Connected Data, Better Insights: Data Enrichment Done Right

I’ve been reading a lot about the “rapid pace of change” – as if change itself is a new thing. The reality is that business has always been defined by rapid change, and change, by definition, is always disruptive to something.

When I joined the workforce, desktop computing, the Blackberry, email, and the dot-com boom were the catalysts that disrupted workplace norms. Today, we’re navigating the complexities of AI, machine learning, and cloud computing.

Through each wave of change, one constant remains: a drive for improved efficiency, particularly in data-driven decision-making. This includes accelerating data access and, crucially, enriching internal data with external information. Today, I see great potential in artificial intelligence (AI) applications to make those goals a reality – bringing data from different sources and formats together, identifying relationships and patterns, and deriving insights that were previously difficult or impractical to detect.

That’s where a new generation of data enrichment comes into play, adding depth and nuance to your existing data in combination with AI to learn and understand more.

It’s worth noting that the fundamentals of data enrichment – identifying, cleansing, and standardizing data – haven’t changed much. But what has evolved is the accuracy, speed, and scale at which it happens.

There are game-changing advancements that will make enrichment easier than ever for your business – directly targeting your biggest challenges so you can make data-driven decisions and unlock value faster than ever.

What is Data Enrichment?

First, we’ll start with the basics in case a refresher is needed. What is data enrichment?

Data enrichment is the process of augmenting your organization’s internal data with trusted, curated third-party datasets. This delivers real-world context about the places, people, properties, businesses, and environmental risk factors that matter most to your business.

This, in turn, helps you reveal previously hidden patterns that transform your data into a more valuable resource, improving its usability and helping you make more informed decisions.

When done right, data enrichment is invaluable. It’s key to delivering the context required to achieve overall data integrity. And yet, there are inherent struggles with this process – especially when integrating data from multiple providers.

The Multiple Data Provider Challenge

If you rely on data from multiple vendors, you’ve probably run into a major challenge: the datasets are not standardized across providers.

This makes it difficult to seamlessly integrate datasets, forcing your team to do the heavy lifting of resolving duplicates, validating records, and ensuring consistency – every single time you bring in a new data source.

Not to mention, providers frequently update their datasets without warning, adding new fields or changing structures in ways that break your existing processes. That leaves your data engineers to constantly troubleshoot, remap fields, and adjust workflows just to keep things running. And the challenge only grows as more data sources are added.

Instead of unlocking insights and extracting value, adding more data often creates complexity and errors.

I like to compare this problem to having multiple power cords – one for your phone, another for your headphones, and yet another for both your laptop and tablet. Each only works with its dedicated device, leaving you to haul around a bag of tangled cords (like my personal computer bag).

Sorting through them every time you need to charge something is a hassle, and if you forget one, you’re limited in how much you can use the impacted device. Your choices? Buy a lot of replacement cords, keep track of multiple cords (a pain) or commit to a single brand (which doesn’t always work out in the long run but does limit your options).

These options are far from ideal – not to mention, if the manufacturer changes its design, you’re forced to upgrade everything or go back to the tangled mess.

The dream option would be a universal, future-proof adapter – one that allows you to pick the device that’s best for you, regardless of brand.

Well, my dream power cord solution has yet to materialize … but, I am thrilled that there is now a solution to the challenges of data enrichment with multiple providers – the equivalent of a “universal data adapter,” if you will.

Unlocking Value with Pre-Linked Datasets

Today, you’re able to access

You can pick the best data for your needs, without being limited by a specific vendor’s ID system or fearing the complexity of managing all the overhead. All of that tedious mapping and standardizing has been done for you at the vendor level, to make your efforts so much smoother.

We also often hear from customers that vetting and privacy compliance are two big obstacles when it comes to onboarding new data. Well, those processes are now streamlined like never before. You can feel secure knowing that all data you access has met rigorous criteria on these fronts.

What are the big picture results? Think:

• No more overly complex integrations
• Faster time-to-value
• Bigger ROI

All of this while reducing the risks and costs typically associated with adding more content to the enrichment processes.

Think of all the time and money you’ll gain back for higher-value initiatives when you’re not bogged down by baseline data issues – that’s the impact of this groundbreaking new partnership of providers, and this is just the beginning.

I see programs like this as a win on all fronts: first and foremost for our customers (for all of the reasons outlined above), but also for us data providers.

I’m incredibly energized about the potential for even greater collaboration and innovation in the future. Every new partner strengthens our ecosystem and provides the tools to solve even more of the challenges our joint customers face.

Together, we’re creating a correct and connect data ecosystem that drives better outcomes for everyone.

Correct and Connect

You may be wondering what I mean by “correct and connect,” so let’s talk more about why it needs to be a guiding principle for your data enrichment initiatives – particularly when dealing with multiple datasets.

“Correct and connect” describes the continuous data maintenance loop in which data is sourced, integrated into your operations, and used across your enterprise.

ID-to-ID linkage is the most effective way to achieve this, and an ecosystem of trusted, pre-linked datasets does just that – enabling you to correct your data, connect it to the pre-linked datasets, and unlock unparalleled value at every step. We’ve introduced Data Link as the ecosystem Precisely is building with our data partners to expand correct and connect.

