At Faraday, we talk a lot about [Forward-Deployed Data Science (FDS)](/blog/FDS-changes-the-game)—our team of data scientists who work directly with clients to build predictive models, validate results, and ensure you get the most out of AI powered data. This service (although new) has already proven invaluable to our clients. It means that they get not only accurate predictions, but comprehensive strategic plans to ensure that our data and AI models actually drive business value.

But data science is only half of the equation. The best predictive AI models also require another type of data specialist to operate at maximum efficiency: **Data Engineers.** 

And just like our data science team, Faraday’s data engineers don’t disappear behind the curtain—as is often the case at our competitors. Instead they work directly with clients, in a “forward-deployed” capacity. They’re the ones helping you format and structure your data correctly, making sure your onboarding process is as smooth as possible, and reducing or resolving errors  to ensure your pipeline stays live and healthy. If you’ve ever submitted a dataset and gotten feedback on how to improve it, or had someone help troubleshoot an ingestion issue, you’ve already worked with our data engineering team.

Take [Chris Thomas](https://www.linkedin.com/in/krys-tomis/), one of our incredible data engineers, [who coincidentally just wrote a whole blog about how to best avoid pitfalls while importing your data into Faraday](/blog/ingesting-data-pt2). His job—and the job of all our data engineers—is to make sure your data is clean, structured, and ready for predictive modeling. And in just the last weeks he’s helped numerous clients avoid pitfalls that could have set their timelines back by months—thanks Chris\!

So what’s the difference between data science and data engineering, and how do both help you get the most out of Faraday? Let’s break it down like Raygun\!

[![Raygun break dancing](/images/blog/raygun.gif)](/images/blog/raygun.gif)

### Data science: turning data into predictions

Forward-Deployed Data Scientists (FDS) focus on understanding your business goals and applying AI to help you achieve them. They understand the nuances and behaviors of the predictive AI models that power our business, but also can provide strategic advice on the implementation of data that ensures that your predictions drive value. 

Their job is to help you:

* **Define and refine your use case**—what do you want to predict? How can AI drive better decisions for your team?
* **Data discovery and solicitation**–what data is needed for the use case.
* **Build predictive models** that identify key audiences, behaviors, and trends.
* **Validate model performance** to ensure accuracy and impact before deployment.
* **Interpret and apply insights** so you can confidently take action based on data-driven predictions.

For example, if you’re a solar company looking to expand into new markets, an FDS won’t just give you a model—they’ll help you determine the right questions to ask in the first place. How do we ensure that the data used for training isn’t biased by work in existing markets? What is the logical cut off for differentiating promising new markets from a likely low performing market? Which of our [powerful AI use cases or datapoints](/templates) can increase your conversion rates the fastest? By framing the problem strategically, they ensure that your predictions are aligned with real business objectives—helping you make informed decisions that drive sales, optimize marketing spend, and accelerate growth.

### Data engineering: optimizing your data and models

Data Engineers on the other hand focus on the infrastructure that makes data science possible, while working directly with you to ensure your data is in top shape. 

Their job is to help you:

* **Structure your data correctly** before ingestion so it matches well against the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) (FIG).  

* **Troubleshoot data issues** so you don’t lose time on formatting errors.  

* **Automate through properly configured integration** so your data flows seamlessly into and out of Faraday without constant manual updates.

Where data science can be viewed as more strategic and high-level—focusing on where you want to go, why, and how it will affect your business—data engineering is tactical, focused on how to get there efficiently. Engineers ask questions like, “How can we best implement this idea?” or “What changes will make this process more efficient?” 

At Faraday, a Forward-Deployed Engineer (FDE) like Chris undertakes tasks like supporting integrations with your data warehouse, reviewing your data during ingress, flagging potential issues, and suggesting optimizations to improve matching and model performance. 

### At Faraday, you benefit from both types of data experts

We don’t offer both Forward-Deployed Data Science and Forward-Deployed Data Engineering just because it sounds nice (although doesn’t it?)—we do it because it’s the fastest way to get actionable insights into our clients’ hands.

