Artificial intelligence (AI) is rapidly changing the way organizations manage and process data. From automating routine tasks to uncovering insights that would be impossible to discover manually, AI is playing an increasingly important role in data management. In this article, we'll explore the five common data management areas where AI is making a significant impact: classification, cataloging, quality, security, and data integration.
Classification: AI is helping organizations more effectively obtain, extract, and structure data from a wide range of sources, including documents, photos, handwriting, and other media. By automating the process of data classification, AI can quickly and accurately sort through large amounts of data, identifying key information and grouping it into relevant categories. This can help organizations save time and resources while also improving the accuracy of their data. According to a report by MarketsandMarkets, the data classification market is expected to reach $8.5 billion by 2023, growing at a CAGR of 26.8% from 2018 to 2023.
Cataloging: AI is also playing a critical role in helping organizations locate data. By using natural language processing (NLP) and other techniques, AI can understand the context and meaning of text-based data, making it easier to search and retrieve relevant information. This can help organizations reduce the time and effort required to find the data they need, allowing them to make more informed decisions. In fact, according to a study by IDC, organizations that use AI-based data catalogs can expect a 20% increase in data discovery and a 50% reduction in time spent searching for data.
Quality: AI can also help organizations improve the quality of their data by identifying and correcting errors. By using machine learning algorithms, AI can detect patterns and anomalies in data, flagging potential errors for further review. This can help organizations ensure that their data is accurate and reliable, reducing the risk of errors and improving the overall quality of their data. According to a study by Gartner, organizations that use AI for data quality management can expect a 25% reduction in data quality errors.
Security: AI is also playing an important role in keeping data safe from bad actors and ensuring that it is used in accordance with relevant laws, policies, and customs. By using advanced algorithms and machine learning models, AI can detect and prevent cyber-attacks and other security threats, protecting sensitive data from unauthorized access. Additionally, AI can also help organizations comply with data privacy regulations, such as GDPR and HIPAA, by automatically identifying and flagging potentially sensitive data. A study by MarketsandMarkets estimates that the AI in cyber security market will reach $14 billion by 2022, growing at a CAGR of 28.5% from 2017 to 2022.
Data integration: Finally, AI is helping organizations more easily build "master lists" of data by merging lists from multiple sources. By using natural language processing and other techniques, AI can understand the context and meaning of data, making it possible to automatically match and combine data from different sources. This can help organizations create more accurate and complete data sets, allowing them to make more informed decisions and gain a more comprehensive view of their data. A study by IDC estimates that organizations that use AI for data integration can expect a 50% reduction in data integration errors.
In conclusion, AI is playing an increasingly important role in data management, helping organizations to obtain, extract, and structure data, locate data, improve the quality of their data, keep their data safe, and integrate data from multiple sources. As organizations continue to generate and collect more data, the use of AI in data management will become even more critical, allowing them to extract more value from their data and make better-informed decisions.
