A data sharing vision: use cases

Every use case has its own destination. However, we can all take learning out of other use cases to accomplish similar goals. 

Data sharing - not for everyone?

Sharing one's data asset with others, even with competitors, might sound weird - skepticism is a common first reaction to the concept of data sharing. With data sharing, companies share data updates via a trusted network where efforts are shared on business partner data such as addresses, tax numbers and even info on social compliance or financial stability. In this blog, I address some of the success cases of companies benefitting from a data sharing approach. Intrigued? Read the full use cases via the read more links. 

From data management to data sharing at the Schaeffler Group

Company-wide managed, and commonly used data is the foundation for a successful
digital transformation. Integrated high end data-quality facilitates industry 4.0, advanced analytics, efficient end-to-end processes, digital business models, smart products and machine learning. The global automobile and industrial supplier Schaeffler recognized this potential early on and started managing its data.

With this knowledge, Schaeffler started focusing on the creation of an internal data marketplace where employees can access all required data. In addition, Schaeffler began using the so-called data shareconomy. As a member of the data sharing community, Schaeffler implemented a data sharing solution for its customer and vendor domains. As there are significant overlaps of business partner data maintained by different companies, even across sectors, community members share quality-assured data in a legally compliant manner over a cloud platform. Hereby creating a win-win situation.

Read on how Schaeffler gained advantage with cost reduction and increased efficiency

Automated decision-making at Bayer Group

Bayer's good practice presents a forward-looking approach to automate master data workflows and manage the trade-offs between data quality, risks and manual efforts in vendor master data management.
Bayer's data quality framework builds on a semantic knowledge graph and more than 1,500 Data Quality Rules, defined in the Data Sharing Community, and combines them with Bayer-specific rules. These executable business rules and the validation with external data allow an instant-risk-based approval of master data requests instead of 24h service levels.

The initial pilot scope has achieved a considerable automation rate as well as several secondary benefits in the area of data quality and documentation of data-related knowledge. 

Visit the Bayer presentation

 

Evonik's journey to a touchless First-Time-Right data lifecycle process

Evonik has actively managed the domain 'business partner' for over 10 years. During this time, the maturity of this data domain developed successfully.

With changes in their enterprise strategy and the vision to become a data-driven company, Evonik needed to reassess its technology capabilities and processes, to meet the requirements for digitization, automation, and augmented data analytics. Within this vision, Evonik developed a touchless first-time-right data life cycle process by using external and internal data in a smart way.

A new 'business partner smart app' was developed and is now the central platform for Evonik's business partner data lifecycles. 

With these first innovative steps, the first results already show an enormous increase of business value. On its journey, Evonik had to overcome various challenges of which trust was one of the main challenges. Company-wide trust needed to be built that data could be maintained automatically and in a collaborative way using external data.

Read the full success story

 

Calculate your data sharing business case 

Curious what data sharing can do for you? With data sharing you increase data quality at lower costs. Data Sharing beats manual data maintenance both in efficiency and quality while lowering costs by up to 40%! Use the data sharing business calculator via the link (below).


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