Garbage in garbage out is perhaps the most overused phrase of all during the ERP conversations. Everyone uses it, but very few understand the consequences and implications of this phrase. Also, your data, whether structured poorly or diligently, forms the foundation of your business model. But why? Because many data sets are relevant in the ERP conversations, such as master, configuration, transactional, and historical, they each have their implications on the selection and implementation of ERP. Suppose you implement or select an ERP without going through the phase of data cleansing or aligning your data with the future business model. In that case, you might end up choosing an incorrect ERP, as your poorly structured data may be influencing the decision for your ERP. The data cleansing could mean aligning your BOMs with your production process or your customer structure with how you transact with them. But how to follow the structured data cleaning approach to select the suitable ERP and avoid implementation disaster?
In today's episode, we invited a panel of cross-functional experts for a live interview on LinkedIn who brings significant expertise to discuss data cleansing prior to ERP implementation. We covered many grounds, including the difference between what garbage truly is during the implementation and why ERP historical data is hard to migrate compared to CRM. Finally, we discussed the business model implications of bad data and how the master data needs to be planned in different contexts in the multi-system scenario.
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