This brings us to the next big area that I’m excited about.

The Transformation of the Data Marketplace

Time has shown that most data buyers may start their search in a marketplace, but these platforms rarely provide enough detail to support a major purchase — resulting in direct relationships with the data providers

Does this resonate with any experiences you’ve had browsing data marketplaces?

I’ve found that many data marketplaces have been the equivalent of a “data flea market” – meaning you might find multiple vendors offering the data you’re looking for, but you still need to closely sort through the options, select candidate suppliers, test them, select a supplier, negotiate terms, and ultimately sign an agreement directly with the supplier.

So ultimately, traditional marketplaces haven’t really simplified the buying process – they’ve ended up serving more as directories, rather than transaction hubs.

That’s what makes new developments in data marketplaces, particularly Snowflake, so exciting: you can instantly access pre-loaded data and content. You no longer need to download data from a provider into a staging environment, run intake checks, and then transfer it to Snowflake to check it again.

If you’re a data analyst, engineer, or data scientist, that means the heavy lifting is taken off your plate – you can immediately work with the data, without worrying about the logistics of ingesting and integrating data from multiple providers.

Having the vendor data available in Snowflake, along with native apps, means a lot less overhead and a lot more efficiency in getting access to and processing the data – especially when you factor in those critical ID-to-ID connections that we discussed earlier.

This is particularly important for use cases requiring address data – which is notoriously difficult to work with. When your address data is incomplete, outdated, or incorrect in the cloud, it can lead to cascading errors that disrupt workflows for systems that require location-based insights, like routing or service delivery.

You can tackle these challenges with native geo addressing and data enrichment apps that help you reduce complexities, cut costs, and eliminate the need to navigate multiple providers.

Together, all of these capabilities are a real evolution in how you’ll access, handle, and process data, and get meaningful insights.

3 Tips for Success

So, what are some of the best practices to follow to get the most value from these environments? Here are three things you should prioritize:

1. Use curated, enterprise-grade datasets to uncover actionable insights and drive better outcomes across your use cases. For example:
   • Insurers will find tremendous value in risk datasets that offer insights into the potential for wildfires and property fires, floods, crime, and more
   • Retailers will benefit greatly from demographic datasets that describe customers, their preferences, and general population trends
2. Seek out apps that allow you to natively process tasks like geo addressing and enrichment within platforms like the Snowflake AI Data Cloud. This will help you leverage the full potential of those platforms, increase operational efficiency, and enhance data-driven decision-making.
3. Select datasets that are designed to work together. There are an increasing number of data companies that are using ID systems to make the work of using these datasets more effective over time. Examples include the work of the Overture Foundation, Google’s IDs, and Precisely’s Data Link program.  

ID-to-ID Linkage: Real-World Applications of Connected Data

Hopefully by this point, you’re feeling as inspired as I am about the possibilities that these data enrichment advancements can unlock for your data operations, and for your business.

The ability to link data across multiple providers, through unique persistent identifiers, is meeting a need that’s more crucial than ever – one that I’ve heard about repeatedly through my work with various partners and customers. I wanted to share some of those insights with you.

Think about recent, headline-making natural disasters – we’ve seen hurricanes in Florida, wildfires in California, and severe flooding in Vermont reshape entire communities. This leaves business and government agencies rethinking how to assess risk and plan for the future.

I was recently with an insurance customer analyzing aerial imagery of Florida’s coastline. Some homes had been wiped out by the storm, while others remained standing. Why?

The answer was in the data: homes built after 2020 followed stricter building codes and fared much better. That insight is critical. If you’re an insurer, it’s details like that which will ultimately help you best serve your customers with accurately priced policies, guidance for mitigating potential risks, and more.

But getting the full picture to ensure better decisions typically requires even more information – data that often comes from different sources and doesn’t automatically align.

It’s not just insurers facing this challenge. A utility company I worked with needed to track new construction phases – when permits are issued, when roads are built, when residents move in – to plan their service expansions. Again, data that typically comes from various sources and has added painful complexities and headaches to manage.

That’s where data connected through shared identifiers will change everything. When all parties use the same IDs for properties, businesses, and infrastructure, integration becomes seamless. Instead of spending time reprocessing and rematching, you can trust that the data you’re working with is already aligned.

Utility companies can better track a construction project’s progress without manual verification; Insurance providers can flag missing or incorrect data with confidence, knowing exactly which property record it refers to.

The entire process moves from reactive problem-solving to confident, proactive decision-making.

Cut Enrichment Complexities – Get Actionable Insights, Faster

The ability to seamlessly enrich data from multiple sources, where your data resides, has become a must-have capability.

There’s already incredible momentum and advancements for your business to leverage in this area, and they all aim to cut the complexities, reduce overhead, and boost efficiency so you can accelerate your time to value with meaningful insights and decisions.

And the great news? There’s even more to come that we can’t wait to share with you. In the meantime, you can take a closer look at the Data Link program and Snowflake Marketplace innovations, and reach out to our team if you’d like to learn more.