Ensuring data quality throughout an organization helps companies get the most out of their GenAI initiatives.
Recent estimates project that approximately 328.77 million terabytes of data are created each day, and global data creation is forecast to grow to more than 180 zettabytes by 2025. In today’s digital-first, data-driven world, business leaders face a myriad of challenges when it comes to harnessing the power of their massive amounts of data, including navigating increased data regulation and the acceleration of generative artificial intelligence (GenAI). However, with all these challenges vying for leaders’ attention, there is one issue in data management that might not be getting the attention it deserves or requires—data quality.
What is Data Quality, and Why Does It Matter
Data quality refers to the form of measurement used to determine the condition or health of an organization’s data or its data sets. It typically includes implementing rules for the organization’s data that focus on five factors: accuracy, completeness, consistency, reliability, and timeliness (or whether the data is up to date).
One of the main reasons for establishing high-quality data within an organization is to help business leaders make good business decisions. However, the expansion of AI has shown that the need for data quality goes beyond that, as AI only performs well when it’s fed with high-quality data. The majority of professionals agree that AI is critical to business success, yet many organizations still struggle to progress their AI projects – and data quality issues are one of the reasons why. In a recent study we conducted, decision-makers cited poor data quality as a top barrier hindering AI implementation. As organizations establish high-quality data, they will be more prepared to implement AI and help their organization remain competitive and successful in a difficult market.
See also: Data Quality Remains Biggest Detriment to AI Success
Maintaining Data Quality for AI
Many organizations implement AI for a variety of business cases, including forecasting, risk prevention, churn prediction, pricing optimization, classification, and more. In some cases, those AI models can be biased based on old data. With better data quality practices, which include providing fresher data, AI models can become more ethical and fairer. Eventually, high-quality data will lead to increased trust and usage of AI in organizations. This allows teams to make faster decisions based on these models and ultimately reduce costs.
Not only is there a lot to gain from ensuring high-quality data, but organizations have a lot to lose if they don’t. According to Gartner, poor data quality costs organizations an average $12.9 million every year. High-quality data can help organizations outperform competitors, remain seamlessly data-driven and, in some industries, high-quality data enables businesses to better adhere to data protection laws and ethical guidelines.
As the world of data continues to change, here are four best practices to follow to establish high data quality within your organization.
Best Practices for Establishing Data Quality
1. Adopt a holistic approach to data management. As data leaders work to establish data quality, it’s imperative that they develop a holistic and ongoing approach to their data management that considers both technological and organizational aspects. Organizations that have a multi-step process, rather than rely on a single method, are better able to test and measure data quality throughout a series of tasks and determine which ones work best for their purposes.
2. Foster collaboration across teams. When it comes to data quality, the human element is one of the most important aspects, but it is often overlooked. The quality of an organization’s data is backed by subject matter expertise, making it not just a technology challenge alone. As such, effective data quality management requires collaboration across multiple teams, including those over IT, data science, and business and compliance. This ensures that all the right subject matter experts are involved and able to verify the quality of the data together.
As organizations grow, fostering collaboration between teams and across departments can become more difficult, so making it a top priority now is key to long-term success.
3. Provide data quality training. Promoting team collaboration will do little to help an organization’s data quality if the individuals who work with or have an impact on the data are not properly educated. Training programs will be most effective if they involve individuals from the C-suite all the way down to data analysts and data scientists to show how important the initiative is for the company. Additionally, providing this type of training will help spread the necessary awareness for employees to better understand the priority that data quality holds within that organization.
4. Establish a data governance framework. As organizations work to implement the above human-centric practices to establish data quality, they should also institute a data governance framework to ensure any subsequent data analysis can be tailored to the user’s needs. A good data governance framework can be implemented by the following five practices and ensure data remains accurate, complete, consistent, reliable, and timely.
- Data profiling: Review and analyze data to better understand its source and how it’s structured.
- Data cleaning: Address or fix incorrect, duplicate, or incomplete data, including detecting outliers to avoid overhead in modeling.
- Data standardization/normalization: Transform data to fit a uniform format for ease of use
- Data validation: Define and employ a set of rules to validate the integrity and accuracy of data before it’s used.
- Metadata documentation: Record data transformation to provide visibility and transparency.
As companies work to ensure data quality throughout their organization, they will be better able to remain competitive in this difficult and ever-changing technological market and help their organization leverage the most out of GenAI initiatives.
