power of AI comes in, because now the machines are starting to make decisions on their own, based on the data we’ve collected.

Exciting stuff, isn’t it?

8. AI Fundamentals

Now that we’ve covered machine learning, it’s time to talk about AI itself.

So, what exactly is AI?

Well, AI is when machines not only learn from data but also make intelligent decisions on their own.

It’s like giving our computer brains that can reason and solve problems.

For myPizza Bakery, AI could be used to enhance customer interactions.

Think about a chatbot that helps customers place orders, answer questions, or even recommend the perfect pizza based on their taste preferences.

Another cool application could be using computer vision to automatically monitor ingredient quality, ensuring that every pizza is made with the best ingredients.

AI is like the cherry on top of all the data and machine learning work we’ve been talking about.

It ties everything together and shows how data can truly transform a business.

Throughout this blog series, we’ll keep using myPizza Bakery as our example to simplify and explain key data and AI concepts.

By the end of this journey, you won’t just understand the technical fundamentals — you’ll see how these concepts directly apply to solving real-world business challenges.

Whether it’s processing data, analyzing trends, or making predictions, this series will give you the tools to navigate the fast-changing world of data and AI.

So, grab a slice of your favorite pizza, and let’s dive into this exciting adventure together!

Stay tuned for my upcoming posts of this series.

If you loved this story, please feel free to check my other articles on this topic here: https://ankit-rathi.github.io/data-ai-concepts/

Ankit Rathi is a data techie and weekend tradevestor. His interest lies primarily in building end-to-end data applications/products and making money in stock market using Tradevesting methodology.