Data Quality Management (DQM). It’s on top of many businesses’ minds. In this AI generation, quality data is proving to be more important than ever. As such, many businesses try to achieve data quality through DQM practices. So, what exactly is DQM? What Is Data Quality Management? According to the SAS institute, DQM provides a “context-specific process for improving the fitness of data that’s used for analysis and decision-making”[1]. Essentially, it is a process to ensure that data is reliable and effective. Specific goals of DQM can be broken down into a few categories: ·         Validity ·         Accuracy and Precision ·         Redundancy Erasure ·         Consistency ·         Timeliness These serve as the primary measurements of how effective data can be. Through these practices, organizations can feel confident that their data is reliable to use on high-level business tools— especially AI. DQM Practices Improve AI Data makes up the structure of any AI model, so it’s important that this data is high quality. Defined by Thomas C. Redman in their 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]. DQM enables AI’s potential through important practices that improve data quality. These include: 1) Data Profiling This refers to the process of examining data to understand its structure and content to identify patterns, anomalies, and potential quality issues. For AI, data profiling is important for identifying potential quality issues that could hamper a model. 2) Data Cleansing The cleansing process fixes data errors to ensure that it is usable and reliable. When training an AI model, this process prevents the model from using data that would otherwise be a hindrance to it. 3) Data Standardization Standardization establishes a normalized format for data values (i.e., standard date formats). In doing so, AI models often have an easier time analyzing data, utilizing it, and providing consistent results. 4) Data Quality Assessment Assessment is the process of evaluating data quality dimensions to identify areas of improvement. Data quality assessment ensures that blind spots are filled, and data is improved before it reaches an AI tool. 5) Data Enrichment Data is enriched when more context and detail are added to the data itself via relevant internal and external information. This allows AI models like Language Learning Models (LLMS) to expand and deepen their knowledge base, resulting in more encompassing and nuanced responses. DQM is Necessary for Reliable AI Data Integration DQM is clearly crucial to ensure proper business data. Yet, a crucial aspect of DQM can be found in data integration. Data integration is important for pulling together data across an organization and is crucial for establishing an adequate AI model. Practically every large business understands this. Yet, AI is still failing at an alarming rate. A Gartner study, used in a Venture Beat article, found that when AI models failed, a whopping 85% did so because of inadequate data [3]. The culprit of this bad data can often be traced back to inadequate data integration. Because data integration is crucial for bringing together relevant data, it is pivotal in enabling DQM. In fact, a 2024 report from KeyMakr found that “89% of businesses face data integration hurdles” [4]. Not so coincidentally, a PR Newswire article found that nearly nearly 8 of every 10 businesses struggle with DQM [5]. Together, these statistics point to a trend: ·         89% struggle with data integration ·         80% (roughly) struggle with DQM ·         85% of AI models fail because of poor data It follows that the success of DQM relies on the strength of data integration. In turn, DQM is closely correlated to the probability of success for an AI model. Data integration makes up a large part of DQM because the goal of integration is to ensure reliable, accessible, and unified data across an organization. To integrate properly, data integration must also provide the capability to execute many important DQM practices. So, how do the 11% of businesses that don’t struggle with integration find success? Often, the answer lies in data integration tools. Tools like IBM’s DataStage, Kore Integrate, and Oracle Data Integrator help businesses profile, cleanse, standardize, enrich, and assess their data. This is done during the data integration process, which pulls organization-wide data into a single, accessible source that can be accessed by an AI model. To put it simply, data integration tools provide an effective means of achieving DQM, ultimately increasing the success of AI models. Conclusion Data quality management is an effective way to improve your business from its foundation to its peak. Using comprehensive DQM practices and integration tools can improve both the operations of your business and the functionality of its AI. At the end of the day, high-quality data breeds high-quality 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 quality management provides a,and more complex data sets. [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 Data Quality Management (DQM). It’s on top of many businesses’ minds. In this AI generation, quality data is proving to be more important than ever. As such, many businesses try to achieve data quality through DQM practices. So, what exactly is DQM? What Is Data Quality Management? What Is Data Quality Management? According to the SAS institute , DQM provides a “context-specific process for improving the fitness of data that’s used for analysis and decision-making”[1]. Essentially, it is a process