AI in Retail Relies on Clean, Accurate Data

Artificial intelligence is only as good as the data that trains it. If disparate, unorganized data is input to AI-powered tools, the results may be inaccurate or unusable.

Research from Nvidia found that while 64 percent of large retailers (annual revenue exceeding $500 million) are already using AI, 22 percent are currently assessing or piloting it. Therefore, in order for retailers of any size to keep up with industry players that are already leveraging these tools, the data used for AI inputs must be rock-solid.

To ensure your data is up for the job of training AI, consider these six strategies:

1. Detect and correct data anomalies.

Identifying outliers like rare items, events or observations in your data baseline is key to optimizing AI. Although data anomalies don’t always signify something is wrong, they're worth investigating to understand why the deviation occurred.

Anomaly detection is done through statistical, machine learning and clustering-based methods. Though the right method depends on data types, distribution and computational resources.

By detecting and solving anomalies early, retailers are more likely to ensure accurate models and avoid problems down the road.

2. Automate data cleansing.

Automating data cleansing — i.e., the process of fixing or removing incorrect, corrupted, duplicate or incomplete data within a dataset — is a crucial component to managing data effectively and ensuring accuracy and trustworthiness.

This step is imperative because clean, well-prepared data prevents AI from generating skewed results and reduces the computational resources needed by training models. Furthermore, clean data that’s automated frees retail tech teams to focus on developing AI models instead of fixing data problems.

3. Monitor data quality constantly.

Data quality monitoring includes assessing, measuring and managing data for accuracy, consistency and reliability. Round-the-clock monitoring supports data quality issue detection before it gets out of hand, negatively impacting AI and overall business performance.

After defining data quality metrics, such as completeness, accuracy and validity, conduct regular audits to ensure data quality remains high.

4. Make data governance a habit.

Strong data governance makes certain retailers comply with regulations such as the General Data Protection Regulation (GDPR). It also confirms that data is clean, consistent and accurate before being used as AI inputs.

To practice data governance, develop data quality standards such as creating a data dictionary and establishing retention and deletion policies. Practice and recognize data stewardship for employees promoting your data governance initiatives.

5. Keep data safe and secure.

Data breaches can damage reputation, revenue and trust. Lack of sufficient data security measures in AI systems could lead to noncompliance that is often accompanied by legal liabilities and fines.

Secure your data through encryption, access controls, firewalls, regular backups, continuous updates and security awareness training.

6. Make sure data is standardized.

Through data standardization, AI models learn patterns more effectively and consistently. By practicing the most common standardization techniques — data cleaning, data governance, data normalization and data transformation — retailers are ensuring data consistency, which is essential for training AI and machine learning models.

Ready, Set and Go With AI

Clean, high-quality data is crucial to building an unbiased, high-quality AI model for retail organizations. Reliable, usable data for strong AI outputs is necessary for cost efficiency and scalability.

By adopting and implementing these six AI strategies, retail tech leaders can create an effective AI model and tap into a portion of the projected billions or even trillions of dollars in its economic value.

AI is transforming the retail landscape. But remember, AI needs reliable data to train it. Retailers should first consider the state of their data before tapping into the value of AI.

Duane Barnes is the president of RapidScale, a Cox Business Company, encompassing the acquisitions of RapidScale and Logicworks.