Getting your Data Ready for AI

AI (Artificial Intelligence), and especially GenAI (Generative Artificial Intelligence), is everywhere nowadays. In 2024, McKinsey reported in a global AI survey that the respondents’ adoption of AI had increased to 72% compared to about 55% in the previous year. The usage of GenAI has almost doubled to nearly 65%—a trend that is still expected to grow.

So, it’s only natural to be curious about how to leverage AI if you and your company have not done so yet. Or maybe you have, but the results were disappointing? If so, this article might be for you, even though (or maybe especially because) we are not about to dive into the hyped capabilities of AI systems. Instead, we will highlight an often-overlooked aspect of successfully implementing an AI solution: Data. Having the correct data, i.e., data that not only holds the key to your specific business needs but is accurate, complete, reliable, and without biases, is no easy feat. However, it is a task worth the effort and necessary to leverage AI’s power.

What Could Possibly Go Wrong?

As usual, when it comes to data, the phrase “garbage in, garbage out” perfectly reflects the risk of feeding poor-quality data into AI systems. The algorithms and models you build rely entirely on the data you feed them. If your data is messy, biased, or incomplete, the results from your AI models will reflect those flaws, and you will end up with inaccurate predictions or decisions.

Imagine a bank using an AI system in its loan approval process. If the model was trained on, e.g., outdated income data or data based on highly biased human decisions, you cannot expect accurate predictions. Cleaning up the data solely for this specific use case would be necessary. However, this might also lead to a waste of resources if data is not shared ideally among different bank departments; because the same work might have to be done by another department that wants to use the data for different purposes. Moreover, imagine that this flawed model makes it into production. When inconsistent or biased predictions accumulate, it will not only directly hurt the bank’s day-to-day business but also wholly undermine the trust and confidence in AI solutions.

By going the extra mile to get your data AI-ready, you pave the way for AI solutions to make accurate predictions and decisions. Clean, well-structured data reduces the time and effort needed for preprocessing, allowing teams to focus on innovation rather than troubleshooting.

What Does Actually Go Wrong? (AKA Data Issues)

Data stands at the core of every AI system; it greatly impacts the performance throughout its lifecycle. Starting from the data used for training or fine-tuning a pre-trained model through the data that inferences are performed on to testing or monitoring data. We already mentioned that inaccurate, messy data is a problem. In addition to data quality issues, more aspects must be considered, ranging from scalability problems, over data segmentation, and data silos to legal and regulatory requirements. The following section will go into more detail on all these topics.

Data Quality

AI is only as good as the data it’s based on. Poor data quality can lead to inaccurate results, undermining the credibility and effectiveness of AI solutions. Some of the most common data quality issues are:

Incomplete Data: Missing values or fields in datasets can lead to models that fail to generalize. For example, a healthcare AI trained on incomplete patient data might overlook critical diagnoses, resulting in non-ideal treatment suggestions.

Inaccurate Data: Errors in data entry or outdated information can mislead AI models. For instance, a logistics AI relying on incorrect inventory data might make wrong decisions for resource management, leading to delays or lost revenue.

Duplicates: Redundant records increase the dataset size without adding value. On the contrary, they even decrease the value by causing potentially skewed model outputs and unnecessarily high computational costs. A special challenge addressed in our series on data reconciliation (see below) is duplicate data that is hard to detect due to differences in the data schema or value expression.

Outliers: Extreme values can skew model performance, particularly in regression or clustering algorithms. Detecting and handling outliers is crucial for robust AI outcomes.

Bias: Data bias can, for example, be introduced through historical inequalities, sampling errors, or subjective judgments. They can lead to factually wrong or unfair and discriminatory AI outcomes. For example, biased hiring data can contribute to maintaining workplace inequalities.

Segmentation and Silos

While effective data segmentation is critical for AI solutions that deliver meaningful insights, poor segmentation and data silos can limit the utilization of datasets.

Segmenting data into meaningful groups (e.g., by customer demographics or geographic regions) requires domain expertise and clear objectives. Poor segmentation can obscure patterns, leading to irrelevant or inaccurate model outputs. For example, in fraud detection, if transactions are only segmented into ‘online’ and ‘offline’ while other relevant features such as the transaction amount or region are ignored, fraudulent and legitimate transactions cannot sufficiently be separated.

Often, data is scattered across departments and stored in incompatible systems. This fragmentation is known as data silos and hinders the integration of datasets needed for AI. For instance, marketing data stored separately from sales data may result in incomplete customer behavior analysis.