Making data engineering a client-facing function is a huge step that moves the process forward, ensuring that costly holdups and mistakes don’t happen before your predictive models even have a chance to work and that they keep working at peak efficiency after deployment. 

Here’s what that means for you:

🤔 **No more guessing if your data is good enough.** FDEs provide hands-on support before and during ingestion, catching issues early so they don’t slow you down later. After deployment they’re the ones making sure your models stay healthy.

⏳ **Faster time to value.** Instead of back-and-forth troubleshooting that delays model deployment, our FDEs work with you upfront to get data right the first time.

📊 **Stronger, more accurate predictions.** The best AI models in the world can’t fix bad data. By optimizing your data at the start, we ensure your models perform at their peak.

💰 **Fewer costly mistakes.** If your data isn’t structured correctly, you could end up with mismatched records, poor model performance, or wasted budget. FDEs help prevent that before it happens.

🤝 **A seamless experience.** Whether you’re working with an FDS to interpret predictions or an FDE to streamline integrations, you have direct support from an expert who understands your business.

In short, data science helps you make smarter decisions, and data engineering ensures your data is ready to support them. While forward-deploying both roles isn’t yet standard practice in the industry, we believe transparency, explainability, and speed to deployment aren’t just nice to have—they’re essential. And we’re not in the habit of waiting for the industry to catch up.

Our clients see the impact firsthand. Numerous businesses have leveraged our forward-deployed approach to streamline their data, improve model performance, and drive real results. You can explore a few of their [success stories here](https://faraday.ai/stories).

If you’re already working with Faraday, you know what this means for your business and bottom line. And if you’re not—shouldn’t you be?

Let’s chat about how we can help you put your data to work.

Faraday’s forward-deployed data scientists and engineers work directly with clients to ensure clean, structured data and precise predictive models—maximizing accuracy, efficiency, and business impact.

Data science + data engineering: How Faraday’s forward-deployed team fulfills all your data needs

Before committing to any predictive modeling platform, you need to know if their services actually work for you. That’s where a [validation test](https://faraday.ai/blog/cross-validation-technical-blog) comes in. Here at Faraday, a validation test (such as a holdout or cross validation) lets you see how well our AI models can predict real outcomes—before you even sign up. By using your historical data, we can prove the accuracy of our models without ever seeing your future results. Essentially, if the model is predictive, that means we're a good fit for helping you decide who to send direct mail to, how to accept and organize leads with lead scoring, and more. It’s the best way to validate AI-driven predictions—before you invest, and without any risk.

## How does it work?

A holdout test works by splitting your historical data into two sets: **one we see** (pulled from your recent conversion data, this is known as the test set) and **one we don’t** (pulled from the **most** recent conversion data, this is known as the holdout). First, we use only the training set to build a predictive model. Once it is built, we generate predictions and compare them against the conversions that were ‘held out.’ If our model accurately predicts those hidden outcomes, you can be confident in its ability to drive real results.

Cross-validation works similarly, but instead of holding out a single set for testing, it splits the data multiple times into different training and validation sets. Each subset takes a turn as the validation set while the rest are used for training, ensuring a more comprehensive assessment of model performance.

If you’d like a more detailed description of how this process works, take a look at this [blog](https://faraday.ai/blog/holdout-testing-with-faraday) from our COO, Robin Spencer, or for more detailed setup instructions, see this [blog](https://faraday.ai/blog/holdout-testing-evaluate-prediction-accuracy) from our CEO, Andy Rossmeissl.

Validation testing gives you a real-world view of the model’s accuracy, providing the reassurance that our predictions will stand the test of time—by making sure the AI isn't just memorizing past data, but identifying trends that matter going forward. But for the test to work, we need the right data—uploaded securely. Here’s what you need to know to get results as quickly as possible.

## What data do you need?

To set up a validation test, you must provide **names and dates** for both **leads and conversions**.

### Leads

We need to know when someone became a lead. Use the [Leads template](https://faraday.ai/data_solicitation/leads_template.xlsx) to provide this information, making sure to include the date for each lead. A leads file should ideally include the following fields:

* **First name**  
* **Last name**  
* **Address** (Street, City, State, Zip)  
* **Optional:** Email, Email Hash, Phone  
* **Date** (e.g., the date a lead was added to your system or when they received a mailer)

These personally identifiable details (PII) allow us to match leads to our demographic dataset. Without PII, we won’t be able to validate predictions, and the validation test cannot be run.

