Entdecken Sie, wie DHL Group die Konnektivität im gesamten Unternehmen verbessert und mehrere Prozesse durchgängig automatisiert hat
“By 2027, the application of GenAI will accelerate time to value of data and analytics (D&A) governance and master data management (MDM) programs by 40%,” reports Gartner.
GenAI is fast becoming the tool of choice to aid digital work, especially when handling data and analytics, like MDM. But it can be hard to know how to properly apply it.
“Generative AI capabilities are now becoming part of MDM solutions,” says Gartner’s Helen Grimster. “To avoid implementing GenAI prematurely, data and analytics leaders must understand the master data use cases it can enhance and solve.” Gartner has listed the top 10 use cases for GenAI in MDM, to help better understand where it can improve handling your master data. We have run down five of the use cases in this blog. You can download the full report to find out the rest of the countdown.
“Organizations can use GenAI to streamline ingestion and onboarding of master data,” reports Gartner. This helps to achieve quicker time to value, increases efficiency and can help uncover new domains. GenAI can enhance areas including: • Attribute identification • Entity discovery • Automated data ingestion
GenAI aids attribute identification by automatically identifying and categorizing fields in data sources, like recognizing words for a product category and sorting them appropriately. It can also identify individual fields to determine the MDM domain for entity discovery, such as recognizing a product domain based on an SKU or a name, or recognize a company based on a zip code. The acquisition stage can also be accelerated by automated data ingestion. For example, uploading large product inventories in bulk, would require lots of time and effort, with a high chance of error when entering data.
“Data modeling is pivotal in MDM”, according to Gartner. “It provides the framework for creating a centralized MDM hub that applications and analytical data stores use to deliver a curated, singular source of truth.” Data modeling provides scalability, as well as ensuring accuracy, and helps align business strategy. GenAI can assist with data modeling with: • Schema matching • Recommendations • Automated MDM models
Schema matching helps organizations produce coherent and aligned data models across disparate data sources and apps, such as SAP, Microsoft Dynamics 365, or Salesforce. Additionally, Gen AI algorithms can suggest which attributes and structures data models should use based on schema matching. Automated MDM models can catalog models during discovery. This helps with matching an aligning attribute and minimizes the risk of error that occurs when this is performed manually. Data modeling can take a long time, so automating the process is a great benefit.
“Data quality is foundational to any successful master data program. The quality of master data directly influences the credibility, accuracy, and the use of the insights derived from the data and operational functionalities that the data supports,” says Gartner. GenAI can use automation to ensure data accuracy, completeness, and consistency thanks to abilities such as: • Rule recommendations • Dashboards • Smart fields
Rule recommendations associate data quality rules with the relevant master data fields, while dashboards help display data quality results in a digestible way. Form fill can also be made easier with smart fields that verify the information by being context-aware for information such as addresses, phone number,s and emails to standardize and verify data.
“Master data tools have sometimes been criticized for being difficult to use or to configure. Organizations can use GenAI in a number of ways to help improve user experience (UX),” says Gartner. This helps to make data accessible and uncovers insights. • Assistants • Co-pilot services • GenAI-personalized UX
Prompt-based UI assistants allow users to access data by searching across your documentation to give personalized help to users, such as how to perform actions within the app. This reduces training time and increases user adoption. Co-pilot services provide a conversational interface to simplify analysis of master datasets and help uncover opportunities. GenAI-personalized UX allows users to create their own dashboard layouts, based on their relevant datasets, as well as offering suggestions to improve the way they perform tasks.
“Governance of master data requires insights to bring together organizing policies, processes, and standards that determine how data is utilized, treated, and shared within an enterprise,” says Gartner. GenAI can augment and automate governance by combining data quality, management, and policy enforcement. This creates a holistic view of data to help derive intelligence and improve efficiency. • Automating associations • Insights from stewardship activity • Insights from unstructured data
GenAI can link business glossary definitions, policies, and data owners to master data, which helps to significantly improve productivity and accuracy. This will help drive cross-functional collaboration across different teams within an organization. It can also derive insights from stewardship activity and unstructured data by analyzing how stewards across the organization respond to master data issues. GenAI can then learn from how stewards respond and offer up these solutions for similar issues. With unstructured data, GenAI can help to interpret the information and turn it into valuable insights. This helps ensure consistent policies and definitions across data domains enterprise-wide.
Combining GenAI with your MDM solution can help solve multiple business challenges. It can assist with maintaining data integrity while providing a flexible foundation with the potential for scalability.
Download the full Gartner report for a complete rundown of the top 10 use cases for generative Ai in master data management.