Breaking down silos is a step towards creating unified, comprehensible datasets, which are a cornerstone of successful AI implementations. It requires fostering team collaboration, implementing centralized data platforms, and ensuring consistent data governance.

Regulations and Legal Considerations

With the rise of AI, regulatory and legal scrutiny around data usage has intensified. Compliance with laws and regulations is essential to avoid legal liabilities and reputational damage.

Regulations such as the GDPR (General Data Protection Regulation) in the EU and CCPA (California Consumer Privacy Act) in the US require organizations to collect, store, and process personal data responsibly. For AI projects, this means ensuring data is anonymized, securely stored, and used within the limits of consent.

More recently, the EU AI Act was designed to ensure a safe and ethical use of AI. It classifies AI systems based on risk levels and imposes specific obligations for high-risk applications. This includes requirements for transparency, robustness, and proper data management. Organizations must ensure that AI systems operate within these standards to avoid penalties and maintain compliance. Please have a look at our article on the EU AI Act for more information.

Data Storage and Processing

AI models generally thrive on large datasets. However, the sheer volume of data required can lead to storage, processing, and analysis challenges. Thus, handling massive datasets requires robust infrastructure, such as distributed storage systems or cloud platforms. Without these, organizations may struggle to scale their AI initiatives.

Moreover, many AI applications require real-time or near-real-time data. Setting up pipelines to ingest, process, and analyze live data streams is technically demanding but essential for applications like fraud detection or recommendation systems.

There are also other challenges in processing data for AI applications. Often, unstructured data has to be pre-processed, e.g., annotated with metadata or labels, to make it usable for AI. Once an AI model is successfully deployed, its performance must be monitored. Otherwise, changes in the data patterns, i.e., so-called concept drifts, might go unnoticed, and the model increasingly fails to deal with a dynamic, constantly changing world.

What Can You Do? (AKA Solutions)

To unlock the full potential of AI, data must be made accessible, reliable, and ready for analysis. Therefore, one must overcome challenges like poor data quality, fragmented silos, and regulatory constraints. Data governance plays a big part in achieving this by providing the framework for managing data effectively.

One of the primary obstacles to making data accessible lies in overly segmented data silos. Choosing the appropriate data architecture that can handle the expected amount of data and helps centralize aspects of your data is a big part of the solution. Please have a look at our article on modern data architectures to learn more.

Effective master data management builds a unified, consistent view of critical data across systems by creating a single master record for functional business entities (e.g., customers, products, etc.). It reconciles duplicates and resolves discrepancies in key information shared across the company.

Creating a data catalog is a decisive step toward optimizing data assets and collaborating on data resources in your company. It requires you to carefully assess all the data used and enrich it with meaningful metadata. A data catalog makes data easily discoverable and more transparent and reduces duplication. It is, therefore, another part of the solution to eliminate data silos.

Data quality management includes cleansing, validation, and monitoring of the data. It should be supported by automated tools that can, e.g., alert about potential errors and inconsistencies in the data or even automatically resolve them. Data quality management is essential not only as a solution for all potential data quality issues themselves but also provides benefits through monitoring and alerts, which can prevent unnoticed concept drift during the life of an AI application.

Following data security and privacy standards by using anonymization, pseudonymization, and encryption techniques is essential for compliance with current law while protecting sensitive information. It’s important to think about the potential usage of data before you even start collecting it to ensure you get all the necessary consent and store the appropriate information. Otherwise, you might end up with a plan for an AI solution that is perfect on paper but will never see the light of day because using the necessary data is prohibited by law.

While data management systems and other software that support data governance tasks are available, unfortunately, they will not magically solve all potential problems you may have with your data. Nevertheless, they can be helpful tools for creating a clean data stake. Whether a ready-made product serves your needs, you need a bespoke solution, or anything in between is a highly individual decision. We at CID can help you dive into your current data usage and create a plan to bring your data into the age of AI.

What Now? (AKA Conclusion)

Getting your data ready for AI can be seen as a tedious task that shows no immediate, inherent value. However, this view is rather shortsighted and cripples all data-driven initiatives, whether AI or BI solutions, before they can even start. It requires attention to detail, compliance with best practices, and continuous improvements. By addressing quality issues, data accessibility, and ensuring regulatory compliance, organizations can unlock the full potential of AI to transform their business intelligence.

Ready to start? Contact us to begin by evaluating your current data practices and building a roadmap to AI readiness—it’s an investment that pays off.

Author © 2025: Lilli Kaufhold – www.linkedin.com/in/lilli-kaufhold/