🚧 **Note on date formatting:** You can format the date in any standard way (e.g., YYYYMMDD, MM/DD/YYYY) and our system will automatically handle it.

### Conversion event data (transactions)

We also need the dates of individual transactions—along with optional details like product type and amount. Use the [Orders/Purchases/Transactions template](https://faraday.ai/data_solicitation/orders_purchases_transactions_template.xlsx) to submit this data. The conversion file should have the same structure as the leads file (first name, last name, address, etc.), but instead of lead dates, it should contain conversion dates.

### Conversions (customer-level data)

Customer-level conversion data (e.g., just the first or last transaction date) **is not ideal** because we need each individual transaction date. Although you can provide customer-level data using the [Customers template](https://faraday.ai/data_solicitation/customers_template.xlsx), we typically do not use it for a validation test.

## Uploading your data securely

To keep your data safe, always upload it through the **Faraday dashboard** in the Datasets section. You can also connect your platform to your existing data warehouse, but as this integration is more involved, this step would likely come after your successful validation test has been completed and you’ve decided to sign up with Faraday\!

🚧 **Do not email us any data.** The Faraday dashboard is the only secure place to upload files.

### Steps to upload:

1. Go to [app.faraday.ai](https://app.faraday.ai) and sign in.  

[![Sign in page](/images/blog/are-you-ready-holdout/sign-up.png)](/images/blog/are-you-ready-holdout/sign-up.png)

2. If you see a test mode warning, disable it before uploading your data.  

[![Make sure to leave test mode](/images/blog/are-you-ready-holdout/leave-test-mode.png)](/images/blog/are-you-ready-holdout/leave-test-mode.png)

3. Navigate to the **Datasets** section. 

[![Navigate to the Datasets section](/images/blog/are-you-ready-holdout/datasets.png)](/images/blog/are-you-ready-holdout/datasets.png)

4. Click **New Dataset** and select **CSV**. 

[![Select new dataset](/images/blog/are-you-ready-holdout/new-dataset.png)](/images/blog/are-you-ready-holdout/new-dataset.png)

5. Upload your **leads.csv** file and your **conversions.csv** file.

[![Choose CSV upload](/images/blog/are-you-ready-holdout/upload-csv.png)](/images/blog/are-you-ready-holdout/upload-csv.png)

## Want a little more help?

If you're still unsure how to set up your validation test, we've got you covered. [Watch this video walkthrough](https://www.loom.com/share/ab3750fdb50d47c2b6a0fc05e96e10dd?sid=aefe17c6-ba5f-458f-986f-73db3cf43877) with Faraday CTO Seamus to see the process step by step.

And if you still have questions, reach out to us at **support@faraday.ai**—we're happy to help\!

And that’s it\! Once uploaded, we’ll take care of the rest. Ready to get started? Upload your data today and see how Faraday’s AI models can drive real results for your business.

Validate Faraday’s predictive accuracy risk-free with a holdout test—just upload your data, and we’ll prove our models work before you commit.

Are you ready for your pre-purchase holdout validation?

When you’re ready to use Faraday to build [AI models that deliver custom predictive datapoints](https://faraday.ai/features/customer-behavior-predictions), the first step is getting your data into our system. There are two primary methods to make that happen—using a data warehouse or uploading individual `.csv` files. Your best option depends on your team’s setup and resources. This guide covers how to ingest your data and best practices to make it usable for modeling as quickly as possible.

## Part 1: Locating your data

### Data warehouse vs. file upload

**Data warehouse:**  
If your data resides in a warehouse (e.g., [Snowflake](https://www.snowflake.com/en/) or [BigQuery](https://cloud.google.com/bigquery?hl=en)), integration is straightforward. Your team can provision a small, dedicated space within the data warehouse for Faraday, providing secure, efficient, and timely transfers without the hassle of repeated manual uploads. Our [connections](https://faraday.ai/docs/how-to/connections) console provides the necessary credential details for integration.

