Wednesday, April 1, 2015
09:30 AM - 10:15 AM
National Instruments (NI) manufactures and sells a vast and complex array of instruments, devices, and systems that equip engineers and scientists with the systems they need to innovate and solve the world’s greatest engineering challenges.
NI has been challenged with a fragmented customer experience due to choice complexity in its vast array of products and services in addition to proliferation of inconsistent and duplicate product information. To overcome these challenges, NI has begun to simplify the customer experience making it easier for customers to understand what they need to buy. The product data platform is expected to improve marketing efficiency and effectiveness through data accuracy and governance and provide a unified view of product information for accurate business intelligence and more informed executive decision-making.
Key elements of their approach include:
- Business conditions leading up to investment in product MDM
- Approach taken to justify investment
- Defining the enterprise product data model
- Taxonomies, hierarchies and governance
- PIM tool selection and pilot
- Implementation approach
- Progam results
- The road ahead
Margie Eddy, Data Architect, has spent a career working in a variety of data management and architecture roles in IT services providing consultative assistance to customers across multiple industries. She came to NI in 2012 to lead the efforts to solve the problem of product data management. She has expertise in master/reference data management, data integration, data modernization, data quality and profiling, data modeling and information requirements planning.
Kyle Klufa is the Product Manager for enterprise Product Data at National Instruments, a leader in the Test and Measurement industry. For the past 2 years he has been working on the eBusiness team at NI working on various projects for product data, reference data, governance and internal collaboration. He oversees the alignment of Product Data and product concepts to the enterprise product model. Having graduated from Oklahoma State University in Mechanical Engineering, his background provides insight into concepts and categorization that optimize existing models at NI.