CEO of Acumatica, a fast-growing cloud ERP company. John Case has nearly 30 years of industry leadership in cloud services.
The transformative power of technology has embarked on an exciting phase. Artificial intelligence (AI) and machine learning (ML) are becoming more thoroughly integrated into business operations, impacting how organizations make decisions. These technologies promise to give businesses a competitive edge by helping them make smarter, faster and more data-driven decisions.
As we consider ways to leverage new technological capabilities and breakthroughs, we must not lose sight of a fundamental reality: The effectiveness of these AI- and ML-driven insights depends on the quality of the data. Data is the lifeblood of these technologies. It’s the fuel that ignites a world of possibilities. Before a business can trust AI or ML, it must first trust the quality of its data.
The problem: Most business data is not “clean” enough.
In the context of AI and ML, “clean” data refers to data that is free from errors, missing values, inconsistencies, duplications and irrelevant information. For these systems to work effectively, data needs to be accurate, relevant and consistent. Clean data is well-organized, complete and properly categorized, ensuring that the systems can process it without encountering misinterpretations or gaps in understanding.
As businesses increasingly adopt new capabilities, they expect them to produce immediate and accurate results, but this assumption overlooks a key reality: The data feeding those systems is often rife with issues. Many companies face significant challenges with the quality of their data, and many are not aware of how flawed it really is. One survey found that 71% of marketing executives believe their teams have sufficient data for better decision-making but that the data is often not usable because it’s not clean enough.
Without trustworthy data, the effectiveness of AI and ML solutions is negated, and rather than being an instrument for insightful decision-making, it becomes a source of faulty business decisions and frustrated stakeholders.
Data validation is a critical first step in building reliable systems.
What does it mean to validate or clean your data?
Data cleaning goes by many names—including data scrubbing, data purification, data refinement and data validation—all of which refer to the process of preparing data for analysis by identifying and correcting errors, inconsistencies and irrelevant or redundant information. One of the key tasks in cleaning data is identifying anomalies—unexpected or irregular data points that don’t align with the rest of the dataset.
These anomalies could be as simple as typos (misspelled names or incorrect product codes), missing values (blank fields where important information should be) or more complex issues such as data that doesn’t conform to expected formats (such as dates entered incorrectly or inconsistent currency symbols).
Regardless of the source of the issues, these types of anomalies can have a significant impact on system performance. While the process of data scrubbing may seem tedious and time-consuming, it’s paramount for business success. By proactively identifying errors and cleaning the data, businesses set the foundation for accurate AI- and ML-driven predictions.
Don’t forget data governance.
While data validation addresses immediate errors and inconsistencies, a long-term solution to ensuring data quality must include robust data governance policies.
Data governance refers to the set of procedures and standards that dictate how an organization collects, manages and uses data. A well-implemented data governance framework ensures that data remains consistent and secure over time.
By establishing clear protocols for managing data throughout its lifecycle, a robust data governance policy prevents future data quality issues and maintains data integrity across all departments and systems. Adopting strong data governance practices enables businesses to maintain a high level of data quality and reduce the need for reactive data cleansing, saving them time and resources.
Tech vendors play a critical role in this process by helping businesses identify and address data anomalies and ensure that the technology is only working with high-quality information. Only when companies can be confident in the integrity of their data can they truly trust AI to drive meaningful and reliable business outcomes.
Better data means better results.
It’s easy to be dazzled by the potential of leading-edge technology. However, beneath the sophisticated algorithms and impressive outputs lies one essential truth: AI and ML are only as good as the quality of the data they rely upon.
Companies can ensure the capabilities of their business management systems are working with the best possible information by implementing the fundamental policies needed to maintain clean data. Prioritizing valid data improves prediction accuracy and boosts confidence in the resulting outcomes, enabling companies to make smarter, more informed decisions that drive results, spur growth and lead to business success.
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