**File uploads:**  
If your data is managed as individual files, you can upload them directly via our datasets console. Although this method is simple, it is time-consuming to keep data fresh over time because of the level of effort required each time.
- `note:` Faraday is looking for `.csv` (comma-separated values) files - they can be exported from databases, various business platforms and spreadsheets alike.

  **Automation considerations:**  
  For brands using cloud storage (e.g., [Amazon S3](https://aws.amazon.com/s3/) or [Google Cloud Storage](https://cloud.google.com/storage?hl=en)), setting up automated data transfers is simple and efficient. These services can be connected to Faraday with minimal effort and can be nearly as effective as connecting directly to a data warehouse.

  An SFTP transfer option is equally suitable, but may require slightly more knowledge and access to resources within your organization.

---

## `.csv` file formatting requirements

To ensure smooth processing, follow these guidelines when preparing your CSV files:

- **Format:**  
  - ***Files:*** Most of these files are comma-separated and UTF-8 encoded, ideally with a header row listing the column names. If headers are missing, you can add them in the dataset configuration's 'Advanced options'.
  - ***Data warehouse*:** Sticking as closely to simple and common types of fields such as decimals, integers, boolean (true/false) and text (string/varchar) are most effective. Tables or views are both acceptable.

- **Size:**  
  - ***Files:*** Individual files can be up to 5GB in size, and generally should be fewer than 50 columns. Consider splitting larger files into smaller parts and-or removing any fields not related to *behavior*, *time*, *quality* or *identity*.
  - ***Data warehouse*:** Generally, there is no size limit for data warehouse tables, but large tables should be handled with care because the larger the dataset, the longer the processing time.

- **Structure:**  
  - ***Files:*** If providing updates to data, ensure consistent structure across all files in a dataset (cloud bucket/SFTP: "*prefix*") - column names, orders and the data types they are set to (if applicable).
  - ***Data warehouse*:** If using a table, ensure it remains up to date through a regular refresh process. Flattened tables (not nested) are preferred in most cases. Plan on establishing a set of fields to include and avoid changing them over time (such as adding or removing fields).

- **Delimiter consistency:**  
  - ***Files:*** Prefer commas as standard delimiters, but this setting is customizable at the dataset level (tabs, semicolons). If necessary, ensure all columns are quoted.

- **Additional flexibility:**
  - ***Files:*** We accept zip-compressed files such as .gz or .bz2 if you are batching and saving on storage. Further information can be found in our [API docs](/docs/reference/createupload).

---

## Part 2: Data essentials for accurate predictions

Once your data is uploaded, it must include the necessary elements for precise predictions. Faraday supports various data types, but certain formats yield better results.

### Types of data

- **Event stream data:**  
  This type of data is preferred for its detailed insights into customer behavior, where each row represents an event (e.g., account creation or purchase) - with a *timestamp*. This granularity is key for understanding customer journeys. (See our [Datasets documentation](https://faraday.ai/docs/abstractions/datasets) for more details.)
    
    Flavors:
    - email engagement data
    - website engagement data
    - app usage data
    - order data
    - sales funnel data
    - customer journey mapping
    - direct mail response tracking

- **Customer-level (summary) data:**  
  This type of data captures one row per customer with summary information such as: purchase history metrics, customer tier, original source or lifetime value. With a joining key (e.g., customer_id), it works best when combined with event stream data.
  - `note:` Summary type data may hold valuable insights on your customers' [first-party traits](https://faraday.ai/blog/first-party-features). It *can* also include event timestamps as additional columns when relevant and is frequently seen in CRM exports.

### Essential fields

To ensure accurate modeling, include these key fields:

- **Personally identifiable information (PII):**  
  First name, last name, address (street, city, state, postal code), email, and phone number. At least name + address or email are required at a minimum. More PII enables enhanced data enrichment through the [Faraday Identity Graph](/docs/abstractions/traits), and can unlock a world of value.

