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How to turn your data collection into data insights with AI?

Product Manager

Companies gain more and more information by collecting data. But simply collecting data is not a guarantee for success. The challenge is to transform collected data into data insights. The good news: artificial intelligence (AI) and machine learning (ML), in terms of learning from data and automating repetitive tasks, can support you in your data management activities.

Artificial intelligence (AI) and machine learning (ML) are introducing new ways of creating business value and will fundamentally impact and change the Data Value Chain and the way data assets are managed.

Experts see a significant potential of AI for replacing manual, error-prone data management practices, while at the same time improving data quality, data use and thereby business value. For example, Bosch has been able to almost completely automate the manual process of commodity code assignment in product master data creation with the help of machine learning. With this approach, Bosch can fulfill the increasing demand for this assignment task across the enterprise with a scalable solution.

To identify areas in which AI is used to improve data quality, 44 use cases have been collected and analyzed. The bottom line of this analysis is: ML can support data management and improve data quality across the entire Data Value Chain.

AI/ Machine Learning can achieve success for your business with:

Data creation and enrichment

  • AI can be used to automatically prefill forms in application systems with values known from past data entries, thereby preventing invalid or wrong entries.
  • AI can extract data objects from unstructured data, thereby converting data from one format into another (especially when it comes to transforming unstructured data into structured data). For instance, product or customer information can be extracted from e-mails or photos.
  • AI can automatically generate and enrich existing data. For instance, machines can learn the content, wording, and writing style of previous product descriptions in order to automatically generate descriptions for new products.

Data integration and maintenance

  • AI can guide the data cleaning process by identifying outliers and predicting potential demand for data repair based on already corrected data. In this scenario, the user validates each correction, while the ML system simultaneously optimizes its correction strategy.
  • An AI-assisted system can learn how data attributes associate with each other from transactional data for extracting data creation rules. Using this approach, existing business rules can be validated and extended.
  • Novel approaches based on AI can support data integration processes by interpreting semantics of schemas and ontologies.

Data protection and retirement

  • An AI system can learn to identify individual attributes of PII in order to distinguish between sensitive and non-sensitive data.
  • AI can be leveraged to detect suspicious user behavior, like unauthorized data access or creation of data leaks. In this scenario, an AI system can learn data access patterns to identify outliers that indicate fraudulent behavior.

Data discovery and data use

  • An AI system can learn from previous data use contexts in order to recommend data to users (in a data catalog, for instance) in a similar way Amazon does it for the products offered.
  • An AI system can cluster selected metadata attributes, thereby improving the process of selecting relevant data.
  • AI can link datasets as they are being used by interpreting the semantics of dataset schemas and their content.

This shows how real value can be derived out of support and usage of AI technologies.


Interested to learn how AI can create business value for you?

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