subject: Why Data Cleaning Services For Business? [print this page] Statistics on data cleaning is overwhelming and there are mountains of discussions, white papers and tweets available for data quality, data profiling and Master Data Management. I think we need to step back and try to understand how and why data cleansing has become a hot topic.
You may have realized that corporate data is not usually kept as simple and effective as we thought it was. Your organization may have shipped the items bought back because they were not what you thought you ordered.
In some cases, another department was found that the inventory item, even if we have an update on the status of the urgent delivery from a supplier, because the element is introduced in under a different number or description, you could not have possibly known the item was actually available from existing inventory.
The data quality problems that industries across the world have occurred as a result of many years of manual inventory record keeping and purchasing, through mergers and acquisitions and business units as well as migration of data from various existing systems into new fangled ERP black holes. There are a number of reasons.
A trap often fallen into common data assumes that just because you implement a new ERP system for your organization will now have data quality. Remember the old computer motto - "Garbage In, Garbage Out". Let me tell you based on first hand experience that there is nothing "sexy" about incorrect data when the production line is down or any other time.
Data cleansing and data profiling service is a very tedious and detailed oriented. There are a number of basic rules to follow if the job profiling and cleaning is done internally or outsourced to someone who specializes in data cleaning. Here are some rules to consider before a project is launched:
1) Perform a detailed mapping of data across all internal systems, including engineering, procurement, asset management, inventory management plants, etc. The objective is to standardize and document all data sources within the company once and ensure that each department is represented and determines which data elements are needed to perform their professional duties required.
2) Build a database of data center cleaning and make sure all sites are referenced using each element. This ensures that the updated information will be donated to various existing systems. You will need the old information and updated information at this stage of the process.
3) The database data cleansing should include a balance of scripts for electronic data corrections and manual verification. A robust process for answering questions should be put in place. My preference is that the system must use a web-based utility that tracks the change history data and other information associated data such as contact information, the state of problem solving, classification, questions and answers, etc.
4) The data must be referred to a classification scheme and a standard implementation for descriptions and properties. The schema can be designed within your company, priority purchased from another supplier or you can opt to use a dictionary classification open to the public as.
5) Free text is not our friend in the world of data standardization. If all possible uses of a system that has built in rules and data to ensure that any person entering data in the system includes the standards and the importance of data quality, in addition to the high cost Companies using the wrong data.
6) data cleansing and profiling the right way is not "cheap", but the cost of cleaning up bad data is always less than the expenditures incurred by cleaning your data repeatedly or continue to operate your organization based on incorrect information generated by one or more databases dirty.