- **Event timestamps:**  
  Accurate timestamps are critical for tracking when events occur. "YYYY-MM-DD" is the preferred format, but other common selections are available via the app and API. 
  - `note:` Ensure all time-based fields follow the *same* format, whichever option you choose.

  - **Event properties:**
    Other values which are captured at the time of the event that may exist in your records. (Cost of goods, method of payment, unique products or services, result of event)
    
    In these cases, the additional data should answer the question: `How much value was generated for your business by this event?`

- **Unique identifiers:**  
  Each set of data should have a *primary key* (e.g., an order number) and a *reference key* to link said event to an individual. These datapoints deliver maximum flexibility to both you and Faraday through the process of updates.

### Identity resolution and matching

After your first-party data is connected, Faraday’s identity resolution software links individuals to additional third-party attributes with over 1,500 available datapoints and counting. Providing as much quality PII as possible improves matching accuracy and minimizes reliance on aggregated datapoints.

At a minimum, you must provide an email or a combination of first and last name with street address and postal code.

---

## Note on data hygiene

Organizing and labeling your data correctly is vital — poor quality input yields poor quality output every time. Clean, well-curated data leads to smoother processing and more accurate insights.

**Best practices include:**

- Maintaining consistent formatting: no type mixing across fields, names and addresses follow a similar structure, categories have fixed sets of standard values, and avoiding filler values for missingness (e.g., "None", "N/A")
- Verifying that phone numbers are a full 10 digits, accurate emails are provided, and names come with *full* physical addresses
- Deduplication
- Ensuring you do not have large gaps in field coverage or in time between groups of events
- Not providing more than you intend to use (aside from future use cases)

Investing time in even minimal data hygiene upfront saves time and reduces the likelihood of missing a problem that was lying in wait.

---

## What’s next?

Getting your data into Faraday is just the beginning. By following these best practices, your data will be structured and ready for predictive modeling—whether you’re predicting lead conversion or conducting market opportunity analyses. If you have trouble checking all the boxes above, don't worry - Faraday will help find a solution for your needs.

Let us know if you have additional questions as you prepare your data. You can connect with support at support@faraday.ai - happy modeling!

To get started with Faraday, proper data ingestion is essential. This article offers a technical overview of the processes and best practices for preparing and ingesting your data into Faraday's system, ensuring fast and accurate predictions.

Preparing your data for seamless ingestion - a technical guide

When brands partner with [Faraday](http://faraday.ai), the goal is clear: deploy predictive models as quickly and effectively as possible to [drive business ROI](https://faraday.ai/stories/financial_services_client). But before any predictions can happen, the foundation has to be set, and that means getting the right data into the platform.

For marketers without a dedicated data scientist on their team, this step can feel like a hurdle. What data does Faraday need? In what format? How can you ensure a smooth process that avoids issues with matching and model performance?

These are big questions, and answering them correctly could be the difference between actionable predictions in a week or two versus a much slower and less efficient process.

## Start with the big picture

Before diving into spreadsheets and data exports, take a step back. The first question we ask any new client is: ***What problem are you trying to solve?*** Your goal determines the data you’ll need. For example:

* Are you looking to improve conversion rates? You’ll need historical data on past conversions.  
* Want to identify high-value customers? Transactional data will be key.  
* Need to optimize outreach? Engagement metrics and response data should be included.

Knowing your objective up front ensures that the data you provide aligns with your business needs. 

So what data do we need? The best answer is probably to give us access to *everything—*or at least, as much as is feasible for your company. While some data points are necessary to make the right predictions, all of them can help us to build a better and more holistic understanding of your existing customer base and the best potential customers you could reach within the US population.

![Meme about how much we love data](/images/blog/welovedata-meme.png) 

## So more IS better, but formatting matters

*So we’ll just give Faraday all our data as is and let them figure it out?*

**So, the answer is both yes and no.** While more data is generally better, it’s not just about volume—it’s about data quality. Brands without an established system to ensure [data hygiene](https://www.indeed.com/career-advice/career-development/data-hygiene) may encounter issues like malformed addresses (e.g., missing state or zip code), missing customer details (e.g. no address at all), or improperly formatted columns (e.g., first and last names in one field). While Faraday’s team can spot these issues early on, working with clean, well-structured data from the start will drastically speed up the process, leading to faster, more accurate predictive models.

