Data quality and data quality management have been an issue for as long as there has been data. Its importance grows the more data is produced. Master data forms the spinal cord of business processes. It is therefore essential to have a constantly up-to-date and reliable master data basis for the entire company, a "single point of truth". To achieve this, multi-domain solutions are the method of choice today.
Poor data quality is increasingly becoming a risk factor. How can this be illustrated? Let's take the example of KPMG. In the past, auditors used to get a picture of a company's processes through interviews with employees. Today, they ask for data records, the analysis of which often reveals organizational deficiencies. Christian Ledulé, Director IT Advisory at KPMG, made this clear. He says:
"The truth lies in the data - if it is properly maintained."
Christian Ledulé Director, IT Advisory at KPMG
He is right! Data is becoming increasingly important; at the same time, the importance of data quality is growing, because without reliable data quality, analyses based on this data are nothing more than waste. The background to this development is the rise of "big data", i.e. the handling of large, complex and rapidly changing volumes of data.
This development is being accelerated by the "Internet of Things". The number of networked intelligent objects is doubling every five years: While there were 12.5 billion worldwide in 2010, the figure is expected to be 50 billion by 2020, says Cisco.
As a result, the degree of digitalization in value chains will increase rapidly in the future. It is no wonder that 90% of all companies across all industries are convinced that the ability to efficiently analyze and effectively use large amounts of data will be crucial to the success of their business model in the future. The main problem here is data quality. No matter how much data there is, it is worthless without recognizing correlations, meanings and patterns. However, the use of processing and analysis methods can only generate monetary value from data if it is "right". Evaluations or planning are prone to error if they are not based on complete, clear and "correct" data.
If data quality is so important, what is the current situation here? 95 percent of companies report the long-term consequences of poor data quality. These consequences are:
- increased operating costs,
- the impairment of employee productivity and efficiency,
- lack of agility,
- slow processes,
- the loss of competitiveness and
- increasing customer dissatisfaction.
Data quality in master data management has a massive impact on processes along the value chain. The measurability, control and improvement of data quality must therefore be ensured.
The goal of creating a single point of truth that is valid across all data is therefore becoming even more important in view of developments such as big data and Industry 4.0. And what are companies doing?
Nevertheless, many of them work without a professional solution for the management of master data, the backbone of their business processes. It is not uncommon for large companies to make do with home-made solutions. In doing so, they avoid the task of adjusting their business processes and establishing rules for handling data. However, this requires professional master data management: clear guidelines on which data is maintained where and how, who approves, etc. In addition, those responsible must also be accountable for the accuracy and completeness of the data. However, as long as there is no awareness of the need for changes in business processes and responsibilities, many companies see no reason to spend money on a master data management solution. They cannot avoid one thing: professional master data management costs money. Poor data quality and non-existent or "self-made" master data management cost even more money!
Let's summarize the advantages of a master data management solution once again:
What developments are currently taking place in master data management?
The trend towards multi-domain solutions for master data management is continuing. Companies are faced with the task of creating an integrated overview of all customer, product, supplier and other master data (such as reference data), which is, however, stored in different data silos and various company divisions. The IAO study makes it clear that if, on the other hand, processes, data schema and data quality rules for both the customer and product data domains are administered by the same team in an organizationally integrated master data management system, it can be obvious and more efficient in the long term to do this in the same information architecture and, for example, in the same central multi-domain system. Due to the multitude of today's data sources and the rapidly increasing volume of data, the requirements for the management of master data in different domains have also become so similar that they can be maintained by a master data team via a central multi-domain system.
What are the specific advantages of a multi-domain solution?
In addition to integrated company-wide data management, multi-domain MDM systems with data quality rules and lifecycle processes can also support data governance, i.e. uniform and binding framework conditions, workflows and responsibilities for handling, maintaining and distributing data. When using various single-domain data silos, it is naturally difficult to ensure company-wide compliance with defined standards. If, on the other hand, there is only one source for master data, users have considerably less autonomy in developing definitions and rules for data, as the cross-domain data architecture is binding and transparent. The result: effective governance principles and cross-functional collaboration between departments. Both together lead to greater process efficiency and better resource allocation.
In addition to supporting data governance through multi-domain MDM systems, clean master data leads to improvements in many core operational areas of a company, such as marketing, sales, finance or field service. All relevant data from purchasing to sales converge in a central system. By opting for a multi-domain system, companies are investing in just one solution. It not only works for customer or product data, but can also be used for all other master data categories (including reference data). There is no need to maintain numerous data silos. This saves costs, not only when purchasing, but also during ongoing operation.
Conclusion
Companies need to recognize that big data analytics is now a powerful addition to the already known drivers for master data quality, such as compliance requirements, a 360-degree view of the customer or the creation of a "single point of truth". Effective data quality and master data management is therefore absolutely essential in the context of big data. This is the only way to ensure data quality and to identify and utilize structures in big data. Only professional master data management provides the basis for generating "good" information from "good" data and then generating reliable knowledge with the help of effective analytics. This is and remains the prerequisite for sound business decisions
