Use Data Enrichment to Supercharge AI
AI transforms how we interact with technology, make decisions, and solve complex problems. It has been at the heart of many innovations over the past two years, powering everything from the chatbots that enhance our customer experiences to the predictive analytics engines that help us make financial decisions.
What defines a successful AI initiative, and how can your organization ensure that your investments and hard work deliver maximum value for your organization? The answers lie in data integrity and the contextual richness of the data that fuels your AI.
What Does a Successful AI Initiative Look Like?
Successful innovators invest their resources in projects that deliver value. What does that mean for AI? Here are some of the key factors that define a successful AI initiative:
Improved business outcomes. First and foremost, AI initiatives should drive better business results. Stephen Covey advised that highly effective people should “begin with the end in mind.” That applies to AI initiatives as well. When project leaders have a clear objective aligning with the organization’s strategic goals, they start on the right foot.
Risk and compliance. Businesses must navigate many legal and regulatory requirements, including data privacy laws, industry standards, security protocols, and data sovereignty requirements. The consequences of noncompliance can be severe, including fines, penalties, and reputational damage. Therefore, every AI initiative must occur within a sound data governance framework.
User trust and credibility. Users who see an AI-powered application providing inaccurate results will quickly lose confidence. They may stop using it, and they’re likely to approach future initiatives with skepticism. Once trust has been lost, it can be difficult to regain.
Cost savings. Identifying and correcting errors in your data consumes time and resources. That’s especially true in the case of AI applications. If machine learning models have been trained on untrustworthy data, fixing the problem can be expensive and time-consuming. That can lead to delayed rollouts and lost credibility.
Contextual data. Data integrity is multifaceted. Consistency, accuracy, and completeness are essential, of course, but to get truly outstanding results, it’s important to have a complete picture of reality. That means adding context to your internal data by enriching it with information from trusted external sources.
Context Is Essential
Many organizations fail to attend to the contextual richness of their data before embarking on a new AI initiative. That’s a huge mistake because it leaves out important information about the topic at hand.
Imagine, for example, that you’re developing a predictive model based on retail customers within a specific geographic market. Let’s assume you have some basic information about each customer, including their name, address, age, and products they’ve purchased from you in the past. Imagine accessing more detail based on each customer’s home address. You stand to gain valuable insights into each individual’s income, lifestyle, family status, and more.
You need reference data sets from trusted, authoritative sources like Precisely to do that. We work with organizations around the globe that have diverse needs but can only achieve their objectives with expertly curated data sets containing thousands of different attributes.
Read our eBook
In this ebook, we delve into the crucial data validation and enrichment process, uncovering the challenges organizations face and presenting solutions to simplify and enhance these processes.
Here are some of the categories of enriched data that Precisely offers:
Address and property data, including highly granular information on specific properties, verified and constantly updated with the latest details.
Boundaries such as governmental (cities, towns, etc.), administrative (school districts, census blocks, etc.), neighborhoods, and industry-specific boundaries.
Points of interest including businesses, leisure and recreational sites, emergency services, geographic features, and more.
Streets, railways, and paths, including estimated travel times, paved vs. unpaved, one-way vs. two-way roads, etc.
Risk factors. Precisely has extensive data revealing natural hazards, offering clues about potential damage from flooding, wildfires, earthquakes, tornadoes, and man-made hazards such as high-risk industries, crime, and more.
The PreciselyID Unlocks Valuable Location Context
Precisely can link every address to over 9,000 attributes, offering rich contextual detail for each location. The key to this capability lies in the PreciselyID, a unique and persistent identifier for addresses that uses our master location data and address fabric data.
We assign a PreciselyID to every address in our database, linking each location to our portfolio’s vast array of data. From a data science perspective, this offers tremendous advantages. It makes table joins extremely fast and eliminates the need to deploy complex matching algorithms to associate addresses with their underlying attributes.
The PreciselyID saves on computational overhead, particularly in the case of spatial joins, which are incredibly intensive processes, often requiring a great deal of time to complete.
Enrichment: The Secret to Supercharged AI
You’re not just improving accuracy by augmenting your datasets with additional information. You’re unlocking a whole new world of possibilities. Here are eight ways that enrichment helps to supercharge your AI:
- Model accuracy and performance. Enriched data provides a deeper, more contextual basis for training AI models. That leads to far more accurate and robust models.
- Faster model training. High-integrity data reduces the computational resources required for model development.
- Effective feature engineering. Practitioners can rely on consistent data to extract meaningful features contributing to model performance.
- Reduced overfitting. High-integrity data avoids the introduction of noise, resulting in more robust models.
- Easier model maintenance. By building models around data with integrity, less rework is required because of unexpected issues.
- Reduce preprocessing overhead. Clean data reduces the need for data prep.
- Reliable model deployment. Data that is reliable leads to reliable models that perform consistently and dependably.
- Enhanced model interoperability. Transparent, accurate data aids in the understanding of model decisions, builds trust, and identifies biases.
There are numerous examples of how enriched data can be applied to industry-specific AI use cases. Insurance companies, for example, use data enrichment with location-based information to assess risk accurately. They use data about a property, location, historical weather events, and other factors to score risk and accurately calculate premiums. Real estate professionals use AI enriched with neighborhood data, crime rates, school quality, and local amenities to better assess property values.
By enriching your data before your AI initiatives, you can dramatically improve your AI applications’ accuracy, performance, and overall utility, leading to better business outcomes while saving time and money. Enrichment also enhances user confidence, vastly improving business users’ likelihood of embracing these innovations.
To learn more about leveraging the power of data enrichment to supercharge your AI initiatives, read our free ebook, Validation and Enrichment: Harnessing Insights from Raw Data.