First Tip for COVID Days – Data Cleansing
Are you a manager in an organization? This is the moment where we all have some time and can take a little break to think about how we will catch up after the crisis.
What do we mean by data cleansing?
When talking about data cleansing in information systems, the intention is to improve the quality of the data in the system.
Why invest time in data recovery?
Inaccurate data constantly consumes a lot of time.
For example, when a salesperson has to choose a SKU, and they have two very similar SKUs, every time, before choosing, they will hesitate between the SKUs.
In the best case, they wasted a few seconds, and chose the correct SKU. In the worst case, they chose the wrong SKU, and this will lead to a collection of errors – incorrect production, incorrect delivery, repeated production, lower customer satisfaction rating, and more.
Examples of data that requires cleansing
- The customer list –
- each customer will appear only once, and customers who no longer exist must be deleted
- Customer details – names, phone numbers, classifications must be accurate
- List of contacts – must be correct, phone numbers and e-mails of contacts, mark which contact person is in charge of each issue (to whom an invoice is sent, to whom a delivery certificate is sent)
- The SKU list –
- Inactive SKUs will be cancelled, we will check that there are no duplicates (two
- SKUs for the same product)
- SKU data – check that the data is complete and correct – name, description, classification into product groups, engineering data
- Bill of materials – all the parts in the bill of materials exist and are marked correctly
How to know which data needs cleansing/where to start
- Prepare a list in Excel of all the data you want to cleanse
- It is recommended to work in a demo/testing environment – that is, not to “spoil” the data in the existing system (in the real environment)
- Select a process in the system – for example, a sales process, and execute it in the information system
- During the execution of the process in the system, add to Excel list any data encountered that is not correct
What to pay attention to
- It is advisable to take a real and representative example – for example, an order we received from one of the customers
- Fill in all the fields – our goal is to find out all the data that is missing/requires correction, therefore it is important to enter data in all the fields in the system. Only then will we find out which fields have problematic data.
Still not sure where to begin?
Contact us, we will arrange an online meeting, and complete the process at your organization in the system together
