Why Data-Cleaning is so important for the Manufacturing Industry – Part II

importance of data cleaning part 2

Continuing forth from our previous blog post, let’s proceed on to some more problems that are associated with low-quality master data and why data cleaning hence, becomes so important for the manufacturing industry.


  • Spend Management: A lot of material items are ones that are infrequently replaced, purchased on a decentralized basis and bought from a broad range of suppliers, which results in inefficient purchasing processes. This means an increase in order fulfillment times, higher processing costs for transactions and administrative support that can be time-consuming. The lack of evenly distributed supplier data in systems results in poor inventory control and therefore lack of spend visibility.

  • Sourcing:
    Sourcing projects in general or shall we say the make-versus-buy decisions that are associated with the manufacturing industry, requires a wholesome view of the existing approved parts lists and also the approved vendor lists. In cases of bad material master data, these processes become difficult due to supplier proliferation, parts proliferation and also the failure of being able to leverage already existing contracts.

  • Decrease in Employee Productivity:
    When there is duplicate or obsolete inventory, it leads to an excess of material records. Due to lack of an effective search functionality, the problem is compounded further for material management software. Studies have shown that even in leading manufacturing firms, maintenance workers have to spend about 60% of their time in just identifying/searching for MRO parts. This means that most businesses could actually benefit from improvements, that could be made in their loss of wrench time. With a certain number of employees searching for the right part, their time, resource and expertise which is needed to find the parts that are needed to run a manufacturer’s plant/equipment, contributes directly to a decrease in worker productivity.

  • Difficulty in Searching and Location of Parts:
    Inadequate material data descriptions make it difficult for employees to find the right parts/supplies. This is especially problematic, as it leads to maverick purchasing, wherein purchases outside negotiated strategic contracts of the organization are made.

  • System Functionality Being Compromised:
    In an overarching sense, low-quality master data has multifaceted impacts. We’re not talking just about the time and resources, but also how the co-dependability of various platforms on data. Companies today use sophisticated business analysis tools that give data-led insights – branching from accurate reporting and decision making to prediction and projections. If there are inaccuracies in data, a minor chip can actually escalate into something so big that it might end up compromising all the hard work invested. Hence ranging from responsiveness to real-time integration, poor quality of data can really compromise an organization’s system functionality in myriad ways.

Thus, with so many issues in place, the need for clean data becomes indispensable in the following ways:

  • Clean data ensures that all the obsolete data is removed from your system, which in turn, reduces the storage costs.
  • Accurate data facilitates marketing campaigns that are effective and precise. Clean data, hence, can help you get a higher ROI for your marketing activities.
  • Accurate data, helps you understand the changes in behavior patterns of your intended customers
  • It will help you be in line with all the compliance policies that are concerned with data protection

Since manufacturing industries rely heavily on big data and use of algorithms to guide them through the essential decision-making processes – for reliable and accurate outcomes, it is imperative that the data should be clean. Being able to choose the best supplier and materials is quite dependent on the right interpretation of data and accuracy of analysis, in order to stay ahead of the competition.

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