Blog June 17, 2024

Data Considerations for Implementing Supply Chain ERP Software

By Daniel Wang
June 17th | 2 min read

Considering implementing supply chain ERP software? When it comes to big price tag investments such as an ERP, it is important for businesses to ensure their foundation is well established. In other words, their data should be streamlined, and data processes must be properly mapped out. Learn how to ensure data integrity and streamline operations with Daniel Wang in article below.


Can an ERP Handle my Data? 

Implementing an ERP is a pivotal moment for any organization. When done correctly, it streamlines operations, slashes redundant documentation, and integrates processes. However, this is difficult to get right. The efficacy of supply chain ERP software is heavily dependent on the quality and integrity of the data it processes. Ensuring data integrity is crucial to avoid delays, significant cleanup, and other unforeseen headaches. It is much easier to scrub the data during implementation than attempting to untangle errors after go-live. Before diving headfirst into an implementation, it’s important to assess where your data stands across four key aspects: completeness, structure, flow, and access.  

  1. Completeness

Data completeness is fundamental to streamlining operations – all master data points need to be collected and updated to meet requirements from upstream, immediate, and downstream processes. Failure to do so may result in missing key visibility and additional effort to manage inconsistent data points across multiple entries. When evaluating data completeness, ask yourself the following:

  • Data Collection 
    • Are my methods comprehensive and automated? 
    • Are there redundant hand-key steps? 
    • Have users been carefully trained? 
  • Validation 
    • Have I set up mechanisms to identify and correct missing data? 
    • Are there flags for incomplete entries? 
    • Are users able to submit incomplete data? 
  • Audits 
    • Do I have regular audits to verify key data is being collected? 
    • Are these organized into separate master datasets? 
    • How are these datasets being regularly maintained? 


  1. Structure

Data structure is crucial for the smooth operation of an ERP system. Data needs to be recorded consistently to enable leverage multiple use-cases. By establishing a consistent structure and setting up strong guardrails, you can protect the integrity of master datasets and avoid data fragmentation. When evaluating your data structure, consider the following: 

  • Standardization
    • Does the data recorded follow a clear and consistent standard? (ex. SKU number) 
    • Do all entries in this field record the same type of information? 
    • Are my methods comprehensive and automated? 
  • Enforcement
    • Is data being validated against existing standards before it’s saved? 
    • Do we leverage secondary datasets outside the system? (ex. paper-based ledgers) 
    • Does the data recorded follow a clear and consistent standard? (ex. SKU number) 
  • Normalization
    • Is my data organized in in clearly defined master datasets? 
    • Are master datasets being continuously updated? 
    • Is my data stored and linked in all required formats? (ex. internal SKU number vs vendor number) 
    • Is data being validated against existing standards before it’s saved? 


  1. Flow

To capture efficiency gains from an ERP, data must correctly flow to appropriate documents and dashboards. Poor flow generates redundant capture processes, increasing the chances of hand key errors. By carefully routing captured data during process mapping, you can significantly reduce unnecessary labor. When mapping out your data flows, consider the following: 

  • Source
    • Is existing information being pulled from a master dataset?
    • Will new data be captured at the endpoint?
    • If new information is captured, how will that be updated in the appropriate master dataset?
  • Destination
    • Who is the intended end-user at the destination?
    • Will the end-user be able to overwrite the existing dataset?
    • How will the information be used?
  • Format & Type
    • Does my data need to be transformed before arriving at the destination?
    • If my data is transformed, should the transformation be recorded in a master dataset?
    • How much data needs to be transmitted at once?
    • How often will data need to be transferred?


  1. Access

Controlling and providing access is crucial for maintaining data integrity and protecting efficiency gains. Failure to set permissions appropriately can lead to unintended data manipulation, suboptimal decisions, and security breaches. To protect your newly defined processes, you will need to consider the following: 

  • Access 
    • Are our permissions consistent across roles and functions? 
    • Can employees able to easily access all necessary information? 
    • Are employees able to access information at the right time? 
  • Controls 
    • Do we have strong mechanisms to authenticate users? 
    • Is our authorization process clearly defined? 
    • Do we regularly review permissions & access across all roles & functions? 
  • Traceability 
    • Do we log changes to access? 
    • Can we monitor user-driven changes to master data? 
    • How long are our activity logs stored for? 


Data integrity is the cornerstone of effective ERP implementation. To capture efficiency gains, your data needs to be complete, structured for multi-faceted use cases, flow to the right endpoints, and be accessible to the right functional owners. In the journey towards supply chain digital transformation, maintaining data integrity is not just a preparatory step—it’s a strategic imperative. 

Does this apply to you and your business? Find out more by reaching out to the LIDD team today.  

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