In today’s AI-driven world, the importance of high-quality data has sky-rocketed. Because businesses rely on data for decision-making, Data Quality Management (DQM) practices are becoming essential for ensuring that data used is both reliable and effective.
But what exactly does DQM entail?
What is Data Quality Management?
According to SAS Institute [1], DQM provides a “context specific process for improving the fitness of data that’s used for analysis and decisions-making” [1] . Essentially, it makes sure that data is accurate and trustworthy for any business application.
DQM’s goals can be broken down into several key areas:
● Validity
● Accuracy and Precision
● Redundancy Erasure
● Consistency
● Timeliness
These measures assess how reliable data is and provide organizations with confidence that their data can support advanced business tools- especially AI models.
How Can DQM Enhance AI?
Data forms the backbone of any AI model, which means its quality directly impacts AI’s effectiveness. As Thomas C. Redman highlights in a recent Harvard Review Article, “companies are beginning to realize that, properly managed, data [is] an asset of potentially limitless potential… [and] AI unlocks that potential” [2].
Through key DQM practices, data quality can be improved in several important ways, enabling AI to perform at its best:
Data Profiling
This process involves examining data to identify patterns, inconsistencies, and quality issues. For AI, this helps spot potential problems early on, ensuring that only reliable data is used for training the model.
Data Cleansing
Data cleansing involves correcting errors to ensure data is reliable and usable. This step is crucial for AI training, as inaccurate or flawed data could impede the model’s performance.
Data Standardization
Standardization ensures that data follows a consistent format, such as uniform date formats. This makes it easier for AI models to process and analyze the data, ensuring more consistent and accurate results.
Data Quality Assessment
This involves evaluating the data against quality metrics to identify areas that need improvement. By assessing the data before it’s used in AI, businesses can address any gaps or weaknesses that may affect the model’s performance.
Data Enrichment
Enriching data involves adding more context and detail from both internal and external sources. For AI, this enriches the knowledge base, enabling models like language learning models (LLMs) to provide deeper and more nuanced responses.
The Importance of Data Integration for Effective DQM and AI Performance
While DQM is essential, data integration plays a critical role in its success. Data integration involves combining data from various sources across an organization, ensuring that AI models have access to unified and comprehensive datasets.
However, despite its importance, AI models often fail due to poor data. A study by Gartner, used in a Venture Beat article, revealed that 85% of AI failures are attributed to inadequate data [3].
A major contributor to poor data quality is ineffective data integration.
A 2024 KeyMakr report found that 89% of businesses face challenges in data integration, which closely correlates with difficulties in DQM [4]. With this, PR Newswire found that nearly 8 out of 10 businesses also struggle with data quality management [5].
These statistics highlight a clear pattern:
● 89% of businesses face data integration challenges
● 80% struggle with data quality management
● 85% of AI models fail due to poor data
These figures underscore the importance of strong data integration for successful DQM and, by extension, the performance of AI models.
Data integration is crucial for providing accessible, unified, and reliable data across the organization, which is essential for both effective DQM and successful AI applications.
How Leading Businesses Overcome Data Integration Challenges?
So how do the 11% of companies that don’t face data integration difficulties succeed? Often, the answer lies in the use of advanced data integration tools.
Tools like Kore Integrate, IBM’s DataStage, and Oracle Data Integrator offer comprehensive DQM features, including ETL (extract, transform, and load) and EAI (enterprise application integration). These tools help businesses profile, cleanse, standardize, enrich, and assess data throughout the integration process, creating a single source of accessible and reliable data for AI models to use.
In short, data integration tools play a pivotal role in achieving DQM, which directly impacts the success of AI models.
Conclusion
Data Quality Management is a foundational practice that enhances business operations and supports AI model effectiveness. By implementing DQM practices and utilizing integration tools, businesses can improve both their data quality and AI outcomes. Ultimately, high-quality data leads to high-performing AI.
References:
[1] Bauman, John. “Data Quality Management What You Need to Know.” SAS. Accessed November 23, 2024. https://www.sas.com/en_us/insights/articles/data-management/data-quality-management-what-you-need-to-know.html#:~:text=Data%20quality%20management%20provides%20a,and%20more%20complex%20data%20sets.
[2] Thomas C. Redman, “Ensure High-Quality Data Powers Your AI,” Harvard Business Review, August 12, 2024, https://hbr.org/2024/08/ensure-high-quality-data-powers-your-ai
[3] Reisner, Sharon. “Why Most AI Implementations Fail, and What Enterprises Can Do to Beat the Odds | VentureBeat.” VentureBeat, June 28, 2021. https://venturebeat.com/ai/why-most-ai-implementations-fail-and-what-enterprises-can-do-to-beat-the-odds/
[4] Pokotylo, Paul. “Challenges in Maintaining Data Quality.” Keymakr, August 26, 2024. https://keymakr.com/blog/challenges-in-maintaining-data-quality/
[5] Ataccama, “Data: Nearly 8 in 10 Businesses Struggle with Data Quality, and Excel Is Still a Roadblock,” PR Newswire: press release distribution, targeting, monitoring and marketing, April 7, 2021, https://www.prnewswire.com/news-releases/data-nearly-8-in-10-businesses-struggle-with-data-quality-and-excel-is-still-a-roadblock-301263583.html
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