If you're facing challenges with data hygiene, don’t hesitate to reach out. In addition to our standard data cleanup, we offer more robust professional services upon request, with a variety of packages designed to help you wrangle your datasets. Even if your data isn’t perfect, we’ll help you identify and fix any issues, setting you up for quicker results.

## So what data should I include?

At a minimum, the following fields should be included to ensure proper matching and predictive accuracy:

* **Customer identifiers:** Name, email, phone, and address (as complete as possible)  
* **Leads:** Name and the date that someone became a lead  
* **Conversions (transaction-level preferred):** Dates of individual transactions, along with product details and amounts (if applicable)  
* **Engagement metrics:** Website visits, email opens, and interactions

#### Customer-Level vs. Transaction-Level Data

While you can provide customer-level conversion data (e.g., first or last transaction date), we usually do not need it and cannot use it for a holdout test. Instead, transaction-level data—with a date for each transaction—is preferred, as it enables more accurate predictive modeling.

And if you're unsure where to start, focus on conversion rates. Defining how your brand measures conversion—whether through completed purchases or booked appointments—helps us create more accurate predictive models and simplifies the entire process.

## Where does your data live?

Once you’ve identified the right data, the next step is understanding where it lives. Is it spread across multiple systems like a CRM, e-commerce platform, and customer support database? Do you have easy access to it and permissions to share it externally, or will you need internal approvals? For enterprise brands, pulling data may require specific requests within your organization. Understanding these logistics early on can prevent delays.

In the best case, your data lives in an organized data warehouse like [Snowflake](https://www.snowflake.com/en/), [Bigquery](https://cloud.google.com/bigquery?hl=en), or [Redshift](https://aws.amazon.com/redshift/). This makes ingress easy, as Faraday is a fully composable platform that can integrate with existing data warehouses, functionally sitting on top of them as a decisioning layer, and then automatically routing predictions to channels like [meta ads](https://aws.amazon.com/redshift/), [Shopify](https://www.shopify.com/), or many others. To learn more about our available integrations, take a look at [our integrations landing page](https://faraday.ai/integrations). 

![Faraday adds a decisioning layer on top of your data warehouse](/images/blog/data-warehouse-first-strategy.png) 

If your data isn’t stored in a warehouse, you can upload individual CSV files through our dashboard, but if you do make sure to pay attention to your data hygiene\!

## Data hygiene, what’s that?

Data hygiene is the practice of keeping your data clean, structured, and accurate to ensure the best possible outcomes when using predictive modeling and AI tools like Faraday. Poorly formatted, incomplete, or inconsistent data can lead to mismatches, errors, and weaker insights. Clean data doesn’t just make processing smoother—it directly impacts the quality of predictions and business decisions, **and this is the single most important thing to pay attention to when importing your data into Faraday\!**

The first step to good data hygiene is ensuring consistency in formatting. Datapoints such as names, addresses, and dates should follow a structured format, ideally with first and last names in separate columns and dates consistently standardized in a format like YYYY-MM-DD (I[SO 8601](https://en.wikipedia.org/wiki/ISO_8601)). Additionally, emails and phone numbers should be complete (no 7 digit phone numbers or filler values like “noemail@none.com”) to enable seamless enrichment with third-party attributes in the Faraday Identity Graph. Clean PII helps ensure the best possible enrichment rates for model performance.  
Properly labeled and complete fields prevent confusion and make it easier to match records correctly whereas missing or incomplete records can significantly weaken predictive accuracy. 

Despite the fact that data gaps can be somewhat mitigated by aggregated scoring, better results are always seen when providing a sound dataset from the get-go. If certain data points are unavailable, we can still make predictions, but providing as much information as possible—especially time-bound event data—will dramatically improve our ability to analyze your data quickly and accurately.

So in conclusion, taking the time to check, clean and structure your data before ingesting it into Faraday will save time, prevent headaches, and lead to stronger, more reliable predictions. If you have questions about how to prepare your data, don’t hesitate to reach out to your Strategic Account Manager or Faraday support. 

