“AI is only as smart as the data it has been trained on.”
– Sundar Pichai, CEO of Google

Businesses big and small are increasingly adopting artificial intelligence (AI) to streamline their operations and get a competitive advantage. AI-driven customer service chatbots, fraud detection, credit scoring, predictive analytics, and supply chain optimization are just a few of the numerous use cases of artificial intelligence in business.

However, AI is not a silver bullet on its own. The outcomes of your AI initiatives heavily depend on the data you feed into AI systems or, to be more accurate, data quality.

Why is data quality so important for your AI strategy? What are the key characteristics of high-quality data? What can businesses do to enhance data quality?

Read our article to find answers to these and other questions.

Why data quality is the backbone of AI

Let’s face it: no matter how advanced your AI systems are, they can’t compensate for poor data quality. It’s like filling a Ferrari with the cheapest fuel – despite a powerful engine, it won’t run the way it’s meant to. The same happens when you feed inaccurate, outdated, or inconsistent data into AI systems – this often leads to poor model performance, missed opportunities, and, in some cases, costly errors.

On the other hand, high-quality data is a strategic asset that gives AI models the context, clarity, and consistency they need to function properly. Here are the main reasons why focusing on data quality is one of the smartest decisions when implementing AI systems.

Confidence starts with a strong data foundation

You can only trust AI-driven insights when they’re based on reliable, well-prepared data. If your data is full of gaps, duplicates, or inconsistencies, your AI system will likely reflect that. Data accuracy is especially critical when you’re using machine learning for forecasting or natural language processing to analyze text documents.

Reducing the risk of expensive mistakes

Bad data doesn’t just lead to confusing outputs – it can cause real damage. From miscalculations to privacy concerns around sensitive data, poor data quality can erode trust and introduce unnecessary risk. In healthcare, for instance, inaccurate or incomplete datasets can affect how AI models assess patient outcomes, potentially leading to flawed diagnoses or treatment plans.

Enhanced decision-making

Again, artificial intelligence crucially depends on the data it’s given, which, in turn, affects the quality of decision-making you base on AI-driven insights. Only high-quality data allows you to derive accurate insights for well-informed decisions.

Keeping everyone on the same page

AI projects usually involve teams from IT, operations, compliance, and beyond. When data is well-organized and accessible (that is, high quality), it’s easier for people across departments to collaborate and stay aligned.

Laying the groundwork for growth

As AI systems expand, the data behind them needs to scale, too. However, low-quality data can slow everything down. When supported by a comprehensive data governance framework and thoughtful data preparation, high-quality data makes it easier to scale up, add new use cases, and respond quickly to changes – all without sacrificing performance.

The ingredients of good data

Even the smartest AI algorithms can’t do much with messy, irrelevant, or incomplete information. If you want your AI systems to work as intended, make sure your data meets a few key standards. Here’s what to look for:

Accuracy

Accuracy means the extent to which your data accurately represents the real world. If it’s outdated, inconsistent, or wrong, your AI model will be basing its outputs on a distorted picture.

Completeness

Missing data is more than an inconvenience – it can skew results or lead to blind spots. The more complete your dataset, the more reliable the model outcomes.

Timeliness

The next feature of good data quality is timeliness, that is, whether the data is up-to-date. Outdated information can result in incorrect conclusions and adversely impact decision-making.

Relevance

Not all data is useful. Feeding your AI model with everything you have might feel thorough, but if it’s not directly tied to the problem you’re solving, it just adds noise.

Uniqueness

It’s important that your data is free from duplicate records. Otherwise, your AI system can over-represent certain data points or trends.

Diversity

Finally, AI trained on a narrow set of inputs can end up reinforcing bias. Contrarily, diverse, high-quality training datasets that reflect different groups, perspectives, and edge cases helps build systems that are fairer and more inclusive.

The risks of poor data quality

Poor-quality data can compromise your entire AI project. Here’s what can go wrong when the quality isn’t there:

Inaccuracy

The main risk of overlooking data quality is inaccurate output. If the input is wrong, don’t expect high-quality output. That can lead to flawed predictions, misleading insights, and, ultimately, wrong decisions.

