Digitization

Success Through Reliable Master Data

Michael Wilp02/24/2021

Why is reliable master data important in process manufacturing? And what can I do with it anyway? Master data management allows you to use the right data at the right time for the right person through the right channel. First, a few words about the current situation.

Situation in the Process Industry

Companies are stepping up their efforts in process manufacturing in areas such as innovation capability, digitalization and sustainability in order to protect themselves against economic fluctuations or adverse political conditions—such as trade conflicts. Manufacturers are increasingly finding themselves in an environment that is difficult to predict and are trying to operate in the here-and-now as far as possible, because the order situation is sometimes only foreseeable for a few weeks.

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Attractive to My Target Group

Development of individual solutions and more sustainable products focuses on customers’ specific requirements. Such strategies are well supported by analysis of big data. The logical consequence in the next step would be to use artificial intelligence to open up opportunities to find substances with new or better properties. But back to master data management.

Ideally, an ERP system supports a large part of the company’s business processes. In this way, the ERP system manages the master data; this results in smooth data exchange throughout the company and reliable analyses. Nevertheless, master data management does not end with introducing ERP software.

I Can Trust My Data

Rather, the challenge is to ensure a universally valid data set that is reliable and not kept redundant. This calls for cooperation from all organizational units involved in the business process. This can ensure an organization’s master data assets remains uniform, accurate, consistent, and accountable.

Master data management remains a complex challenge, and means ensuring the data remains reliable at all times. Even though ERP systems provide clear data models, the challenge is less technology-driven and more dependent on the requirements of the respective business process. This includes all business processes across the entire organization. At the same time, secure data storage must be guaranteed.

Master data management is always required when more than two business processes use the same master data.

For example, productivity increases when employees spend less time on documentation and can use existing experimental data to make faster decisions. In the absence of a cross-departmental solution, valuable time is lost. In this scenario, information must first be found, or possibly merged from locally-held spreadsheets.

In fact, a considerable proportion of the work steps could be avoided if data were managed in a comprehensive and non-redundant way. The more information is available to all parties involved throughout the entire life cycle of a product, right up to the customer, the greater the potential for further digitalization.

Automation of My Repetitive Tasks

Starting with cross-organizational data management, the next step leads to digitalization of processes and automation of routine activities.

This poses the following challenges:

  • Managing and networking science-based innovation processes and data across the product lifecycle
  • Compiling enterprise-wide information to reduce cycle times and time to market
  • Streamlining data access and reporting across the enterprise for knowledge-based and rapid decision making
  • Promoting internal and external information provision to facilitate collaboration
  • Sharing consistent data all in one place
  • Optimizing the development process (from research to QA/QC to marketing)

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Author:
Michael Wilp
Product Marketing Manager | COSMO CONSULT