Common Reasons Why Data Cleansing Projects Fail
It is no surprise that we often run into companies who have experienced a failed data cleansing project and no longer believe in the value that cleansing provides. It is unfortunate to hear about companies who have invested thousands of dollars in data cleansing projects, only to end up going back and correcting the data afterwards. When meeting with many of the materials, purchasing and procurement managers in these situations, we typically discover a few common reasons why their previous cleansing projects failed.
One of the most common reasons for project failure is that the previous service provider simply used automated software to rapidly extract and classify thousands of existing items without human review. While the speed and efficiency of this method may have been impressive, the end result was not. In these cases, data was returned to the customer with incorrectly classified items, inconsistent descriptions, and often, inadequate information. Although the quality of these automated software applications has come a very long way and is continuously improving, the truth is there is no software application that can reliably transform large files of unstructured data into accurately standardized, enhanced and structured descriptions without human intervention.
Another common reason why data cleansing projects fail is due to a lack of flexibility to accommodate customer requirements and an unclearly defined Standard Operating Procedure. Many data cleansing companies are very rigid and will only cleanse and format data to their own standards. Obviously, this can become a significant issue as every company is unique and has different business requirements when it comes to format, standards, abbreviations and project timeline. If data is not standardized and structured according to customer requirements, it not only defeats the purpose of implementing a data cleansing project, but also requires a significant amount of time and effort for the customer IT department to re-work and prepare the data before uploading. Project timeline is also critical as Data Cleansing is often part of a larger ERP implementation. If the data cleansing deliverable is not completed on time and within scope, the entire project will be delayed, costing the company valuable time and money.
The final common reason why data cleansing projects fail is due to the absence of a long-term strategy to maintain ongoing data quality as items are added, modified and suspended within the catalogue. If a catalogue management process is not implemented after the cleansing project is complete, the data will quickly revert to its previously corrupt state. Once again, this common mistake defeats the purpose of investing thousands of dollars into a Data Cleansing project.
Data Cleansing can provide many short and long-term benefits for all units of the business when implemented properly. If you are one of the unlucky companies who have invested thousands of dollars into a failed data cleansing project, don’t feel bad, you’re not alone. While Gartner claims that the MDM market is still premature, there are a few solutions available that have been proven and perfected over many years of experience. Although every data cleansing company uses software to a certain extent, the best results can only be achieved through a combination of software and human intervention. When considering a data cleansing project, it is well worth the time and effort to research various service providers to understand their cleansing methodology and ability to meet company specific requirements. After all, data is the foundation for business decisions and if the foundation isn’t constructed properly, the entire investment will come crumbling down.
About I.M.A. Ltd.
As a results-oriented company, I.M.A. Ltd. is dedicated to providing the most accurate, consistent and reliable data available in the industry, while continuously developing and improving solutions based on the changing market and feedback of customers. Although many competitors have chosen to sell software and services based solely on speed and efficiency, I.M.A. Ltd. believes that quality remains the most important factor when dealing with critical inventory data. The I.M.A. theory suggests that a balanced combination of technology and human intervention is required to achieve the highest level of data quality.
For more information on Data Cleansing and related services, visit www.imaltd.com or contact email@example.com.