Product data is critical to staying competitive, but companies face challenges in the way they extract value from the data, particularly test data, according to a survey of senior product executives. The survey, commissioned by NI, also finds a correlation between advanced data strategies and increased innovation.
The report, “Designed to perform,” conducted by FT Longitude, polled 300 senior product executives, including chief product officers, SVPs and VPs of engineering, R&D, manufacturing, operations, production, and product across 10 industries including Semiconductor, transportation, consumer electronics, and aerospace and defense. They were asked questions about how they use product data to help meet challenges around product complexity, cost, compliance, and time to market.
Two of the biggest factors driving a change in product strategy is shrinking time-to-market requirements in combination with increasing product complexity, said NI. These challenges have resulted in over-budget and over-schedule product releases as well as recalls, said the company.
Key findings reveal that two-thirds of respondents believe that a data strategy is essential to optimizing the product lifecycle and 52% of companies with a company-wide product data strategy achieved a faster time to market over the last 12 months, compared to 33% without a data strategy.
Despite the value of data in product development, 55% of respondents cited costs as a key factor preventing them from improving their current product lifecycle.
Test data as a differentiator
Survey results also indicate that test is an underutilized resource: 38% of respondents said they rarely use test to inform product design and 51% believe they could extract more value from their data if they implemented test earlier in their processes.
A majority of respondents believe there is big value to be gained from insight into their test data. Forty percent of respondents believe integrating test data into the product development process is among the top initiatives that could bring the most value to the business and 55% report that integrating test data into the product development process will be a key priority over the next 12 months.
Other top initiatives include implementing a corporate data and analytics strategy that includes engineering and manufacturing (41%), creating an innovation program (36%), modernizing engineering processes (34%), and leveraging artificial intelligence/machine learning in the manufacturing process.
The survey also finds that top concerns among respondents vary depending ON company size. For example, the biggest concerns cited by product innovators at companies with 1-500 employees include shrinking time-to-market requirements, talent shortages, environmental laws, and trade barriers. In comparison, top concerns at companies with 1,000+ employees include increasing complexity of customer devices, supply chain volatility, global competition, and customer demands for product customization.
Overall, 57% of respondents said their production processes are outdated and cannot keep up with new business and technology trends and 46% believe their companies will lose market share within two years unless they make significant changes to product lifecycle processes.
Even though 65% believe a data strategy is essential to optimize the product lifecycle, 47% of companies use a limited data strategy, only 29% of companies with advanced data programs are fully capable of using manufacturing data to improve manufacturing processes, and only 24% are combining engineering, manufacturing, and in-use data to gain advanced insights. In addition, 74% of companies are not using product data to implement digital twins.
NI said the research indicates that companies with more advanced product data strategies have better business outcomes. These include higher levels of innovation, productivity, manufacturing efficiency, and speed to market.
But there is still more work to be done.
In particular, the survey identifies test data as the most underutilized resource with many companies dealing with inadequate data analysis and testing bottlenecks. One-third of respondents said their inability to gain insights from test data prevents them from improving their product lifecycle, and 52% said the way they capture, store, and manage test data prevents them from extracting value from insights.
Overall, the findings indicate that companies with limited data strategies are lagging behind. For example, over the past 12 months, 70% of companies with limited data strategies have invested in product data and analytics as a priority versus companies with advanced data strategies that have moved on to priorities around machine learning, digital twins, and robotic process automation.
The report also provides six tips on how to build a good data strategy. These include identify areas for improvement, work backward to identify data sources, implement a standardization strategy, build a product-centric data pipeline, analyze and act, and scale across the organization.