Sie haben Probleme mit der Genauigkeit der Datenklassifizierung. Welche Tools können Ihnen helfen, bessere Ergebnisse zu erzielen?

You can discover and classify data by using any AI or rule based tools. Accuracy can be improved by multiple iterations of training the tool on your data. Still in my experience it’s difficult to go close to 100% accuracy. So having SME’a to review and approve is essential.

Improving classification accuracy is an iterative process, relying not just on tool selection but also on a deep understanding of the data, continuous model tuning, and regular performance evaluation. Machine Learning process seems to be a viable solution. So, we can leverage Informatica's Axon integrates with Generative AI, Google's AutoML, DataRobot automates the process of data classification. Also, Creating labeled datasets is facilitated by data annotation tools like Prodigy and Labelbox, essential for supervised learning.

Data Discovery: Navigating complex Data landscapes In a large retail corporation, the data discovery process involves scanning numerous databases to identify and classify diverse data types, from customer transaction histories to inventory details. This example shows how data discovery tools are essential in managing vast data sets, helping organizations understand what data they possess and where it resides, crucial for effective data governance.

Data Protection Tools: Safeguarding against unauthorized access or malicious use is paramount. Data protection tools, including encryption, masking, and anonymization, play a crucial role in ensuring compliance with privacy and security regulations. Integrating these tools with data discovery and cataloging ensures consistent and correct data classification throughout its lifecycle. Data Governance Tools: Defining and overseeing data strategy, policies, and standards is achieved through data governance tools. These tools help establish and communicate data classification frameworks, ensuring alignment with organizational objectives. By coordination they ensure a cohesive data classification process.

Among their various benefits, Data Discovery tools are particularly adept at learning and adapting, ensuring data classification stays current. These tools use machine learning to refine their classification models as they encounter new data types and categories. This adaptability is vital in fast-evolving data environments, where definitions and regulations can change rapidly. Such tools automatically update classifications, aligning with internal policy changes and external regulatory shifts. This dynamic approach ensures ongoing compliance and relevance in data governance. This AI-enabled automation is especially critical in large enterprises where the volume and complexity of data can be overwhelming.

Data Catalogs: Organizing data for accessibility and insight A pharmaceutical company uses data catalog tools to maintain a centralized repository of research data. The tools help catalog various clinical trial results, making it easier for researchers to find and utilize specific datasets. This scenario highlights the role of data catalog tools in organizing and making data easily accessible, enhancing the ability to leverage data for critical decision-making.

Similar to data categorisation, data cataloging tools also benefit from the AI integration for creating the column or table description to better understand it's business and data definition.

Data Catalogs utilizing knowledge graphs significantly enhance active metadata management by revealing complex data relationships. These graphs, integrated with AI and machine learning, refine automated data classification by providing a richer, contextual understanding of data. Additionally, domain expert feedback on these knowledge graphs is crucial. Their insights help tailor AI-driven Data Discovery tools, ensuring they align with specific organizational needs. This approach boosts immediate classification accuracy and fosters a cycle of continuous learning and improvement, evolving AI models for smarter data management.

Data Catalog is a key process which connects the Business Users to understand the Technical information about the data in an easy way. Popular tools in the Catalogue is Alation, Collibra, Informatica Axon, InformaticaCloud Data Governance and Catalogue (CGDC), Informatica Enterprise Data Catalog (EDC).

Data Protection tools enhance data classification accuracy, especially under regulatory compliance like HIPAA in healthcare. Using advanced algorithms, they identify and classify sensitive data, such as personal health information, ensuring it's correctly categorized for protection. These tools automatically enforce access controls based on data sensitivity and stay updated with regulatory changes, maintaining ongoing compliance. They also enable risk assessments and generate compliance reports, which are crucial for audits. By integrating legal insights, they align classification with specific regulatory needs, bridging the gap between data management and compliance requirements.

Data Protection: Safeguarding valuable data assets A financial institution implements data protection tools to secure sensitive client information against cyber threats. These tools manage encryption, access controls, and monitor for unusual activity, demonstrating how data protection tools are vital in securing data from unauthorized access and maintaining trust.

The most impactful capabilities of Data Governance tools for data classification accuracy are Standardization of Classification Processes and Integration with Metadata Management. Standardization ensures uniformity across an organization, eliminating inconsistencies in data categorization. It sets clear, uniform rules, reducing subjective judgments and errors. In tandem, integrating metadata management provides detailed insights about data's origin, context, and content, crucial for informed classification. This synergy between standardized processes and enriched metadata understanding results in a consistent, accurate, and context aware classification system, essential for robust data governance.

Data Governance: Streamle compliance and quality management An international media company uses data governance tools to ensure compliance with various global data regulations and to maintain data quality across different regions. These tools help standardize data handling practices and track compliance, showcasing their importance in providing a framework for consistent and compliant data usage.

Data Governance plays a vital role in the Organization's key success. Popular DG Tools in the market is Collibra, Alation, Informatica AXON, Information Enterprise Data Catalog EDC, Informatica Cloud Data Governance and Catalogue

Selecting the right source for your usecase will increase the chances of your product to be successful. Understanding the quality of data you need, at different level along with its data quality scores at different parameters help to make the correct decision.

Governance and Master Data Management as well as AI management go hand-in-hand we all need trusted insights from information... how to attain it is a process. While there is no right or wrong the end results must build on following approach (1) define critical attributes in use particularly in MDM or an AI model (2) steward (assign an owner) these assets by keeping a close tab on how and where used... i.e. don't fall in the temptation of business wanting more attributes for reporting etc..when you can leverage and re-purpose current critical data elements (CDE) in your framework (3) "only" use these attributes in MDM/AI models while ensuring your data dictionary encompasses all KPI/metrics at work

With the rise in demand for data mesh and data products, the need for these tools for classifying, cataloging and discovery have become must as underlying service. If we want to reduce our efforts to recreate what we already have we need these tools.

Combining continuous Employee Education with Machine Learning and AI Enhancements in a rapidly changing data landscape forms a feedback loop that boosts data classification accuracy. Regular training keeps employees abreast of evolving data patterns and classification practices, enabling them to provide vital feedback and insights. This human input is essential for training and refining AI models, especially in handling nuanced data. As AI algorithms evolve, they automate complex classifications more effectively, lightening the manual load for employees. This synergy between informed human judgment and advanced AI leads to a self-improving system, continually adapting and enhancing classification accuracy in a dynamic data environment.

In addition to the four mentioned tools, various others enhance data classification accuracy: ML & AI-based Tools: Use algorithms for automated data classification based on patterns and historical data. Data Quality Tools: Ensure data accuracy and consistency, identifying and resolving inconsistencies, errors, and duplicates. Metadata Management Tools: Manage data metadata, offering additional context for accurate classification. DLP Tools: Implement policies preventing unauthorized access and data leakage, enhancing data protection. Collaboration & Workflow Tools: Facilitate team collaboration, streamlining workflows for improved data accuracy. Integration of diverse creates a comprehensive data classification strategy.