Why Data Matters in AI Adoption

Posted: June 23, 2025
WBC Digital Solutions
5 min read

Author: Zahra Hassan

AI Adoption has become one of the leading topics in recent days where according to McKinsey, 78% of organizations use AI in at least one business function, and 63% of organizations intend to adopt AI within the next 3 years. From recognizing images to creating one, and facilitating self-driving cars to humanoid robots, it is apparent that AI is booming and has already made its mark on various industries.

But what we don’t see is that behind every successful AI adoption, data plays a very important role. It is the fuel that powers AI systems and their capabilities to achieve transformative results.

The Role of Data in AI Transformation

Think about the very YouTube we are using everyday, and how its algorithm functions in a way that offers suggestions and recommendations based on our individual preferences. 

How does it work? Well, you know that the recommendation engine uses our user data to tailor the content that we may likely watch based on our watch history, liked videos, comments, etc. Without user data, YouTube would have been a long forgotten platform.

Let’s see how data plays a significant role in AI adoption:

Data Types

If you are planning to adopt AI in your organization, it’s important to know what types of data your organization holds, so you can strategically structure and standardize it for correct usage.

Let’s take a look at the types of data available in an organization:

The main takeaway here is that when planning for AI adoption, data preparation is key. Knowing different types of data can help you strategize and standardize the transformation effectively.

Why Data Quality Matters? (Importance of Data Preprocessing)

Like we discussed before, the quality of your data determines the success of your AI adoption. As the saying goes, ‘Garbage In, Garbage Out’, if you feed bad data into your system, you are guaranteed to receive bad outputs.

According to Gartner, companies spend an average of $12.9 million due to poor data quality. Not only does it make an impact on revenue, it also hampers the long-term growth of the business leading to poor decision-making and derailed outcomes. This is why data preprocessing is crucial for good quality data. It refers to the processing of cleaning and transforming raw data into a usable format for analysis and modeling.

The four major steps in data preprocessing will eliminate unnecessary, incomplete, and incorrect data and replace the missing values (data cleaning) by combining them into datasets (data integration), then removing the unnecessary variables (data reduction) and transforming them to model-friendly formats (data transformation).

Now you have a fair idea why data preprocessing is important and how it transforms the raw data into meaningful outputs for AI adoption.

The Power of Data Strategy

Before we discuss the characteristics of good quality data, let’s see how a well-defined data strategy in place can leverage the success of AI adoption in a business organization.

Data strategy is a comprehensive plan that determines what data you are capturing, how and for what purpose. A well defined data strategy will show how to collect, manage, govern, and use data to generate value. It comprises three key elements: 

This shows that having a well defined data strategy can save your money and time as it avoids collecting unnecessary data, redundant efforts, and malpractices. It helps in making effective data-driven decisions by keeping everyone aligned with their business goals. So if you are planning for AI adoption, make sure you have a well defined data strategy in place for a successful transition. Otherwise, even if you have the best AI tools and practices, you will have to face inefficiencies and irregularities in your operations throughout.

Characteristics of Good Data

So far, we have been talking about data types, data preprocessing, data strategy, etc. But how do we ensure that we have good quality data in our hands?

Let’s take a look at the 5 key characteristics of good quality data:

Quality or Quantity?

We all have heard that for AI Adoption, we need large amounts of data to make the system learn and deliver desired outcomes. So you may have a question in mind: does quantity matter more than quality?

The answer is: no, it doesn’t. Quality matters more than quantity. It doesn’t matter how much data you have, if it lacks quality, your system will fail to deliver what you are hoping for. Even if you have small sets of data, if it meets the standard of good quality data, you will have a much desired output that’s reliable and relevant. It’s not wise to feed a system with lots of data without ensuring its quality, as it will have long term negative consequences as discussed before.

Data Governance: Why It’s Important?

It’s essential to have a strong data governance when adopting AI into your business to ensure correct and ethical usage of your data, and to protect your data from security vulnerabilities like unauthorized access and data breach. 

Data governance offers visibility on who accesses the data and how it’s being used by providing processes, structures and policies to build trust, manage risks, and maximize the benefits of AI adoption. 

Overall, it helps to measure AI transformation efforts effectively by staying vigilant and responsive to increasing regulations and constantly evolving digital trends.

Conclusion

If you are preparing your organization for AI adoption, learning is essential, as you need the knowledge and skills to guide your organization through its AI transformation. From reading this article, you have now learnt the pivotal role data plays in AI adoption. 

Understanding where AI can deliver the most value, learning what it takes for a successful AI adoption, and implementing the right course of actions and procedures are some of the initial steps to unlock the full potential of AI in your business.

Looking for a roadmap to adopt AI in your business? Book a free consultation with us to help you design your own plan and successfully transform your operations into an AI-driven business!

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