Classifying data with AI: Precise analyses through intelligent ordering
Classifying data plays a central role in a variety of processes. Assigning data to a class is often a prerequisite for precise forecasts, such as the assignment of articles to product groups. The MDM Booster supports you in automating this task using artificial intelligence.
- Automatic assignment: Assign messages, articles or requests using AI.
- High accuracy: Reduce errors and complete your data
- Scalability: Efficiently and automatically process large amounts of data.
- Increased security: Automatically identify and protect sensitive data.
- Compliant: Facilitates adherence to GDPR and compliance requirements.
Classify data: Grouping and structuring data in a targeted manner
Data classification using artificial intelligence (AI) is an automated process in which data or objects are categorized. Automatically assign your data with your individual AI models with high accuracy and in real time. For example, materials, malfunctions, or inquiries can be automatically assigned to appropriate groups, such as product groups, employee groups, or sales areas.
Unstructured data makes it difficult to maintain an overview and costs valuable resources
Digitalization offers companies enormous opportunities. For automated processes, data is often needed in a structured form (in fields). However, experience shows that the data is often stored in a structured manner not neatly arranged, but often Unstructured and in different formats. This situation can pose a significant challenge for traditional systems and people.
Master Data Management (MDM) systems already significantly minimize the effort required, for example, for data classification. Artificial intelligence can make a valuable contribution to further reducing workload and optimization, as well as relieving the burden on your experts. The knowledge of your experts can be permanently secured and made available to the organization.
Your benefits when you classify data
- Minimal effort: No need for manual searches and assignments in unstructured data or maintenance of mappings and rules.
- Optimized processes: Properly assigned data is the basis for a large number of process optimizations.
- Risk minimization: Many regulatory requirements demand classification and allocation of data, such as dual use, customs tariff numbers or default risks.
Practical example: classifying data: Automatic assignment of IT faults to employee groups or solutions
A company receives hundreds of tickets every day via a ticket system for customer support. These tickets relate to a variety of different requests and faults, such as
Software problems (error messages in applications)
Hardware problems (defective laptops, printers)
Access problems (forgotten passwords, missing permissions)
The existing semi-automated analysis and assignment process, performed by employees or rules, is now supported by a custom-trained AI model. This AI model was able to correctly assign and process more than 98% of the queries.
This is how it works:
- Training: The AI model is trained with historical tickets and the respective assignments / solutions.
- Automatic assignment: New tickets are classified in real time. The AI model interprets the ticket information, responds automatically to non-critical inquiries or forwards the ticket to the responsible employee group (e.g. software team, hardware support).
This principle of classification is used in many areas, such as the categorization of e-mails (spam detection), the sorting of products in online stores or the analysis of medical data.
The advantages
- More efficient processing of tickets and requests
- Relieving experienced IT employees of routine tasks and supporting new IT employees
- Reduction of errors
- Greater customer satisfaction thanks to fast solutions
- 24*7 processing of requests
Typical examples of data classification
Field of application: Sales, marketing, customer service
Customer data is essential for personalized offers, targeted advertising campaigns and efficient customer care. Fine-grained classification enables companies to assess their customers more quickly and make better offers.
Examples: Classification into new customers, existing customers and VIP customers Categorization according to demographic characteristics (age, location, industry)
Area of application: Purchasing, warehouse management, production
Material master data contains important information on products and materials. Standardized classification facilitates inventory management, reduces errors in production and improves traceability.
Examples:
Classification according to product groups (e.g. raw materials, semi-finished products, finished products) Standardized allocation according to ECLASS or UNSPSC for smooth data exchange. Automated identification of substitutes to optimize warehousing.
Field of application: procurement, supplier management, quality control
A systematic classification of supplier data helps companies to manage their suppliers efficiently, minimize risks and build reliable partnerships.
Examples:
Classification into strategic, operational or one-off suppliers
Evaluation according to quality, delivery times and reliability.
Field of application: Financial accounting, controlling, accounting
Accounting data must be structured precisely and comprehensibly in order to ensure smooth processing of invoices, cost centers and financial transactions.
Examples:
Automatic categorization of income and expenses by cost type.
Assignment of invoices to projects or departments.
Recognition of mandatory tax fields (e.g. VAT ID, invoice numbers).
Field of application: personnel management, payroll accounting, recruiting
Personnel data contains sensitive information that needs to be well structured and protected. Targeted classification makes it easier to manage and comply with legal requirements.
Examples:
Classification according to employment status (full-time, part-time, working student, freelancer).
Categorization according to qualifications and training certificates.
Field of application: Product development, quality management, compliance
Technical documentation contains important information on products, machines or processes. Structured classification facilitates management and quick access to relevant data.
Examples:
Automatic categorization of manuals, specifications and maintenance instructions.
Categorization according to product versions or standards (e.g. ISO 9001, CE marking).
Linking with material master data to simplify production processes.
MDM Booster
AI-supported classification for precise master data
The MDM Booster enables automated and precise classification of master data across different systems. With the help of artificial intelligence (AI), the system recognizes patterns, structures and correlations in order to classify data quickly and accurately into the correct categories.
Test 7 days for freeAutomatically classify data with AI
The MDM Booster classifies data from different sources and systems fully automatically using artificial intelligence. Advantages:
✔ Errors in assignments and classification are minimized
✔ New data is automatically assigned
✔ The need for manual maintenance or the use of rules and mappings is minimized.
Seamless integration into existing systems
Thanks to open interfaces, the MDM Booster can be easily integrated into existing MDM, ERP, PIM or CRM systems. Standards and standard formats such as SQL, CSV, Excel, OpenAPI and S3 are supported in a variety of ways.
Individual AI models for customized classification
The MDM Booster makes it possible to train AI models specifically for company-specific requirements – without AI expertise.
With the AI-supported classification of the MDM Booster, you save time, improve data quality and automate your data organization sustainably.
Classify further use cases in the context of data
Churn prevention
Analyze sales-relevant information with your own AI models and predict potential customer churn in order to take early countermeasures.
Sales strategies
An important prerequisite for the precise preparation of your sales strategies is a classification of the various operational and strategic tasks and their influence on target achievement.
Fraud detection
With mass data, such as in the banking sector with credit cards, fraud detection using AI is relatively easy. The MDM Booster supports you in detecting money laundering incidents, fraudulent orders or invalid documents and a relatively small number of transactions by training individual AI models.
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Get to know the MDM Booster in the context of classification. The MDM Booster provides companies with powerful AI software that can be used to automatically analyze data and efficiently implement tasks such as classification.
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