to ensure that data is reliable and effective. Specific goals of DQM can be broken down into a few categories: SAS institute ·         Validity ·         Accuracy and Precision ·         Redundancy Erasure ·         Consistency ·         Timeliness These serve as the primary measurements of how effective data can be. Through these practices, organizations can feel confident that their data is reliable to use on high-level business tools— especially AI. DQM Practices Improve AI DQM Practices Improve AI Data makes up the structure of any AI model, so it’s important that this data is high quality. Defined by Thomas C. Redman in their 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]. DQM enables AI’s potential through important practices that improve data quality. These include: Harvard Review Article 1) Data Profiling 1) Data Profiling This refers to the process of examining data to understand its structure and content to identify patterns, anomalies, and potential quality issues. For AI, data profiling is important for identifying potential quality issues that could hamper a model. 2) Data Cleansing 2) Data Cleansing The cleansing process fixes data errors to ensure that it is usable and reliable. When training an AI model, this process prevents the model from using data that would otherwise be a hindrance to it. 3) Data Standardization 3) Data Standardization Standardization establishes a normalized format for data values (i.e., standard date formats). In doing so, AI models often have an easier time analyzing data, utilizing it, and providing consistent results. 4) Data Quality Assessment 4) Data Quality Assessment Assessment is the process of evaluating data quality dimensions to identify areas of improvement. Data quality assessment ensures that blind spots are filled, and data is improved before it reaches an AI tool. 5) Data Enrichment 5) Data Enrichment Data is enriched when more context and detail are added to the data itself via relevant internal and external information. This allows AI models like Language Learning Models (LLMS) to expand and deepen their knowledge base, resulting in more encompassing and nuanced responses. DQM is Necessary for Reliable AI Data Integration DQM is Necessary for Reliable AI Data Integration DQM is clearly crucial to ensure proper business data. Yet, a crucial aspect of DQM can be found in data integration. Data integration is important for pulling together data across an organization and is crucial for establishing an adequate AI model. Practically every large business understands this. Yet, AI is still failing at an alarming rate. A Gartner study, used in a Venture Beat article , found that when AI models failed, a whopping 85% did so because of inadequate data [3]. The culprit of this bad data can often be traced back to inadequate data integration. Because data integration is crucial for bringing together relevant data, it is pivotal in enabling DQM. Venture Beat article In fact, a 2024 report from KeyMakr found that “89% of businesses face data integration hurdles” [4]. Not so coincidentally, a PR Newswire article found that nearly nearly 8 of every 10 businesses struggle with DQM [5]. KeyMakr PR Newswire Together, these statistics point to a trend: ·         89% struggle with data integration ·         80% (roughly) struggle with DQM ·         85% of AI models fail because of poor data It follows that the success of DQM relies on the strength of data integration. In turn, DQM is closely correlated to the probability of success for an AI model. Data integration makes up a large part of DQM because the goal of integration is to ensure reliable, accessible, and unified data across an organization. To integrate properly, data integration must also provide the capability to execute many important DQM practices. So, how do the 11% of businesses that don’t struggle with integration find success? Often, the answer lies in data integration tools. Tools like IBM’s DataStage , Kore Integrate , and Oracle Data Integrator help businesses profile, cleanse, standardize, enrich, and assess their data. IBM’s DataStage Kore Integrate Oracle Data Integrator This is done during the data integration process, which pulls organization-wide data into a single, accessible source that can be accessed by an AI model. To put it simply, data integration tools provide an effective means of achieving DQM, ultimately increasing the success of AI models. Conclusion Conclusion Data quality management is an effective way to improve your business from its foundation to its peak. Using comprehensive DQM practices and integration tools can improve both the operations of your business and the functionality of its AI. At the end of the day, high-quality data breeds high-quality AI. References : 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 quality management provides a,and more complex data sets . https://www.sas.com/en_us/insights/articles/data-management/data-quality-management-what-you-need-to-know.html#:\~:text=Data quality management provides a,and more complex data sets [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 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/ 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/ 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 https://www.prnewswire.com/news-releases/data-nearly-8-in-10-businesses-struggle-with-data-quality-and-excel-is-still-a-roadblock-301263583.html

The Importance of Data Quality Management and Data Integration for AI Models

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