Anomaly detection is a critical machine learning tool that can drastically improve companies’ data management and information governance capabilities. Anomaly detection is the process of using algorithms or organized systems that can learn patterns and trends from historical data, and that, if used correctly, can identify differences in data sets in real-time. 🕵️♂️💻
Today, we are looking at 3️⃣ ways that companies using anomaly detection machine learning programs can significantly improve their information governance compliance and risk mitigation efforts. Let’s go! 🚀📈
1️⃣ Precise Data Mapping to Retention Schedules 🗺️📅
Anomaly detection machine learning tools can help an organization identify data outliers and inconsistencies within their vast repositories. This capability proves invaluable when aligning files with retention schedules, a crucial aspect of information governance. Here are 2️⃣ examples:
🏦 A bank automatically flags financial compliance files that are due to expire earlier or later than a set prescribed retention period (i.e., X years), so that it can know when to delete them. 💰❌
🍔 A multinational fast-food operator can quickly pinpoint outdated or redundant HR files, which allows them to conduct a streamlined data cleanup process and delete unnecessary and expired personal data. 🌍🍟
2️⃣ Improved Searchability and Data Discovery 🔍📌
Machine learning-based anomaly detection can significantly enhance searchability and data discovery by automatically tagging outliers, reducing data noise, and prioritizing relevant search results (even if a user’s search terms are not an exact match), making it easier for users to find and use relevant information within their data sets.
Here is an example: A firm can highlight atypical patterns in document metadata within discovery documents thereby locating crucial evidence faster and improving the overall efficiency of legal discovery. 📚⚖️
3️⃣ Standardized Naming Conventions 📜✅
Consistent naming conventions are fundamental to information governance success. Machine learning can help ensure that files adhere to these conventions. For example, a company can flag deviations from approved naming conventions in their promotional materials, thereby ensuring that their marketing collateral follows the same structure, facilitating better organization and retrieval. 📄👥
By using machine learning tools such as anomaly detection, companies can improve their data mapping, searchability, and naming conventions, streamline their data management processes, reduce compliance risks, and bolster overall operational efficiency. 📈🏆🌐
Let's go!
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