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Prometis CyberSecurity helped Data Discovery and Classification for Large Manufacturing Firm
Example: In production plants there can be many 100,000 parts in the parts master data in systems for production planning and control. When a new part needs to be developed, it is important to know whether there is an identical or a similar part already in the database. It is important because a new part, regardless of whether it is a single part or an assembly, must be designed, drawn and created in the PPS system with a parts master and parts list.
In the case of a part that is to be procured externally, procurement-relevant master data must be recorded, possible suppliers determine, obtain offers, devise inspection regulations for the quality inspection in incoming goods and prepare the data for proper inventory management and storage
In the case of a part to be produced in-house, the work preparation department must prepare the production and assembly, work plans with the technological definition of the individual work steps must be drawn up on suitable machines, times for remuneration and scheduling and capacity planning must be determined, and possibly programs programmed and finally the part or the assembly must be calculated ( preliminary calculation )
This – incomplete – list of all tasks that arise when inventing a new part shows how important it is to identify similar parts at an early stage, either to avoid reinvention or, if reuse is not possible, the work steps that as a result, inevitably must be simplified and accelerated by using and modifying similar drawings, known suppliers, similar parts lists and work plans, etc.
• Determine Data Classification approach by either sensitivity / value or how Organizations wants to protect it
• Dynamic Data classification currently requires the integration of both manual process involving employees as well as tools for automation and enforcement
Master Data Management
Robust master data management inputs, validations, and rhythms are generally essential in underpinning accuracy throughout Organizations – the Prometis CyberSecurity approach identifies and quantifies opportunities within:
• Data governance – Defining and implementing a framework of rules
• Data lifecycle – Designing and implementing rhythms and processes
• IT architecture – Identifying and selecting appropriate tools to integrate with existing systems
• Data quality – Implementing appropriate KPIs to continuously measure the data quality and create insight to initiate any data cleaning initiatives or interventions
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