And speaking of our supportive team…

## Leveraging forward-deployed data science (FDS) for a faster start

Getting your data into Faraday doesn’t have to be a challenge—we offer multiple options to help you onboard seamlessly. Enterprise clients can access Forward-Deployed Data Science (FDS) for hands-on guidance, while all clients benefit from professional services, dedicated support, and self-serve resources to streamline data ingestion.

Our team helps eliminate bottlenecks by structuring and preparing data, optimizing predictive models, and providing strategic guidance. Whether you need help standardizing fields, refining lead suppression strategies, or aligning predictions with business goals, we ensure your models are built on a strong foundation.

For enterprise clients, FDS embeds our experts directly into your workflow, continuously refining models and adapting strategies to market shifts. No matter your level of support, we’re here to help you get started quickly and drive measurable impact from day one.

If you’d like to learn more about this available resource, take a look at our [recent blog post](https://faraday.ai/blog/FDS-changes-the-game) detailing our decision to forward deploy our data science team.

## Final checklist: Setting your team up for success

To make the handoff between your business and technical teams easier, follow this checklist: 

✅ Clearly define your business objective

✅ Identify where relevant data lives 

✅ Ensure core customer identifiers are included 

✅ Provide date fields for key events 

✅ Clarify how conversion rates are measured 

✅ Work with FDS to streamline the process

By following these steps, you’ll avoid common pitfalls, speed up deployment, and get to actionable predictions faster—maximizing the impact of AI-driven insights on your business.

## In conclusion

Getting your data into Faraday doesn’t have to be complex. With the right preparation and support, you can streamline the process, move quickly from data ingestion to predictive modeling, and unlock actionable insights faster.

If you’re ready to learn more or have any questions about preparing your data for Faraday, don't hesitate to reach out. Or if you're feeling prepared to go at it alone, get started for free today, and [sign up here](https://login.faraday.ai/u/signup?state=hKFo2SBTU2FQa0dIbmg4QzBfV0kwTDczbXdiVHc5Wm1mOVB5TqFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIHk0S3JUSGlUMExSSFVPejduNDdBQnpFeHBjZVVjN00xo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)\!

To deploy predictive models effectively with Faraday, clients need to start with clean, well-structured data that aligns with their business objectives, ensuring smooth ingestion and accurate predictions. This blog serves as a high-level introduction to this process.

Preparing your data for seamless ingestion - a marketer's overview

A lot of our blogs end with a similar CTA: “Want to get started for free? [Sign up today](https://login.faraday.ai/u/signup?state=hKFo2SBTU2FQa0dIbmg4QzBfV0kwTDczbXdiVHc5Wm1mOVB5TqFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIHk0S3JUSGlUMExSSFVPejduNDdBQnpFeHBjZVVjN00xo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)\!” 

And indeed, if you're looking to get started with Faraday, the first step is to create an account. 

But how exactly does that process work? Whether you're new to predictive AI or just eager to see what Faraday has to offer, this guide will walk you through the steps so you can quickly gain access to our powerful platform.

### Step 1: Visit faraday.ai

To begin, navigate to [Faraday’s website](https://faraday.ai). This is the official platform where you can sign up and explore our predictive AI solutions.

### Step 2: Click on "Sign up for free button"

Once you're on the homepage, look for the purple, "[sign up for free button](https://login.faraday.ai/u/signup?state=hKFo2SBTU2FQa0dIbmg4QzBfV0kwTDczbXdiVHc5Wm1mOVB5TqFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIHk0S3JUSGlUMExSSFVPejduNDdBQnpFeHBjZVVjN00xo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)" button in the top-right corner. Click on it to begin the registration process.

[![Sign up button location](/images/blog/signingup-with-fdy-blog/sign-up-button.png)](/images/blog/signingup-with-fdy-blog/sign-up-button.png)


### Step 3: Enter your information

You'll be prompted to enter some basic details, including:

* Your full name  
* Work email address  
* Company name  
* Password (ensure it meets security requirements, we do love our security)

[![Dashboard view](/images/blog/signingup-with-fdy-blog/integrations.png)](/images/blog/signingup-with-fdy-blog/integrations.png)

### Step 4: Verify your email

After submitting your details, you'll receive a verification email. Open your inbox and click the confirmation link to activate your account. If you don’t see the email, check your spam folder or request another verification email.

