If you've been following our FIG v2 series, you already know about the [overhauled data catalog](https://faraday.ai/blog/fig-v2-data-catalog) and the broader [announcement of what's coming](https://faraday.ai/blog/coming-soon-faraday-identity-graph-version2). Today I want to go deep on what I think is the most technically significant upgrade in the entire release: historical data.

## What "historical data" actually means

The [Faraday Identity Graph](https://faraday.ai/catalog) has always given you an up-to-date snapshot of every US adult—what we know about them *right now*. FIG v2 changes that fundamentally. Instead of a single current value for each attribute, we have the full history of values going back many years, each one timestamped and annotated with precision and source metadata.

Think of it this way: rather than knowing that someone's income is \$85,000, you now know that their income was \$62,000 five years ago, \$74,000 three years ago, and \$85,000 today. Each of those data points has a timestamp, a confidence level, and information about where it came from. For you, this means every data attribute isn’t just a point-in-time number, but instead a timeline of historical events.

This is possible because Faraday has been a careful steward of consumer data for over a decade. We've been collecting, normalizing, and preserving these observations the whole time. FIG v2 is the first time we've been able to surface all of that accumulated history in a structured, ML-ready format.

## Why this matters most for modeling

The biggest immediate payoff from historical data is better predictive models. To understand why, it helps to think through how model training actually works.

Let's start with a propensity-to-buy model (i.e., a lead score model). When you build that model, you feed the algorithm historical examples: leads that converted and leads that didn't. The algorithm finds patterns—the characteristics that consistently differentiated the converters from the non-converters—and encodes those patterns into a model. Later, when new leads come in, the model applies those patterns to score them based on which is most likely to buy.

Here's the problem that a model without historical data has: if it’s trained without historical data then it is actually training on what the person looks like *after* they purchased, NOT the actual time of purchase. Take a home remodeling brand trying to predict which prospects are likely to purchase new windows. To train that model, you feed it historical examples: people who bought windows and people who didn't. But here's the catch—a person looks very different in the data *after* buying windows than they did *before*. After the purchase, there's a contractor transaction on their record, a home equity line of credit may have appeared, and their home improvement spend has spiked. Those signals weren't there when they were still a prospect. If your training data captures people as they look *after* converting rather than just before, the model learns the wrong patterns entirely—and the prospects it finds will be people who match a post-purchase profile, not a pre-purchase one.

Until now, this was a problem we could only partially solve. If a brand had leads going back three years, we could append current FIG data to those leads, but "current" data for a lead from three years ago is not the same as that person's data *at the time they became a lead*. A lot has changed in three years.

With FIG v2, we can press rewind and assemble a training dataset based on what they looked like just *before* they submitted the lead form and what they looked like after they purchased. The model learns what a genuine pre-purchase prospect actually looks like—and gets dramatically better at finding more of them.

## A first-of-its-kind resource for data science teams

Better internal modeling is the most immediate benefit, but FIG v2's historical dataset also opens up something that hasn't really existed before commercially: a comprehensive, historical consumer dataset that's fully ML-ready out of the box.

Every historical observation comes with the kind of metadata that data scientists actually need—data types, statistical types, directionality guidance, coverage breakdowns, and more. If your team is building your own internal models and wants to incorporate rich consumer context that goes back in time, this is the dataset to do it with.

## What's coming next: change detection

We're also excited about something that historical makes possible further down the road: monitoring when a person's profile changes in a meaningful way.

The most valuable signal isn't always the value of an attribute—it's the fact that the attribute just changed. Someone who *just became* a new homeowner, or *just had* their first child, or *just crossed* an income threshold is a fundamentally different prospect than someone who has been in that state for years. Life events create moments of opportunity, and right now, most brands have no reliable way to know when those moments arrive.

Having a full historical vector for every attribute makes this kind of change detection possible. We'll be sharing more about how we're building that capability in Q2.

## Try it this month

FIG v2 is in beta now and releasing this month. If you want to get your hands on the historical data and see how it affects your model performance, reach out to your account management team. And keep an eye out—we have more deep dives coming before the full release.

