Imagine opening up a filing cabinet with no folders—just files. It would be difficult and inefficient for anyone to find what they are looking for.
The same idea relates to data, which is why proper data classification is an initiative all companies should prioritize. This is especially true when combining or merging multiple data sources, such as PLMs like Teamcenter.
Classification is the process of organizing your products, parts, data and documents to drive efficiencies in searchability and productivity. An effective classification strategy includes:
- Targeting high value parts for classification.
- Pulling all parts from each organization for the defined categories.
- Providing a part number, description, drawing and/or specification for each part.
- Obtaining the commercial data for each part.
- Looking for clustering opportunities.
- Loading classification data into the PLM.
To get the best ROI from your data classification, no matter your scenario, you can’t miss these tips.
>>>See our data classification experts present this and more information at Siemens Realize LIVE!
Scenario 1: You’re Migrating From Many to One PLM
Data migration is a complex process. And if you’ve been involved in the technical data space for some time now, you’ve probably witnessed some headbutting of systems—whether that be PLM versus ERP, PLM versus CRM and sometimes even PLM versus PLM.
Some companies even believe it’s necessary to own more than one PLM system.
There’s often a lack of clarity about how and where product data needs to be managed. Some may think every business group needs its own classification structure—this isn’t accurate.
Instead of operating on several PLMs, it’s recommended to operate on one central system with a classification search solution that links to your normalized and validated data in a single system, and allows people across the organization to play a role in the product development process.
So, how can you get started? Consider engaging with a Teamcenter expert to guide your transition—it will make your migration to one PLM as seamless and simple as possible.
Scenario 2: You’re Reconciling Duplicate Parts
Often, organizations overlook the negative effects of having duplicated data within their PLM system. After realizing engineers waste critical time and effort searching for parts, many companies begin reconciling with duplicate parts they created.
There are four key steps to repairing the relationship between your PLM system and the parts within it. Once you take action on the following steps, your classification project will be on the road to success:
- Collect then classify the data. You first must gather all the purchase part data together for analysis, then classify what you found against a single data model. This will help dictate the categories that will drive the attributes and allowed values each part should have.
- Enrich and validate the data. Begin harvesting the attribute data for each part using the manufacturer part numbers or associated documentation. While time-consuming, this step is essential.
- Complete a duplicate and near-duplicate analysis. Execute the duplication analysis using a duplicate analysis tool based on attribute value matches that drive pricing and differentiation. This step is critical to making the analysis work.
- Process identified duplicates. Finally, take the groups of duplicates and select one internal part number to be the master for each manufacturer part number (MPN). This ensures purchasing will consolidate spend and rationalize inventory without disrupting existing BOMs.
Scenario 3: You’re Normalizing Data Among Multiple Business Groups
Hear us out: Don’t you want to make big, ugly datasets a thing of the past? Normalizing data among your business—across departments—is a step in the right direction.
Data normalization is the process of grouping similar values into one, bringing greater context and accuracy to your database. For example, at Convergence Data, our data services team reviews data and given attributes to ensure they use one naming standard for similar items and values.
We understand it can be intimidating to normalize data for multiple business groups. However, this presents the perfect opportunity to see maximum ROI on your data classification project. Think about it—when you normalize your data for the long term, you will have continuity that can be enforced with data validation and governance.
Not sure how to get started? Our custom-built software, Design For Retrieval (DFR), serves as a one-stop solution for cleansing, enriching, and validating your data and would be a great stepping-stone to begin normalizing data among your multiple business groups.
Get More From the Data Classification Experts at Siemens Realize LIVE 2022
We are excited to announce we will be attending and presenting at the 2022 Realize LIVE conference, hosted by our partners at Siemens.
Realize LIVE is the premier industry solutions event, connecting the Siemens Digital Industries Software community with peers, partners and products while promoting opportunities to learn, grow and optimize the tools.
Visit us at booth 309 to learn more about our longstanding partnership with Siemens, and how we can improve your Teamcenter instance and help you see maximum ROI on any data undertaking.
