Hi everyone. Welcome back to lesson six. In this lesson, I'm going to discuss the importance and an approach to ensuring that we have good solid data to populate the new earpiece system. I'm Jennifer Nicholson any veteran of an IR p implementation just like critical success factors, you will always hear data cleansing at the top of the list. What is data cleansing? You ask is the tedious process of finding and correcting errors in your data to ensure that each item is only included once and is correct.
Different users can enter the same piece of information several different ways. This then results in the same customer cart vendor etc. being recorded numerous times and makes it difficult to find the right answer when you're looking for something. May master data completeness and accuracy is crucial to MRP implementation success. Many people vastly underestimate scope and complexity of data cleansing. Let's talk about why data cleansing is important to your success, as in tips to improve your approach.
Data cleansing is a huge factor in earpiece success. Because it is leveraged in every phase of the operation. an IR p implementation will be as successful as testing execution, and the only limitation on testing execution or data completeness and accuracy. an IR p implementation will be successful as its training program and an effective European training requires master data completeness and accuracy. and European notation will be as successful as it synchronization of business data I go live and that synchronization requires master data completeness and accuracy. Every success that occurs has complete and accurate master data as a prerequisite three primary reasons that people underestimate the task of data cleansing, which if avoided, will Make your PRP implementation better.
Number one, because master data is usually dispersed among multiple systems. In legacy people rarely appreciate how much of it there is. Some data cleansing ends up being an iterative process, which creates a bit of a catch 22. You can't test without master data. And the reason for the test is to evaluate master data, there were three is extremely difficult to objectively measure progress on data cleansing, particularly in light of number two data that you might have considered cleanse and complete, does not test well. And suddenly it becomes unclenched, saying stuff, isn't it, but to be forewarned is to be formed.
So with this knowledge, there are lots of steps you can take to improve the data cleansing process. Number one, make progress where you can, some data will take time to accumulate, but in the meantime, you can recognize that you have addresses on South Main Street, South Main Street abbreviated as Main Street, as Main Street abbreviated. You know what I'm saying lots of different ways you can put down and need to work on fixing those making them all consistent. Set up a list of how every address should be input. So that is consistent throughout the company. Look for zip code and city mismatches, anything like that.
So to schedule routine data cleansing review meetings, and be relentless about probing for bottlenecks and problem areas. Number three, establish an aggressive completion plan, which you probably will not hit, but invest the time to understand exactly why you're not hitting it. And number four, assign the right people to the task. Smart, hardworking, detail oriented people match the right skills with the job needed. Like most of your P implementation, the lessons learned and the process is created for data cleansing, become part of an organization's DNA. complete and accurate master data will be an essential for success two years after go live as it is on day one.
Make it part of your earpiece success story. In our next lesson, we will talk about testing. Thanks for joining me