FIG v2 adds historical timelines to consumer data, enabling far more accurate predictive models by training on pre-purchase profiles instead of post-purchase ones.

Deep dive: FIG v2 historical data and what it means for your models

We recently announced the biggest update to the Faraday platform ever: an overhauled version of the [Faraday Identity Graph (FIG)](https://faraday.ai/features/faraday-identity-graph). FIG is the foundational dataset on which all other services at Faraday are built and if you want the big picture, check out our [main announcement](https://faraday.ai/blog/coming-soon-faraday-identity-graph-version2).

But today, I want to zoom in and talk about one specific, highly anticipated aspect of FIG v2: the data catalog.

## Starting from scratch

Over the last decade, we’ve learned a lot about how to structure data, and we’ve been continuously improving that whole time. But for FIG v2, rather than continue to make incremental updates, we took those years of learnings and rebuilt the dataset from scratch—ensuring every attribute is standardized, strictly categorized, and crystal clear.

Nowhere is that rigor more obvious than in our new data catalog.

## A better data dictionary

This new data catalog goes far beyond a standard data dictionary. It features an entire dedicated page for every single attribute in FIG v2.

[![FIGv2 data catalogue](/images/blog/fig-v2-data-catalog/data-catalog.png)](/images/blog/fig-v2-data-catalog/data-catalog.png)

Historically, a lot of this highly detailed data has only existed internally at Faraday and clients were required to go through their account management team to get these details. Now, for the first time, we are publishing it so that all of our users can get direct value out of it.

[![FIGv2 data catalogue example](/images/blog/fig-v2-data-catalog/data-catalog-example.png)](/images/blog/fig-v2-data-catalog/data-catalog-example.png)

When you land on these new attribute pages, you won't just find basic metadata like names and descriptions. We are showing you the true depth of the data by surfacing the really important technical details and historical context you need to make decisions, including:

- **Data distribution:** Exactly how many people are observed in each category, or how a numerical attribute is distributed across a histogram.
- **Technical properties:** The API name, category, unit, data type, statistical type, allowed values, and deprecation status.
- **Coverage breakdowns:** Exactly how many individuals have a known value versus a value imputed from their address or geography.
- **Time series and contribution history:** A look at how many observations we've collected over time and the authorities that contributed them.
- **Directionality and interpretation:** Guidance on how to use the data (e.g., how missing values should be interpreted—does a missing value mean "we don't know," or should you assume it's "false"?).

## Built for marketers and data scientists alike

How you use this level of transparency really depends on what you're trying to build.

**If you’re a marketer,** let's say you’re looking for a specific piece of context to personalize an email campaign. In the past, finding that exact attribute usually meant digging through a dense, traditional data dictionary or—more likely—having to reach out to your account manager and wait for an answer. Now, you can skip the middleman, search the catalog yourself, and find exactly what you need to make a campaign feel bespoke.

**If you're a data scientist**, say using Faraday's data for internal modeling, things like data type and directionality matter immensely—they have a massive impact on the resulting accuracy of the models you're building. Instead of hunting for those specs, all of those crucial technical details are now front and center.

## Easier to browse, more to discover

When you start searching through the new data catalog, you might notice the data feels much cleaner. By doing a massive amount of consolidation and normalization behind the scenes, we’ve made FIG v2 much easier to navigate. Better yet, that cleanup created headroom for us to bring in additional vendors and add entirely new attributes.

So go ahead and explore—there is a lot more context about American consumers waiting for you.

**Interested in a specific data point?** If you're looking for a particular attribute or curious about our coverage for a specific demographic, we’re happy to pull those details for you. Just send a note to your account management team directly.

## What comes next

As we noted in our [main announcement](https://faraday.ai/blog/coming-soon-faraday-identity-graph-version2), FIG v2 is set to officially release in April. However, we are currently looking for beta testers to get in early. If you're interested in getting your hands on the new data catalog and taking it for a spin, reach out to your account management team to learn more.

