Save Time and Costs When Maintaining Business Partner Data Together
When data is accurate, complete, and up-to-date, data quality is fit for use, and business processes can run smoothly. In order for master data to be fit for use, you need to know your customer and know your vendor. The better your master data represents real-world customers and vendors, the better your procurement, marketing, sales, and logistic objectives are met.
Figure 2: With data sharing, the burden of maintenance can be spread over many shoulders, thus saving time and money
Data Sharing Can Reduce Data Management Efforts
If you (Company A) have the same customer as another company (Company B), the know your customer requirement is most likely the same for you and Company B. Attributes like a legal name, address, or tax number, don’t depend on a specific business relation. Other information such as financial stability and social compliance refers to the real-world status of a business partner and are independent from Company A or Company B.
The availability of an external reference (or truth) bears the potential to align, to collaborate, to share efforts of managing business partner data. This is what Data Sharing is about: A trusted network of companies who manage business partner data as a shared resource.
To better explain the value of Data Sharing, the chart in figure 3 illustrates the overall effect along the three key dimensions of data management: Time, quality, and effort. From a quality perspective, proactiveness can also be implemented with classical data management techniques like data quality metrics or data architecture. However, this involves a lot of effort. Data requirements must be collected continuously and globally, translated into data quality metrics, and implemented in dashboards and workflows. Existing business partners change their addresses, go out of business, or tax numbers expire, and all these changes need to be proactively identified.
With Data Sharing, you get the same results quicker, and with less effort: Continuous collection of data requirements, monitoring of business partner status, design of data quality metrics, and many more tasks are shared across the Data Sharing Community and reduce the burden for each company.
Data Sharing Effects Quantified
To quantify direct benefits of Data Sharing, parameters regarding costs, complexity, and effort of your data management process must be measured.
- Number of records: Number of customer and vendor records in your systems. Don't include legal entities, ship-to and bill-to addresses, or duplicates, just take the records which are effectively used in your processes and managed (i.e. not inactive) in your systems
- Data maintenance costs: Personnel costs of your data workforce. This number may differ from region to region, just assume an average hourly rate – full costs, not only salary
- Data maintenance duration: How long does it take to research correct addresses, legal names, or tax numbers? To validate the information by authority websites? To enter data manually? Our research shows 3 minutes on average per attribute
- Overlap with Data Sharing Pool: If you find an up-to-date and validated customer record in the data pool, you do not have to research, validate, and enter data manually. You can just copy it. The Overlap is the match rate you can expect. The average for the Shared Data Pool is 43%
- Created/updated record ratio: The expected number of created and updated records per year can roughly be derived from the overall number of records. Our statistics show 5% for customer data and 11% for vendor data
- Data purchase & IT costs: Refers to costs of external reference data, data brokers, company profiles, etc., and to license, integration, and operation costs of tools and external services like tax number validation, duplicate matching, or address cleansing. Due to data validation by Data Sharing Community and use of a common platform, these costs can be reduced
Figure 4: Business case for Data Sharing
The business case considers three direct benefits of Data Sharing:
- First Time Right effect: Lead time reduction of data creation processes, calculated by the difference between data creation costs with and without Data Sharing. Creation costs without Data Sharing are just calculated by the number of created records (5% of the overall number of records, see above), the number of maintained attributes per new record (18), duration of data maintenance per attribute (180 seconds), and personnel costs (50 CHF per hour). With Data Sharing, data maintenance is faster (30 seconds) 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 (43%)
- Zero Maintenance effect: Like the First Time Right effect, just for data update costs with updated records ratio (11%) and less attributes per updated record (5). The default assumption for maintenance duration per updated attribute (20 seconds) is lower than for record creation because pre-validated updates may come proactively from the Data Sharing community
- IT and Data Pooling effect: Straight forward, just the cost savings due to replaced data management tools (e.g. address cleansing, duplicate matching) and lower costs of external reference data. Again, the quantified Data Sharing effect is only due to reduction of data maintenance efforts. Indirect benefits due to higher data quality would exceed these numbers by 10 times (rule of ten, Six Sigma)
Data Sharing Cases
Size matters when it comes to the effect Data Sharing on data maintenance costs, size matters. Another important factor is the maturity of data management processes, i.e. the lead time of data record maintenance due to automation and software integration. The following three cases illustrate how the number of records and the maintenance duration per record affect First Time Right and Zero Maintenance.
A company from the Automotive sector has 70k customer records and 50k vendor records of business partners in 100+ countries. Data is maintained centrally in a shared service center in Eastern Europe.
For record creation, process specialists can use an external website to lookup business partner data in the shared data pool. If they find the business partner in the pool (match rate: 52%), they can manually copy the data to their workflow tool and save significant time, i.e. 60 seconds per attribute compared to 180 seconds for manual address research, tax number validation, etc.
For record updates, the same website is used, with the same benefits. However, due to lower update efforts in general (5 attributes versus 18 attributes, on average), Zero Maintenance effect is lower than First Time Right effect.
Data Sharing effect: CHF 120'640 (35%)
Figure 5: Case A, low integration depth but benefits from day 1
The company from Pharma sector has 280k customer records and 210k vendor records of business partners in 150+ countries. Data is maintained in a global follow-the-sun Shared Service Center at three locations in Southeast Asia, Eastern Europe, and Middle America.
The company has deeply integrated Data Sharing services into their workflow tools: Process specialists can search the data pool directly from their workflow, manually entered data (if not found in the pool) is automatically validated and cleansed, and updates from the Data Sharing community pop up in the workflow, all in the same tool.
Beside First-Time-Right and Zero-Maintenance, the company could also phase-out an existing tool for address cleansing and duplicate matching, and they could reduce costs of external reference data (see IT & Data Pooling effect).
Data Sharing effect: CHF 541'551 (41%)
Figure 6: Case B, deep integration and full-service use
The Machinery company has 60k customer records and 90k vendor records. 80% of their business partners are from European countries and the US with open data access to the national commercial registers. Hence, the overlap with the Data Sharing pool is quite high for this company, 72% overall. Data is managed by process experts within the business processes, supported by an end-to-end workflow, but without a central data management team (i.e. higher personnel costs, higher lead time per attribute).
For data maintenance, data pool lookup, address cleansing, tax number checks etc. are integrated in the workflow tool. However, updates for addresses and legal entity data are not yet fully integrated but downloaded from a website on a weekly basis.
Data Sharing effect: CHF 541'900 (62%)
Figure 6: Case C, high overlap with data pool
Conclusion and Further Information
Data Sharing is the best way to improve data. Quantifying the direct benefits of Data Sharing shows only a part, according to Six Sigma (rule of ten) only 10%, of the effective benefits. However, even this part should be enough to argue the investment benefits of joining a Data Sharing Community. In the Sharing Economy, benefits for each community member grow by the network effects if the community grows.
Dare to share!
You can calculate your own business case on our website: Data Sharing Business Case Calculator.
Data Sharing in Data ManagementShared quality rules, data sources and peer-validated records are three steps that allow you to approach data quality problems together with others, more effectively and with far less manual effort - because a problem shared is a problem halved! Data Sharing for better customer & vendor data
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