Data Sharing Business Case Calculator

Calculate Your Direct Benefits From Joining the CDQ Data Sharing Community

According to Six Sigma (rule of ten), indirect benefits by higher Data Quality exceed the direct benefits by 10 times. However, benefits of process failure reduction, good decisions, or more accurate business planning are hard to quantify and influenced by many factors. To quantify (a part of) the value of Data Sharing, we focus on the direct benefits, basically cost reduction due to more efficient data maintenance.

Use our Business Case Calculator to determine your cost saving potential in data creation/maintenance when using our data sharing services.


Data Purchase & IT
Record Updates
Record Creation
Savings

120000
50
180
0.43

0.05
18
30

0.11
5
20

120000
50000
Number of Records
Costs data maintenance per hour
Data maintenance per attribute (seconds)
Costs per attribute
Overlap with Data Sharing Pool

Lead time reduction of data creation processes, calculated by the difference between data creation costs with and without Data Sharing. With Data Sharing, data maintenance is faster because data can be copied from the Data Sharing pool instead of manual data entry. However, this effect is only calculated for records which match with the data pool, according to the overlap parameter. First Time Right ensures correct data at data entry and reduced efforts of later data correction and, even worse, process failures due to wrong data.

Created records ratio p.a.
Number of created records p.a.
Number of attributes per create
Number of created attributes p.a.
Duration (seconds) per attribute create with Data Sharing
Costs per attribute create with Data Sharing
Data creation costs p.a.
Data creation cost p.a. with Data Sharing

The idea of Zero Maintenance refers to the elimination of maintenance efforts. True zero data maintenance will be hardly achieved, but there are ways to get close to this goal, e.g. proactive and pre-validated updates from a data sharing community.

Updated records ratio p.a.
Number of updates records p.a.
Number of attributes per update
Number of updated attributes p.a.
Duraton (seconds) per attribute update with Data Sharing
Costs per attribute update with Data Sharing
Data update costs p.a.
Data update costs p.a. with Data Sharing
Data purchase and IT costs p.a.
Data purchase and IT costs p.a. with Data Sharing
Data quality costs p.a.
Data quality costs p.a. with Data Sharing
Cost Reduction by Data Sharing

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Article: Data Sharing Benefits

In the current article Data Sharing - the best way to better data, our expert Dr. Kai Hüner explains the direct and indirect benefits of the Sharing Economy approach in data management.

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How did we calculate this numbers?

The calculations in our business case calculator are based on a professional article on "Data Sharing in Business Ecosystems", which will be published in a renowned business journal at the end of the current year. Authors of the article are Prof. Dr. Christine Legner (Academic Director of the Competence Center Corporate Data Quality (CC CDQ) and Professor for Business Informatics at the Faculty of Economics of the University of Lausanne in Switzerland) together with Dr. Kai Hüner (Chief Technology Officer of CDQ AG), Dr. Simon Schlosser (Head of Product of CDQ AG) and Dr. Dimitrios Gizanis (Chief Executive Officer of CDQ AG).

In this article the authors discuss how the principles of the Sharing Economy can be applied to data and show the direct and indirect benefits of Data Sharing: The collaborative management of data reduces the data life cycle costs, i.e. the effort for data creation and maintenance as well as the costs of the quality assurance infrastructure. The improved data quality has positive, indirect benefits on all areas of the company that work with business partners, i.e., primarily purchasing, marketing, and sales.

In connection with direct benefits, the authors speak of the following savings effects: "First Time Right", "Zero Maintenance" and "IT Pooling".

They have illustrated these using a sample calculation with empirical values. These are based on globally active companies that store large amounts of business partner data in their operational systems and standardize them globally. For their calculation, the authors assume approximately 190,000 data records, divided into 60,000 customer and 130,000 supplier data records. The authors use an overlap of 43 % between own data records and the data pool because, from experience, this is often between 20-60%. The authors refer to 3 minutes that are needed on average to maintain or create data (including research time) for each data field in the traditional way, and they calculate with 0,5 minutes using CDQ services.


How CDQ Business Partner Lookup Works

Watch this video to get a feeling how you can easily maintain your customer and vendor master data:

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Fast and

Without duplicates!

See how the web app works and how it functions integrated in a SAP system. Find out which successful companies already rely on CDQ Data Management services.

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Worried about the quality of your business partner master data? Find out how Data Quality as a Service (DQaaS) can boost the value of your most critical asset. Learn more on Data Quality as a Service

Success Story: Data management at Schaeffler Group

In an article recently published in the specialist magazine "Big Data Insider", Markus Rahm provides insights into data management at Schaeffler. And how it has evolved with the help of CDQ. Data Management at the Schaeffler Group
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