Biased outcomes

Data that reflects only part of the picture or carries hidden assumptions can produce outcomes that are unfair or unbalanced, leading to ethical considerations.

Inefficiency

Fixing data problems after the fact is time-consuming and expensive. When teams are stuck cleaning up errors or reworking models, progress slows – and so does impact.

A real-world example: Amazon’s biased recruitment tool

What happened?

Amazon developed an AI recruitment tool to screen job applicants. However, the tool ended up discriminating against women.

Why did it fail?

The training data was based on 10 years of resumes, most of which came from men (reflecting historical biases in tech hiring). As a result, the AI learned to penalize resumes with terms like “women’s chess club” or female-dominated universities.

Lesson learned

Poor data quality reinforces systemic inequalities. It’s crucial to use diverse and balanced datasets to avoid embedding existing prejudices into AI systems.

Preparing your data for AI success

Before AI can deliver meaningful insights, predictions, or automation, it needs the right data. A solid data strategy is critical in any AI initiative. Here are the key steps companies need to follow to ensure high data quality:

Auditing current data assets

As an organization, you likely already store terabytes of structured and unstructured data. Before using it in any AI model, it’s important to understand its volume and quality as well as evaluate your current data management practices, which is where data audit comes in.

Of course, no two businesses are the same, so data audit might look different depending on your specific needs and objectives, but still, there are steps that apply in most cases:

1. Set objectives. Start by clearly outlining what you expect to achieve with data audit – this could be anything from assessing data quality to checking for compliance.
2. Identify your external and internal data sources. Where does your data come from – databases, CRM systems, social media, third-party apps, or else? 
3. Map the data flow. Map out how data moves within your organization to understand who has access to it, how it’s shared, and how long it’s stored.
4. Evaluate data quality. This step involves checking your datasets for gaps, errors, or duplicates. Also, assess whether your current data is up-to-date, accurate, and relevant.
5. Review data security. Implement adequate security measures to prevent breaches.
6. Double-check for compliance. Your company may be subject to privacy regulations and laws, which may vary based on your industry and location. To avoid legal issues, make sure your data management policies align with the requirements that apply to you.
7. Record your findings. Create a report detailing your audit results and recommendations for improvement.

Handling unstructured data

A wealth of valuable insights comes from images, emails, customer reviews, audio files, and other unstructured sources rather than neat tables. Before this kind of data can be used by AI, it needs to be transformed into a structured form. This typically involves using several techniques:

• natural language processing (NLP) to convert text data into machine-readable formats
• optical character recognition (OCR) to extract text from scanned documents and images
• topic modeling to identify key themes across large collections of documents
• named entity recognition (NER) to detect and categorize names of people, organizations, locations, etc.
• sentiment analysis to assess the emotional tone or opinion expressed in the text
• tagging tools to classify and label visual content

Data cleaning

As we’ve previously mentioned, inaccurate, out-of-date, and incomplete data can skew the results of AI processing. Data cleaning is a procedure that helps minimize this risk and improve data quality. This process consists of the following steps: 

1. Remove duplicates. This involves spotting and eliminating duplicate records to prevent over-representation of values and reduce the possibility of distorted analysis.
2. Format your data consistently. Make sure that values like dates, times, and currencies follow a consistent format – for example, using YYYY-MM-DD for dates and ISO 4217 codes for currencies.
3. Clean up errors. At this step, fix incorrect data entries, such as misspellings or inaccurate numerical values.
4. Address gaps in data. There are two ways of dealing with missing values: you can either fill in the gaps or completely remove such records.
5. Handle outliers. Outliers refer to data points that deviate from the norm and thus can hinder the performance of AI algorithms. Trimming (removing the extreme values), capping (limiting their impact by setting a maximum threshold), and applying transformations (like logarithmic scaling to normalize data) are the most common techniques to deal with such anomalies.
6. Reduce the noise. Your datasets may include inconsistencies that don’t reflect accurate trends. Your task is to filter them or smooth abrupt spikes so that AI can focus solely on meaningful signals.

