Data Quality: The Key to Unlocking Effective AI and Predictive Maintenance

Introduction

As mathematician Clive Humby famously stated, “Data is the new oil.” Like oil, data must be refined and processed to make it useful. But this concept misses a foundational first step. Before analysis begins or artificial intelligence (AI) algorithms run, it’s critical to ensure the quality of the “oil.” As the adage says: garbage in, garbage out. While this concept is easy to understand, there is little focus on verifying the data that fuels strategies, processes, data systems, and action plans. Even the most sophisticated data analytics platforms and AI algorithms will fall short if supplied with poor-quality data.

An often overlooked or misunderstood aspect of AI is that algorithms cannot differentiate between good and bad data. It works on logic, learning from patterns in the provided data. Robust, high-quality data sets are critical for training and validation, ensuring that AI models are accurate and reliable. If data is insufficient, analyses and algorithms could lead to undesirable outcomes.

A recent survey by Ernst & Young stated that more than 92% of oil and gas companies are either investing in AI or planning to invest in it in the next two years [1]. With such high adoption rates, it’s critical that data quality isn’t overlooked. AI has the potential to transform the oil and gas industry by increasing operational efficiency, reducing costs, and optimizing decision-making processes for seamless, sustainable operations – but only if it is built upon a solid foundation of high-quality data. Understanding where raw data comes from is essential to knowing whether the outputs will be valuable, trustworthy, reliable, and actionable in both the short and long term.

High-Quality Insights Start at the Data Source

A recent study found that 89% of executives stated a high level of data quality was critical for the success of their organizations, yet 75% indicated that they don’t trust their data [2]. As companies strive to accelerate decision-making processes to keep up with increasing demands, they often do so at the sacrifice of quality. Without the right processes and technologies, decision-makers develop plans using data riddled with assumptions, biased inputs, incomplete information, and inaccuracies.

Research by Gartner found that organizations believe poor data quality to be responsible for an average of $15 million per year in losses to their bottom lines [3]. Insufficient data in the industrial sector leads to extensive time, budgets, and resources spent on the wrong objectives. Not only can poor-quality data harm your bottom line, but it can also be hazardous, with sites experiencing unexpected equipment failures and catastrophic events.

Training an algorithm with low-quality data can lead to AI “hallucinations,” where the model produces incorrect or misleading results. Hallucinations are caused by a myriad of data-related factors and can occur frequently in low-quality models. In these cases, the very tool being used to improve decision-making is instead misguiding it.

According to IBM, the best way to mitigate the impact of AI hallucinations is to stop them before they happen by using high-quality data [4]. Robust, high-quality data sets are critical for training and validation, ensuring that AI models are accurate and reliable. Poor data quality results in analyses and AI algorithms that can lead you down the wrong path.