And keep an eye out—we'll be dropping more deep dives into FIG v2 in the coming weeks\!

Explore the new Faraday Identity Graph (FIG) v2 data catalog to access detailed attribute pages, visual data distributions, and technical metadata designed for marketers and data scientists.

Getting inside the FIG v2 data catalog

We’re excited to share a major product update that Faraday will be rolling out next month.

You’re probably familiar with the [Faraday Identity Graph (FIG)](https://faraday.ai/features/faraday-identity-graph), a collection of consumer profile attributes that we curate and normalize from leading data vendors and regularly refresh with new data. This data sits at the heart of Faraday’s platform: whether you use Faraday for predictions, appends, insights, or some combination, you’ve been using FIG all along.

Now we’re preparing to release **FIG v2**, the largest update we’ve ever made to our consumer data system.

More than just fresh data, FIG v2 represents a complete rebuild of the infrastructure that powers our consumer data. The new system introduces capabilities many of our customers have asked for while making the data easier to understand, deploy, and operationalize across marketing and data science teams.

Here’s a preview of what’s coming:

### 1. Fully redesigned collection of 1,500+ attributes

We’re using more expressive and consistent names, and we’ve consolidated unwieldy groups of related attributes into harmonized replacements, making it easier for internal teams to operationalize data and build models faster. We’ve also added hundreds of new details along the way to provide richer consumer signals, ultimately driving better prospect targeting and higher marketing ROI.

### 2. Transparent data catalog

We're launching a new data catalog with more detailed descriptions and better clarity to enable **you** to find the right attributes for your needs. This data documentation enables faster experimentation and model deployment. For an early example, check out the version of the catalog we built for our current [FIG v1](https://faraday.ai/catalog).

### 3. More frequent updates

Our new infrastructure allows us to deploy fresh data from vendors as soon as we receive it, without waiting for scheduled monolithic releases. Fresher data reduces stale targeting and inefficient campaigns. That way you’ll always have the most current data possible for your models and appends so you can react and engage in real time for more timely engagement throughout the customer lifecycle. This is especially important for “trigger” data like recent movers.

### 4. Historical data

Faraday is surfacing the entire history of each datapoint for each person in our graph. Consider this: your own consumer data profile _today_ is very different from what it was just before you bought a house, had a child, or graduated from university. Until now, you’ve only been able to get “current day” data from Faraday. Because FIG v2 uses a person's entire data history, models will be more predictive and able to recognize when consumers are moving closer to purchase. Appends will also be more relevant, and outputs will be ML-ready for your data science team, ultimately driving higher acquisition and conversion rates, alongside better forecasting and lifecycle planning.

### 5. Release pinning

We’ve spoken with many of you who are excited to be always receiving the most up-to-date FIG data. But some clients want to have more control over their refreshes. That’s why we’ll be allowing you to “pin” your account to a specific date, ensuring organizations can adopt new data updates on their own timeline without disrupting production systems. Subsequent FIG releases won’t be applied to your account until you move your pin date out, providing operational stability and allowing leadership teams to plan growth strategies with confidence while maintaining compliance and model consistency.

## Availability and beta access

FIG v2 will enter beta soon, and we’re currently looking for customers interested in early access. If you’re curious, talk to your account management team.

General availability is planned for **April**, at which point our client services team will work with customers to plan their transition. Organizations that prefer to remain on FIG v1 initially will have time to do so.

FIG v2 represents nine months of work across the entire Faraday team, and we’re excited to begin rolling it out. If you'd like to learn more about how this will impact your account, reach out! We can't wait to tell you more.

Faraday Identity Graph v2 is a complete rebuild of Faraday’s consumer data system, introducing redesigned attributes, historical datapoints, faster data updates, and new tools to make consumer data easier to discover, deploy, and operationalize.

Deep dive: FIG v2 historical data and what it means for your models

Getting inside the FIG v2 data catalog

Coming soon: Faraday Identity Graph v2