Data transformation 

Data transformation refers to converting data into a format that AI systems can understand and process. It involves the following:

• Normalization. Normalization means scaling numbers to a standard range, usually 0 to 1. Why? Let’s say you’re giving AI both income (which could be thousands of dollars) and age (which is usually under 100). If you don’t scale them, the AI might think income is more important just because the numbers are bigger.
• Encoding categorical variables. Artificial intelligence understands numbers better than other values, so it’s recommended to “translate” categorical data, like “Yes/No” or “High/Medium/Low” into numbers. 
• Data aggregation. This step focuses on combining multiple data points into a single, more manageable value. For instance, rather than analyzing sales figures for each day, you can calculate the total sales for the entire month. Such an approach simplifies analysis and helps reduce the amount of data the AI system needs to handle.

Data labeling

Consistent data labeling plays a critical role in the effectiveness of supervised machine learning models. High-quality labeled data allows AI systems to effectively identify patterns. For example, a financial institution can label transactions as fraudulent or legitimate to give the AI model clear examples to learn. The common data labeling techniques include:

• Manual labeling. This method involves human annotators labeling data.
• Semi-supervised learning. Semi-supervised learning uses a small set of labeled with a large pool of unlabeled data to train the model.
• AutoML. Automated machine learning platforms offer tools that help automate parts of the labeling process.

Data Splitting

When building an AI model, you want to know if it’s actually learning, not just memorizing, so you need to test it on data it hasn’t seen before. That’s why you need to split the dataset into separate sets for training, validation, and testing. The best practices for data splitting include: 

• 70/20/10 rule. This approach suggests splitting data into 70% for training, 20% for validation, and 10% for testing. 
• Stratified sampling. Stratified sampling means dividing your dataset into distinct groups (called strata) and then sampling from each group proportionally, which plays a critical role in certain cases. For example, you’re building an AI model to detect email spam, and your dataset includes 90% not spam and 10% spam. If you do a random split, your test set might accidentally have very few spam emails, making it hard to evaluate your model. With stratified sampling, you make sure both the training and test sets contain the same ratio of spam to non-spam emails (90/10) so your model gets a balanced view.
• Cross-validation. This technique involves training and testing your model multiple times on different parts of your dataset (called folds), allowing the model to train and test on different subsets.

Continuous monitoring

Having high-quality data today doesn’t mean it will stay that way. Business operations and processes evolve, customer behavior changes, and new data sources appear, all of which can affect data quality over time. Given that, it’s important to monitor your data quality even after your AI model is up and running.

For this, set up routine checks to catch quality issues like missing values, format changes, or unexpected spikes in the data. Also, watch for data drift, that is, when the patterns in new data start to differ from what your model was originally trained on. Finally, perform regular security audits to detect and address vulnerabilities.

Beyond quality: building a data-driven culture

To get real value from AI, you need more than just intelligent algorithms and high-quality data – you need the right mindset. A data-driven culture helps teams prioritize data quality, make better decisions, improve efficiency, and stay focused on business growth. So, how do you build a data-driven culture within your organization?

1. Start from the top

Cultivating a data-oriented culture starts with leadership leading by example. When managers prioritize data in their decision-making processes, it sets the tone for all the others.

2. Invest in employee training

It’s crucial that everyone in the organization has a basic understanding of your data initiatives. The best way to achieve this is to arrange data literacy training across all departments.

3. Set up clear data governance

Build a solid data governance framework to manage data preparation, data collection, and data management processes across your organization.

4. Stimulate experimentation

Encourage your teams to use data-driven insights to innovate – this cultivates an environment where data is viewed as a valuable instrument in driving business success.

5. Monitor and refine

Building a data-driven culture is an ongoing process. It’s important to regularly gauge the impact of data-driven decision-making on business outcomes, demonstrating the value of data.

What’s next?

All things considered, data quality and proper data management create a crucial foundation for the productive performance of your AI systems. If you consider implementing artificial intelligence into your business processes, DeepInspire can help.

With 25+ years of experience in product development, including AI development, we build innovative solutions for fintech, healthcare, manufacturing, and other sectors, helping businesses embrace data-driven digital transformation. We also provide expert data valuation services to help you make the most of your data assets. Contact us today to unlock an AI-driven future.