How to ensure your data is clean, consistent, and AI-ready

This blog provides guidelines and best practices for preparing data to be effectively utilized in artificial intelligence applications.

By Lisa Zavetz

What do online shoppers, bank customers, and honeybees have in common? 

They all are served thanks to clean, consistent data that is fed into artificial intelligence (AI) to get reliable recommendations.

Some companies have created digital assistants with AI, to help customers seek answers to their questions quickly. Others are using it to prevent fraud, keeping customer information safe.

There’s even a company using artificial intelligence to save the honeybees! (The team established an automated way to identify harmful mites, leading to more efficient recognition of infestations and thus treatments to more quickly help the hives.)

In the current digital landscape, hundreds of innovative AI and machine learning (ML) tools are emerging daily. Each one offers unparalleled capabilities to elevate efficiency, drive productivity, and propel your business towards new frontiers. 

However, the key to unlocking the full potential of AI and ML is hinged on a single, critical factor: the readiness of your data. 

Without clean, consistent, and AI-ready data, your organization faces the risk of failing to capitalize on these cutting-edge tools. This not only inhibits growth but also paves the way for more data-ready competitors to seize market share. The stakes are high in the AI-powered future; it’s not just about leading the market but about being part of it.

In this blog post, we will explore the challenges involved in data preparation for AI applications and discuss how the Segment customer data platform (CDP) can help organizations overcome these hurdles.

The Roadblocks to AI-Ready Data

There are a number of common reasons why your data may not be not AI-ready. Let’s explore them:

Structural errors

If your data contains structural errors, it can’t be properly ingested by AI. Structural errors include typos in your data, incorrect spellings, and inconsistent formatting. For example, say you have a dataset with a column for “age”, but due to an error in data entry some entries in the "age" column are recorded as days and others as years. This structural error in the data can mislead AI models when making predictions or analyzing trends related to age.

Without tools in place, the solution would be to volunteer one of your engineers to spend time organizing the data types of all the columns.

Duplicate data

If you have data that is collected more than once, you’re at risk of owning duplicate data. This information can be collected in one channel, or across multiple ones. 

A common scenario occurs when similar tools – such as Google Analytics and Webtrends Analytics – are used simultaneously, resulting in double-recorded events. You’ll see the same event through both systems, which generates clutter.

Companies need to consider the format of the data, how it is being used, and its quality level.

Data silos

If your data is stored in multiple locations, you are at risk of data silos. Think about who in the organization uses data, and how. Marketing likely relies on a customer relationship management (CRM) system. Analysts rely on the data warehouse, and customer success puts their customer notes in tickets. 

Disjointed data sets can trigger disputes over which data set represents the "truth", making data management an uphill task.

When your data is scattered, it becomes very challenging to keep connected, uniform, and organized. And therefore difficult to pull and feed into your AI application.

Outdated data

Hoarding outdated data can distort your AI model's accuracy. Over time, customer information changes, and if your AI system isn't updated, it could result in irrelevant predictions.

Think about advertising to a pregnant woman. It makes sense to recommend prenatal vitamins to her during pregnancy, but once her child is 5, the ads are irrelevant. If you feed your AI system expired information from years ago, it will result in expired predictions.

Ungoverned data

Companies can end up with non-compliant data when they have inadequate data governance policies and lacking validation processes.

Housing and acting off of non-compliant data (PHI, PII, NPI, etc.) can get a company into hot water. You have to know what you're feeding the model rather than just telling it to consume your entire storage array. If this sensitive customer data is fed into an AI model, it can result in severe privacy breaches and maybe result in a lawsuit.

All of this might seem daunting, but fear not. While poor quality data is ubiquitous, there are steps you can take to get your data AI-ready. This takes proper planning, communication, and collaboration, which we outline in the next section.

Best Practices for Data Preparation

Your data can be cleaned and ready to be ingested by your AI tool with the use of a customer data platform (CDP). The Twilio Segment CDP specifically collects, cleans, and activates your data so businesses can help AI applications deliver more accurate and impactful results.

You can achieve AI-ready data with the tips below:

Ensure high quality data

First, your data has to be high quality– without structural errors or duplications. We’ve written a blog on this, and it begins with auditing your data and establishing consistent naming conventions to effectively enhance the performance and outcomes of AI.

You can block bad data from ever entering your downstream tools if you have a customer data platform. Specifically, Twilio Segment’s CDP has a feature called Tracking Plans. Tracking Plans saves all the time of identifying and removing bad data when it is being used in tools for decision making. 

This lets AI algorithms analyze and extract insights more effectively.

Data enhancement

When you have siloed data, you look at it from only one place. But if you have data coming in from multiple sources, you’ll want to enrich it with the other details you are collecting. 

Data enrichment is important for AI as it enhances the quality, depth, and context of the data used for training AI models. This leads to more accurate predictions, insights, and personalized experiences.

Twilio Segment can enrich customer data with external sources. It offers integrations with various third-party data providers, allowing companies to enhance their customer data by integrating additional information from external sources such as CRM systems, marketing platforms, or data enrichment services. 

This helps enrich and supplement customer profiles with valuable insights and data points, leading to a more comprehensive understanding of customers.

Real-time data

It’s important that the data you feed into AI is in real-time. This can provide up-to-date insights so AI models can make decisions and predictions based on the most current events available. 

As the Segment CDP uses a unique code with websites and apps, it is able to collect and process data in real-time. This data can then be sent to various destinations and used by businesses to understand their users and make data-driven decisions.

Read a case study on how Pomelo was able to increase gross revenue by 15% using real-time data to power real-time recommendations based on the customer’s actions.

Remain compliant

You need to ensure the data you’re feeding into your AI tool is compliant with government and browser regulations so you protect user privacy rights and prevent the misuse of personal data. 

Twilio Segment CDP lets companies collect and store customer data in a structured manner, allowing them to easily identify and remove any unnecessary or redundant data.

By minimizing the data stored, organizations can reduce the risks associated with non-compliance for a better AI experience.


Getting your data AI-ready is a crucial step in unlocking the true potential of artificial intelligence. 

By following best practices in data preparation and leveraging advanced tools like a CDP, organizations can streamline the process and ensure their data is well-prepared for AI applications. 

Segment CDP simplifies data collection, consolidation, cleansing, and real-time processing, addressing common data preparation challenges. With Segment CDP, organizations can accelerate their AI initiatives and gain valuable insights from their data.

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