How to cleanup Product Data?

There is one project in data management everybody tries to avoid: legacy data import. It’s one of the most complicated, time consuming, and painful aspects of any PDM and PLM deployment. Importing data usually lead to lots of problems – how to clean data, how to important data, how to map data to existing systems. Enterprise system developers are spending tons of money implementing different import tools.At the end, service provider and/or VAR is taking "legacy data import" project to implementations. Unfortunately, it costs money and time. Company in data management and content processing tool are paying attention to this problem too.

My attention was caught my ITProPortal – “HP Autonomy Launches Legacy Data Cleanup Software for Improved Info Governance.” Autonomy is a software outfit developing content processing, search and other data-related applications. Pay attention to software called Legacy Data Cleanup. It helps to arrange, classify and clean data. Here is an interesting passage:

“Legacy data is sometimes called ‘dark data,’ as it is effectively wasted space on limited storage, forcing organizations to buy more storage and hindering the modernizing of infrastructure and the transition to the cloud. This data includes obsolete SharePoint sites, old email stores, and a variety of other files that no longer serve any function. “The Legacy Data Cleanup solution helps businesses identify this unnecessary data and remove it responsibly, leaving an audit trail behind to meet company data retention policies. It can also help with the establishment of a records management system, which offers improved access to and more efficient legal holds on old data.”

Product data has lots of diseases – bad classification, broken references, mistyped fields, missed files and many others. Existing import tools are not very efficient and require to clean data before it can be imported. I can see an emergent trend of data tools that can help to bring data into new enterprise systems without cleaning and later to provide service that will make data available and cleaned. Some of the operations can be automated and some of them will require additional user work. However, I believe the last one can be minimized within time.

What is my conclusion? Most of manufacturing companies have data problem. Legacy data lives in many places – customer hard drives, old enterprise data management systems, Excel spreadsheets and Access databases. To be able to extract this data and move on with a new system, new data management components need to be development. First, they will scrap all data, then classify and prearrange this data for customer use. The result – lower cost, faster implementation. Just my thoughts…

Best, Oleg

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