March 13th, 2026 at 10:04 am

In 2006, a small internal competition took place at a company that was still transitioning from DVD rentals to streaming. The goal was simple: improve the recommendation algorithm by 10%.

The company offered a $1 million prize to any team that could achieve that target.

That company was Netflix, and the initiative became known as the Netflix Prize.

Thousands of machine learning experts participated. Some of the best algorithms in the world were developed during the challenge.

But the most important takeaway wasn’t the algorithm.

It was the data.

The teams that performed best were the ones that understood how to work with the massive dataset of user viewing behaviour that Netflix had collected over years.

Around the same time, companies like Amazon and Google were discovering the same truth:

AI success is rarely about the model — it is almost always about the data behind it.

Today, this lesson shapes how modern AI products are built.

At Nordstone, this is something co-founder Ronak Shah and his team have seen repeatedly when helping startups integrate AI into their mobile platforms.

Many founders approach AI with a focus on models or tools. But the real question that determines whether AI features succeed is far simpler:

Do you have the right data?

Why AI Fails Without Good Data

Artificial intelligence systems learn patterns from historical information. If the data is incomplete, inconsistent, or poorly structured, the AI model will produce unreliable results.

This is commonly described in machine learning as “garbage in, garbage out.”

Even the most advanced algorithms cannot overcome weak data foundations.

Several common problems cause AI projects to fail:

Incomplete datasets

If key user behaviours or contextual signals are missing, AI models cannot learn meaningful patterns.

Noisy or inconsistent data

Data collected across multiple systems often contains duplicates, errors, or conflicting values.

Without cleaning and normalisation, this reduces model accuracy.

Lack of behavioural signals

Many apps track only basic analytics, but AI systems require deeper behavioural insights to make accurate predictions.

Data fragmentation

When user data is scattered across separate systems — CRM tools, analytics platforms, backend databases — it becomes difficult to create a unified AI dataset.

Lessons from Industry Leaders

Large technology companies succeeded with AI not because they had better algorithms initially, but because they built data ecosystems early.

Amazon

Amazon’s recommendation engine analyses:

• Browsing history 
• Purchase behaviour 
• Search queries 
• Product interactions 

This behavioural data allows the company to predict what customers are most likely to buy.

Today, recommendation systems reportedly influence a significant portion of Amazon’s sales.

Google

Google’s search algorithms rely on enormous datasets that include:

• User queries 
• Click behaviour 
• Location signals 
• Historical search patterns 

These data signals power the AI models behind modern search results.

Netflix

Netflix collects granular viewing data including:

• Watch duration 
• Pause and rewind actions 
• Viewing time of day 
• Device usage 

This allows its recommendation systems to become increasingly personalised.

These companies demonstrate a simple principle:

AI success follows strong data foundations.

How Nordstone Applies These Lessons

At Nordstone, the same data principles guide how AI-powered products are built for clients.

According to Ronak Shah, many early-stage startups initially focus on AI features before considering the data strategy behind them.

But successful AI products are usually designed in the opposite order.

First comes the data architecture, then the AI layer.

When working with startups building intelligent mobile platforms, the Nordstone team focuses on:

• Identifying key behavioural signals
  • Designing scalable data pipelines
  • Structuring datasets for AI models
  • Ensuring data integrity from day one

This approach ensures AI features improve over time rather than stagnating.

Data Collection Strategies for AI Products

The first step in building a strong AI system is collecting the right data.

Effective data collection strategies typically focus on behaviour, context, and outcomes.

Behaviour tracking

Behavioural data is the most valuable dataset for AI-powered applications.

Examples include:

• Clicks and taps 
• Feature usage 
• Session duration 
• User navigation paths 
• Purchase decisions 

This data reveals how users interact with products in real environments.

Contextual signals

Context helps AI models understand why behaviour occurs.

Useful contextual data includes:

• Location 
• Time of day 
• Device type 
• Network conditions 

When combined with behavioural data, contextual signals dramatically improve AI predictions.

Outcome data

Outcome data measures the result of user interactions.

Examples include:

• Purchases completed 
• Workouts finished 
• Subscriptions upgraded 
• Churn events 

Outcome signals allow AI models to learn which actions lead to success.

Why Behavioural Data Is the Most Valuable

Among all data types, behavioural signals typically have the greatest impact on AI performance.

This is because behaviour reveals intent.

For example:

• How frequently a user opens an app
  • Which features they use most
  • When they abandon a process
  • What content they engage with

Over time, behavioural datasets allow AI models to predict future actions with high accuracy.

This is the foundation behind many systems used in Predictive Analytics.

Building Effective Data Pipelines

Once data is collected, it must be processed and structured before AI models can use it.

This is the role of data pipelines.

A typical AI data pipeline includes:

Data ingestion

Collecting information from multiple sources such as mobile apps, APIs, and backend systems.

Data processing

Cleaning, normalising, and transforming raw data into structured datasets.

Data storage

Storing datasets in scalable environments such as data warehouses or cloud storage systems.

Model training

Feeding prepared datasets into machine learning models for training and optimisation.

Well-designed pipelines ensure AI models receive high-quality, consistent data.

Data Governance in AI Systems

As AI systems become more powerful, data governance becomes increasingly important.

Governance frameworks ensure data is:

• Accurate
  • Secure
  • Compliant with regulations
  • Ethically used

For companies building AI products, governance typically involves:

• Access controls 
• Data retention policies 
• Privacy protection 
• Regulatory compliance 

Strong governance practices also build user trust, which is essential for AI-powered platforms.

The Future of Data-Driven AI Products

The next generation of AI products will rely even more heavily on data.

Advances in machine learning are making models more powerful, but the companies that succeed will still be those with the best data ecosystems.

Startups that prioritise data quality early will gain a significant advantage as their AI models learn and improve faster.

For founders building AI-enabled apps today, the lesson from companies like Netflix, Amazon, and Google remains clear:

Data quality determines AI success.

FAQs: Data and AI Product Development

Why is data quality important for AI systems?

AI models learn patterns from data. If the data is inaccurate or incomplete, the model will produce unreliable predictions.

High-quality datasets lead to more accurate, reliable AI outcomes.

What type of data is most useful for AI apps?

The most valuable datasets typically include:

• Behavioural data 
• Contextual signals 
• Historical user interactions 
• Outcome data 

These signals allow AI models to understand user intent and predict future behaviour.

How much data is needed to build an AI product?

The amount of data required depends on the AI model being used.

Some applications can start with thousands of data points, while more advanced systems may require millions of behavioural interactions.

Can startups build AI products without large datasets?

Yes. Many startups begin with smaller datasets and gradually improve their models as user data grows.

Using pre-trained models and AI APIs can also reduce initial data requirements.

What is the biggest mistake companies make with AI data?

The most common mistake is focusing on AI models before building a strong data strategy.

Without structured datasets and reliable data pipelines, even advanced AI technologies struggle to deliver value.