Material Master Data: Quantity vs. Quality
When it comes to Material Master Data, or any form of data for that matter, we’re quick to assume that more is better. Though this assumption may be true in some cases, the reality is that more doesn’t always mean better. Instead, it can often mean just the opposite, only causing increased confusion, cost, and room for error.
So what is more important, data quantity or quality?
As Material Master Data Cleansing specialists, we may be slightly biased in arguing that quality is more important; however, quantity is a critical element of data quality. The challenge is to obtain just the right quantity of data (not too much and not too little) to enable maximum quality, usability, and efficiency. But how does a company determine what is “just the right quantity” and how is this perfect balance achieved?
The answer is Data Cleansing of course, but more specifically, the Standard Operating Procedure or “Rule Set” that must be developed to consistently structure data. The Standard Operating Procedure defines the Naming Convention, Descriptive Attributes, Abbreviations, Cleansing Policies, and Format that will be implemented to achieve data quality and consistency. Ultimately, the Standard Operating Procedure will define the optimal data quantity and quality to achieve maximum data usability.
Based on 27 years of industry expertise and project experience, IMA has developed and refined a proven Material Master Data Cleansing methodology and Standard Operating Procedure to achieve optimal data quantity AND quality. While the basic implementation process remains constant for each data cleansing initiative, the Standard Operating Procedure often differs slightly as it is tailored to each company based on the industry, enterprise system(s), end user requirements, and internal business processes.
As part of the Standard Operating Procedure, IMA provides a proprietary Noun-Modifier Dictionary, which acts as the foundation for structuring material master data. Based on industry best practices, the Noun-Modifier Dictionary houses approximately 2,200 Noun-Modifier pairs, each with an average 5-7 corresponding attributes (characteristics). The IMA Noun-Modifier Dictionary is designed to provide the most pertinent product information that may be required by maintenance and procurement personnel when searching, issuing, purchasing, receiving, and sourcing materials. For example, a Motor, Valve or Circuit Breaker could easily contain up to 20 attributes describing its every little detail, however, many of those attributes would be completely redundant, only causing unnecessary formatting challenges, opportunity for error, and confusion for end users. Hence, more data is not always better. For that reason, the IMA Noun-Modifier Dictionary ensures the most relevant and commonly used attributes are populated to describe each product. Furthermore, the IMA Standard Operating Procedure offers best practice policies, abbreviations, and formatting guidelines for each enterprise system.
For instance, SAP and JDE both present unique formatting challenges due to their strict character limitations on descriptions fields. Therefore, IMA provides a recommended best practice data format, which enables maximum search ability and system functionality. All elements of the Standard Operating Procedure may be customized and tailored as per customizer requirements. For example, you may wish to rearrange the order of attributes within your concatenated descriptions, or you may want to populate Part Number at the end of each description to improve user search ability.
In summary, before embarking on a data quality improvement initiative, ask yourself, your team, and the data experts what is the intended purpose of the data and which fields/information are truly critical to fulfill that purpose. Though you may think you’re doing yourself a favour by populating every single field your system has to offer and excessively enhancing the dataset with attribute information, you may in fact just be creating more inefficiency and cost at the end of the day. Keep in mind, data quantity and data quality are both extremely important elements of data usability. Optimize data quantity and quality in order to maximize data usability.
