What good is a decision if it is not well-founded? And who gets the blame for an unsound business decision? How deep do you dig to find the actual error? Quite often, processes are blamed. We must dive into them to figure out who is doing what, from A to Z, and find the weak link in the chain.
Why it all depends on perfect master data maintenance
Cleaning up processes has always been a good idea, but many companies overlook the fact that errors most often occur because the master data that is key to a company’s central nervous system is faulty, poor quality, and siloed. This makes it incredibly difficult to work with this data and to use it to create an accurate picture of reality.
Simply put, master data causes difficulties when it is entered manually. The business system of the average midsize company has thousands of customer data points and even more in parts lists, prices, delivery times, supplier details, and product contents – all of which are entered manually.
As the Aberdeen Group’s 2013 analysis of master data maintenance showed, error rates are high (Source: Aberdeen Group, Bridging the gap to best-in-class performance, April 2013, Nathaniel Rowe). Overall data quality varies significantly from company to company depending on the level of master data errors and the company’s ability to register and update master data correctly. Interestingly, a company’s ability to retain customers and ensure customer satisfaction is related to the quality of its data: High data quality results in higher customer retention and satisfaction rates.
Among the companies in the analysis, 20% had high data quality, 30% had poor data quality, and 50% were somewhere in between. In the top 20% of companies, only 6% to 8% of data was incorrectly categorized, while in the bottom 30%, that figure rose to 39%. Meanwhile, 8% of the data in the top 20 was incorrect, compared to 35% in the bottom 30. Finally, it took 1.5 hours to correct an error in the top-ranking 20% of companies, but 55 hours in the bottom 30.
Even though the analysis is from 2013, the numbers are completely consistent with our experience at the global level.
Growing challenges of master data maintenance
Master data is part of the foundation upon which management bases decisions. Since requirements for business data, reporting, and documentation have increased in the past few years, incorrectly recorded master data, lack of master data maintenance, and the storage of master data across too many systems create enormous challenges.
In addition to this, more and more companies – including smaller and midsize enterprises – are beginning to use data analytics to process data better, real-time insights faster, and understand trends and future opportunities. If up to 35% of the data is incorrect, data analytics are misleading, and people on the production floor and in the boardroom are working with the wrong numbers.
Don’t allow your master data to live a life of its own
What do companies with poor master data accuracy do when they need to scale or buy a business? What happens when data needs to be harmonized across the organization? We have reached a point where companies can no longer allow incorrect master data. Nor can they allow master data to live a life of its own within the business system. Asynchronous data across silos and lack of data integrity increases the risks of using the wrong data and the inability to document master data maintenance with the EU’s GDPR in force. Both customers and the organization grow impatient when the wrong data is used and when we need to spend a lot of time qualifying it.
What should be done? Just simplify master data maintenance?
Indeed, this is one third of the rent. In addition, managers must put their trust in automation and central governance: three concepts that together define the best, fastest, and safest way to handle master data today and in the future.
- Automation eliminates up to 99% of all manual entries, thus improving data accuracy significantly. If company employees previously typed in 3,000 values per material – including stock value, production time, weight, content in several languages, etc. – then automation can reduce manual entries to just 30 values. The rest are registered automatically.
- Central data governance establishes rules for master data, including where and how it should be stored. This supports company processes. Master data must be recorded only once and in only one place so that users can easily work with numbers across systems and modules. It is advantageous to make country-specific legal requirements and preconfigured workflows an integral part of the solution.
- Simplification is essentially about making it easier for users to work with master data and to care for master data maintenance. It must be easy for users to change data governance if the company’s processes change. It must also be easy to find the right data and make business decisions quickly on a daily basis. This demands a solution that is both rigorous and flexible.
Creating fast added value through simplified data maintenance
With our solution it.master data management – it.mds – we addressed potential gaps between our customers’ requirements and SAP’s flexible data model. it.mds simplifies a series of internal working processes by managing, consolidating, and harmonizing all master data in a single, central integrated solution – quickly and cost-effectively.
Sign up for our free webinar: Getting Started with Reliable, High-Quality Master Data – Live Demo and Q & A Session, Tuesday May 14, 2019, 3:00 PM – 4:00 PM (CEST).
This article originally appeared on itelligence.com and is republished by permission. itelligence is an SAP platinum partner.