Your Guide to Accurate, Reliable AI/ML – Powered by Data Enrichment

Key Takeaways:

• Data enrichment is the process of appending your internal data with relevant context from additional sources – enhancing your data’s quality and value.
• Data enrichment improves your AI/ML outcomes: boosting accuracy, performance, and utility across all applications throughout your business.
• Overall business benefits of powering your AI/ML initiatives with data enrichment include reduced costs, increased trust, and faster, more confident decision-making.

Businesses across industries are increasingly relying on artificial intelligence (AI) and machine learning (ML) to gain insights, optimize operations, and drive growth.

But effective AI and ML models must be built on a foundation of data integrity. Without the right data, your models are prone to errors that can lead to costly mistakes and missed opportunities.

Data enrichment has a key role to play here. It enhances the quality – and value – of your existing data by adding relevant, trustworthy information. This gives AI/ML models the rich context they need to produce more nuanced, reliable outcomes.

For your AI models to deliver the results you need, data enrichment might just be the game-changer you’re looking for. Let’s dive more into why.

Why an Expanded Perspective is Critical for AI and ML

The adage of “garbage in, garbage out” is particularly relevant when it comes to AI. If your data is flawed, your AI models will produce flawed results. But what if you don’t have all the data?

Here’s a real-world cautionary tale from popular real estate platform, Zillow: the company made headlines after purchasing 9,680 homes in a single quarter – based on suggestions from its AI algorithm. The problem? The recommendations were made` using insufficient data, which lacked the context needed to provide insights into the extent of repairs that would be required, the quality of the homes, the state of the market, and more. This resulted in a staggering loss of over $300 million in one quarter.

Zillow was able to persevere, but many companies don’t have the financial cushion to recover from those kinds of mistakes. This is what makes the breadth and depth of your AI data so essential.

Without expansive high-quality data, even the most sophisticated algorithms can lead your business astray. To maximize the potential of your AI/ML-based solutions, your data needs to meet three essential requirements:

• Complete: All relevant information is included, reducing the need for time-consuming preprocessing.
• Contextual: The data provides the necessary background for your model to make informed predictions.
• Trustworthy: High-quality data from reliable sources prevents models from learning incorrect or misleading patterns.

These three pillars are the foundation of a reliable AI initiative, and data enrichment helps ensure that your models are fed the highest quality data. With that in mind, let’s take a deeper dive into data enrichment.

What is Data Enrichment?

Data enrichment is the process of appending your internal data with relevant context from third-party datasets or data from your other internal systems.

This context expands your understanding of the factors that can impact your business activity – like surrounding neighborhoods and demographics, environmental risks, and even nearby competition. Then, you can unlock valuable hidden insights and reveal relationships that enhance your data’s overall value, accuracy, and usability.

Enriching a dataset with demographic, location, or risk data provides your AI models with more context, leading to smarter insights and decisions. Think back to the Zillow story – if the AI had the context needed to make more nuanced recommendations, the outcome could have been dramatically different.

With access to trusted third-party data sources, you can supplement your existing datasets with additional attributes. At Precisely, our data portfolio is comprised of six main categories:

1. Address and Property
2. Boundaries
3. Demographics
4. Points of Interest
5. Streets
6. Risk

Whatever your use case, these attributes enable you to create a full, holistic view of any location and of your customers – where they live, play, and work.

 Overcoming AI/ML Limitations with Data Enrichment

Again, your AI results will only be as strong and trusted as your data. Whether you’ve already begun your AI/ML initiatives or are planning to start soon, it’s important to know that enriched data helps you tackle the obstacles that stand in your way, and drive results that deliver value.

Here are six examples that highlight the impact of data enrichment on your AI initiatives:

1. Increased model accuracy and performance. Remember, “garbage in, garbage out.” Inaccurate and incomplete training data leads to poor model accuracy and performance – producing low-quality results.
2. Faster model training. High-integrity data reduces the time and computational resources required for model development.
3. Easier model maintenance. Models trained on high-integrity data are easier to maintain, as changes are less likely to cause unexpected issues.
4. Effective feature engineering. Practitioners can rely on consistent data to extract meaningful features that contribute to model performance.
5. Reduced preprocessing overhead. Clean data reduces the need for extensive data prep, simplifying the overall AI pipeline and improving efficiency.
6. Reduced overfitting. Data with integrity avoids introducing noise that contributes to overfitting, resulting in more robust models.

All of these elements add up to big results across your organization:

• Improved accuracy and performance
  By supplementing your existing data with enriched, contextually relevant data, you help AI models make more accurate predictions and uncover deeper insights. This leads to fewer errors and more reliable outcomes.
• Increased user trust
  When your end users experience high-quality outcomes that are accurate and consistent, your customers will gain trust in your systems.
• Faster and more confident decision-making
  Data enrichment allows you to make informed decisions faster, based on complete, trustworthy data. Utility companies, for example, can use data enrichment to determine where to place power lines. With the right enriched data, their AI models can deliver insights quickly and prevent costly mistakes.

• Scalability and Innovation
  Enriched data empowers you to scale AI initiatives without sacrificing accuracy, giving your business the flexibility to innovate.

The Bottom Line: Data Enrichment Powers AI/ML Success

For AI and ML models to succeed, they need to be built on a solid foundation of high-quality data, and data enrichment is key to providing rich context that’s required for that foundation. When you ensure that your models have access to complete, accurate, and relevant data, you’ll maximize their potential.

It’s time to leverage data enrichment for AI to reduce costs, boost trust, and make better decisions that take your business to the next level. Read our eBook Trusted AI 101: Tips for Getting Your Data AI-Ready to learn more.