How important is data quality for AI applications?

Applications of Artificial Intelligence across industries are growing at a raging capacity. However, the sobering reality about the buzzword is that almost 80% of AI projects end up unsuccessful, as per Harvard Business Review. Despite their transformative abilities, AI applications do not succeed in achieving the desired results. That’s where one must understand the importance of data quality.

Data quality plays a vital role in the success of AI models. With poor-quality data, even accurate AI models will fall short. In this blog, we will discuss the importance of data quality in AI models and how to maintain it.

Importance of data quality for AI applications 

Data is the fuel behind the functioning of AI algorithms. The data that is fed to AI applications to feed models and train them determines their learning and functioning. Poor data quality can have a severe effect on how AI applications function and the real-life consequences of such outcomes.

To be precise, Gartner’s reports suggest that organizations suffer a loss of $12.9 million annually because of poor data quality. Therefore, companies can never take poor data quality lightly and must overcome it. This also explains why companies spend so much time and effort acquiring accurate data. Data scientists play a very vital role here in filtering data and overcoming potential hurdles.

Negative impacts of poor data quality in AI models

Bad data can hamper AI applications in several ways. Here are some of the consequences of bad-quality data:

• Inaccurate outcomes:

ML models trained on poor or erroneous data might not function as expected and lead to misjudgments. In real life, this could mean that AI solutions present wrong predictions or poor outcomes if data quality is poor. 

Incomplete or biased data can limit AI models' ability to reach diverse outcomes, making AI unreliable. These instances are deal-breakers, especially for sensitive applications within finance, healthcare, and jobs.

• Flawed AI models:

Poor data refers to unverified or inadequate data that can impact the functioning of AI models. Such data compromises the accuracy and performance of the models. Training models with such data might lead AI applications to several errors and inefficiencies.

• Reiteration costs & efforts:

AI models might function inefficiently if the training data is received from an unreliable or unverified source. To mitigate such limitations it would require companies to invest their time, resources, and costs to filter the data. Once the data is filtered, then AI models must retrain with accurate data to be precise.

• Poor brand reputation:

Gaps and errors in data then influence ML models to produce AI hallucinations and mistakes. Such AI misconduct can hamper a brand’s reputation, as customers might end up with a poor customer experience. In addition to brand reputation, companies might also suffer regulatory and legal complications because of AI misconduct.

However, that’s not all of it, AI models can have several unique consequences because of poor data quality. Depending upon the industry, AI applications can have different complications and impacts based on their sensitivity. In the next section, we will discuss how data quality is critical for several industries.

Importance of AI data quality across industries

Industries such as healthcare, finance, law, and automotive have higher stakes where AI application failures can not be allowed. Below are some examples of AI application failures among these industries because of poor data quality:

For healthcare: 

The healthcare industry is an industry where stakes are high, and any errors can not have severe consequences. For instance, faulty data can compromise the model's functioning, which can then lead to misdiagnosis and misdirect to a wrong treatment. 

Wrong treatment or medicine therapy can even worsen the patient's health. Therefore, when trusting AI for healthcare, accurate data becomes non-negotiable.

For finance:

AI applications in finance are quite similar to those in healthcare in severity. However, instead of health, here there is monetary loss to be afraid of. AI for finance is typically used for fraud identification, risk assessment, and eliminating financial loss. Poor data can lead to irreversible damage in finance as well.

For automotive:

From ADAS to self-driving vehicles, AI is en route to taking over the future of the automotive industry. However, there is a long way to go to fully self-driven vehicles. Poor data about roads, directions, and objects can lead to erroneous judgment and, therefore, can even cause accidents or compromise safety.

For law & judiciary:

Poor or incomplete data can lead to premature predictions and conclusions in AI applications. Such judgment can have severe consequences in law and judiciary applications. Particularly, imperfect data as a reference can lead to biased conclusions and unfair convictions.

How to maintain data quality for AI solutions

The key challenge for AI applications is the scarcity of authorized data sources. If the data is acquired from an unauthorized source, then it is critical to process such data to eliminate any potential risks. A top-level process is required to process the data quality. 

1. Filtration: The first step is collecting data from valid data points. By identifying and picking the most authentic source, one can get high-quality data for AI applications. Data scientists can then help analyze the data to filter out any inconsistencies or gaps.

2. Enrichment: Data scientists can enhance their datasets through data enrichment, where additional data can be supplemented through external sources. Data enrichment helps ensure adequate data is available for AI applications.

3. Validation: Data quality must be verified periodically to ensure consistency and accuracy. Data verification protocols with the right references and standards play a key role in effective validation.

4. Feedback loop: A proper feedback loop must be in place to ensure that AI solutions perform as expected. Effective AI implementations require an accurate feedback solution that can identify and mitigate errors or gaps because of data quality issues.

5. Data labeling: Data labeling is the key to making the most of gathered data and training AI models. It plays a key role in ensuring an AI model’s efficient performance.

6. Eliminating data bias: Data augmentation is critical to identifying and detecting any bias within the data. Such AI bias detection can help overcome any data bias before it is too late and avoid any discrepancies in AI applications.

In conclusion,

Data quality is the foundation behind any successful AI implementation. If poor, the impact of data quality on AI can be severe. As a company, one must evaluate their data quality before any AI application implementations which could eventually speed up AI innovations.