A data sharing vision: benefits of the Shareconomy in data

On a day-to-day business your company is probably having the same burden and making the same efforts in data management as another company B or C, for example with the KYC (Know Your Customer) requirements that are the same, at least for many use cases! Why not reduce effort and share the burden?

Data quality refers to the degree to which data meets the requirements of the business in terms of accuracy, completeness, and timeliness. It is essential that data is fit for use in order for business processes to run smoothly. For customer and vendor master data, fitness for use can be translated to "know your customer" and "know your vendor".

The accuracy of your master data representation of real-world customers and vendors can greatly impact procurement, marketing, sales, and logistic objectives.

• Maintaining up-to-date knowledge of the production locations of your vendors can help you predict delivery shortages and understand the impact of natural disasters or political unrest on your supply chain. Clean customer and vendor data integration is crucial to post-merger success, as it can prevent duplicate entries, diverse address formats, and outdated identifiers.
• Furthermore, monitoring social compliance can help you identify and replace non-compliant suppliers in your supply chain, and stay ahead of the press. It is also important to be aware of all global regulations, including sanctions and embargoes, and identify affected customers before customs authorities do.
• Finally, to avoid payment fraud, it is crucial to verify the effective bank accounts of your vendors and not transfer money to fraudsters. Sharing uncovered attacks with peers can help you fight back against payment fraud.

With Data Sharing, you can increase your data quality at lower costs. Curious to know savings potential with data sharing? You can see instant results that go a long way with our business case calculator.

Data sharing can reduce efforts in data management

Aside from few information like payment terms, most data attributes do not depend on a specific business relationship. Take for example legal names, addresses, tax numbers or information such as financial stability and social compliance that all refer to the real-world status of a company. This information can be classified into either right or wrong, independent from your companies' business relation to vendor X or customer Y.

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: Having a shared source of truth in a trusted data sharing community which manages business partner data as a shared asset.

Direct benefits of data sharing on data lifecycle costs

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.

Direct benefits of data-sharing are:

• First Time Right: Accurate and complete creation of new records, copy validated data from data pool, check and correct data at data entry.
• Zero maintenance: Monitor all records and identify data defects proactively, receive record updates proactively, validation of updates instead of research and manual data entry.
• IT & Data Pooling: Access open data and commercial data at 1 interface, pool operation efforts of data quality management tools, reduce data and software license costs.

Data Sharing effects quantified

To quantify direct benefits of Data Sharing, parameters regarding costs, complexity, and effort of your data management process must be quantified.

• Number of records: These are the number of customer and vendor records in your systems. Take the records which are effectively used in your processes and managed (i.e., not inactive) in your systems and do not care about legal entities, ship-to and bill-to addresses, or duplicates.
• Data maintenance costs: These entail personnel costs of your data workforce. This number may differ from region to region, here you should assume an average hourly rate – full costs, not salary only.
• Data maintenance duration: How long does it take to research correct addresses, legal names, or tax numbers to validate the information by authority websites, and to enter the 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 easily copy it. The Overlap is the coverage or 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: These are the costs of external reference data, data brokers, company profiles, etcetera, and also license fees, integration efforts and operation costs of tools and external services like tax number validation, duplicate matching, or address cleansing. Due to data validation by the Data Sharing Community and use of a common cloud platform, these costs can be reduced.

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%!

Calculate your business case