### Step 5: Log in & get started

Once your email is verified, return to [Faraday’s login page](https://faraday.ai/login), enter your credentials, and start exploring the platform. 

At this point you’ll find yourself on our Dashboard (although all this information can also be accessed through our API).

[![Integrations landing page](/images/blog/signingup-with-fdy-blog/dashboard.png)](/images/blog/signingup-with-fdy-blog/dashboard.png)

### Step 6: Add your data

Now that you're in the Faraday app, the next step is to add your customer data. A great starting point is uploading transaction data, which allows you to generate customer-focused predictions. The easiest way to do this is by using a CSV file exported from your data infrastructure. Ideally, this file would include first name, last name, address, and phone number to facilitate matching but give us what you have and we’ll work from there.

Simply upload your file, map the relevant fields, and let Faraday begin building your transaction event stream in the background.
Most users eventually connect directly to their data warehouses or cloud storage for automated data ingestion—learn more about that in our [documentation](https://faraday.ai/docs/how-to/streams/transaction). Once your data is in, you're ready to start making predictions and uncovering valuable insights\!

### Step 7: Explore use cases and integrations

Now that you have access to Faraday, take some time to explore how you can apply our powerful predictive models to your business case. Browse our pre-built [templates](https://faraday.ai/templates) to see how Faraday can help you make smarter decisions with AI-driven insights.

Additionally, check out our wide range of [integrations](https://faraday.ai/integrations) that seamlessly connect with your existing tech stack. Whether you want to add a decisioning layer to your data warehouse (like [Snowflake](https://faraday.ai/integrations/predictions-with-snowflake)) or send predictions directly to your sales and advertising platforms like [Shopify](https://faraday.ai/integrations/predictions-with-shopify) or [Google Ads](https://faraday.ai/integrations/predictions-with-google-ads), Faraday makes it easy to integrate predictive AI into your workflow.

## More things to do

### Meet the Faraday team

But our technology isn’t our only core differentiator, another one is our people. Our data science team is forward-deployed, ensuring that you get hands-on expertise to tailor AI solutions to your needs. And our sales team? They’re truly the best in the business. Exploring the platform on your own is great, but if you really want the lowdown on Faraday, reach out to talk to Dave, Nate, Charles, or any of our other incredible team members who are ready to help.

### Learn about the Faraday Identity Graph

At the heart of our predictive AI is the [Faraday Identity Graph](https://faraday.ai/docs/abstractions/traits)—a robust dataset containing over [1,500 rich attributes and insights on nearly every U.S. consumer and household](https://faraday.ai/features/faraday-identity-graph). This consented third-party dataset includes the demographic, behavioral, and geographic information you need to push your models to the next level. Whether you're looking to enhance your predictions or simply enrich your data with appends, the Faraday Identity Graph is here to help.

### Troubleshooting & FAQs

**Q: I didn’t receive a verification email. What should I do?** A: Check your spam folder or click on "Resend Verification Email" from the sign-up page.

**Q: Can I use a personal email to sign up?** A: Faraday is designed for businesses, so we recommend using a work email for a smoother experience.

**Q: Where can I get help if I run into issues?** A: Visit our [support page](https://faraday.ai/contact) or contact our team at support@faraday.ai.

### Get started today\!

Creating a Faraday account is quick and easy. Follow these steps to get started, or if you'd rather talk to someone, say hi to [Nate](https://www.linkedin.com/in/nathan-novotny-3484227a/)!

This guide walks you through the simple steps to sign up for Faraday, add your data, explore AI-driven insights, and integrate predictive models into your workflow.

Getting started with Faraday

Data science + data engineering: How Faraday’s forward-deployed team fulfills all your data needs

Are you ready for your pre-purchase holdout validation?

Preparing your data for seamless ingestion - a technical guide

Preparing your data for seamless ingestion - a marketer's overview

How to create a Faraday account: A step-by-step guide