AI’s value relies on the quality of your data

Artificial intelligence (AI) has become an invaluable technology reshaping industries and fostering unprecedented levels of innovation. It is also helping Australian organisations improve their bottom line. According to the CSIRO, last year, Australian businesses reported an average revenue growth of over $360,000 for each AI-enabled solution implemented.

However, AI’s output is only as good as the quality of data and effort you put into it. Just as high-quality ingredients are essential for a gourmet meal, high-quality data is crucial for accurate, reliable, and actionable AI insights.

Understanding data quality

Many challenges of AI implementation can come down to data quality, which encompasses various attributes such as accuracy, completeness, reliability, and relevance. In 2021, Gartner reported that businesses lose an average of $12.9 million yearly due to poor data quality.

In the context of AI, data quality directly influences the performance of machine learning models and other AI data systems. This can lead to inaccurate predictions, bad decisions, and, ultimately, suboptimal outcomes that can undermine trust in AI technologies.

How data quality impacts AI

The high-quality data you produce enables AI models to have a greater learning scope and output of predictions. For instance, in a manufacturing setting, accurate and comprehensive production data can optimise production flow and set up predictive models used for calculating and ordering raw materials. Conversely, low-quality data can introduce biases, reduce accuracy, and lead to erroneous outputs, which can leave you with production bottlenecks and surplus materials.

Bias in AI models often stems from biased data. The model will likely produce biased results if the training data inaccurately represents the population or contains systemic errors. Data quality is critical in applications like hiring, lending, and law enforcement, where biased decisions can lead to unfair treatment of individuals. Therefore, ensuring high-quality, unbiased data is essential for fair and accurate AI outcomes.

Strategies for ensuring data quality

Attaining data quality means adopting a series of strategies:

Data cleaning?

Data cleaning fixes or removes data that’s been corrupted, incorrectly formatted, or rendered incomplete within a dataset. Regularly cleaning data removes these errors. For example, ensuring that all addresses follow the same format in a dataset containing customer information can prevent confusion and errors during analysis.

Data validation

Data validation involves verifying and validating all collected data before using it. Any type of data handling task must include data validation to ensure accurate results, whether it is gathering data, structuring it for presentation, or analysing it.

Data governance

Establish strong data governance policies that define data ownership, stewardship, and quality standards. This framework ensures accountability and continuous monitoring of data quality. Clear data governance policies help manage data access, ensure compliance with regulations, and maintain data integrity across the organisation.

Regular audits

Conduct regular data audits to identify and resolve potential issues. Periodic audits can reveal trends in data quality problems, enabling proactive measures to maintain high standards. These audits should include checks for data accuracy, completeness, and consistency.

How does high-quality data and AI impact key industries?

The importance of high-quality data extends across various AI applications, enhancing their effectiveness and reliability. Here are a few examples:

Government

In Government, AI-driven solutions streamline processes, enhance decision-making and personalise citizen interactions. This enables governments to modernise legacy systems, automate routine tasks, and improve service delivery. Inaccurate data can cause a misallocation of resources, ineffective response to crises, and potential risks due to delayed or incorrect actions.

Manufacturing

Implementing high-quality data in the manufacturing sector can eliminate production build-up and optimise the flow of production by integrating solutions that streamline operations across production, logistics, and customer service. AI provides real-time data and actionable insights, enhancing performance and ensuring no bottlenecks and over-supply of raw materials used. Inaccurate data can lead to erroneous inventory levels and demand forecasts, causing inefficient inventory management.

Retail

For retailers, high-quality data on customer preferences, purchase history, and inventory levels enables AI systems to optimise supply chains, personalise marketing efforts, and improve customer service. Accurate data helps retailers anticipate demand, reduce stockouts, and tailor recommendations to individual customers. Producing inaccurate data can cause overstocking, which leads to excess inventory withholding costs and potential waste of perishable goods.?

Conclusion

The value of AI fundamentally relies on data quality. If you want to harness the transformative power of AI, then you must invest in watertight data quality practices. By ensuring that your data is accurate, complete, and reliable, your organisation can maximise the benefits of AI and drive business growth. High-quality data is not just a technical requirement; it is a strategic asset underpinning AI initiatives’ success.

MakeSense can get your data strategy right

Ready to improve the quality of your data for AI outputs? At MakeSense, we specialise in implementing AI and data solutions, including automation, deep learning, and data engineering. Our experts can deliver tailored solutions to support strategic decisions and drive your company’s future growth.

Visit our AI and Data Services page to learn more about how we leverage AI and data analytics to achieve your strategic goals.