True innovators know that delivering groundbreaking products is challenging enough—and today’s environment only brings a host of new pressures to development and production processes. The pandemic supply chain crisis persists and is intensified by difficult trade barriers. Organizations have had to pivot to new ways of working despite talent shortages and layoffs. Societal issues like climate change are taking center stage, urging businesses to address environmental regulations. Businesses and individuals alike are evaluating the consequences of new technologies like artificial intelligence, machine learning, and virtual reality. The intersection of these technological, societal, and business changes has accelerated innovation, and also added pressures like squeezed time-to-market windows and reduced budgets for already stressed engineering and test teams.

As market conditions evolve, we’re also witnessing a product revolution. Products are getting smarter and relying on increasing amounts of software, computing, and connectivity. Customers are demanding customization, sophistication, and reliability, which requires more complex design and more advanced control processes from manufacturers. From design, validation, production, and in-use phases of development, there are many different types of data (and tons of it) that need to be collected, analyzed, and correlated to make appropriate decisions. These demands can introduce more failure points in a product—but test is the best opportunity to prevent failure. By optimizing your test approach to account for evolving business and market demands, you’ll be able to deliver the most reliable, highest performing products to customers as quickly as possible while reducing operational costs.

We could almost end the conversation there. After all, building reliable products on budget and on time thanks to test is a remarkable short-term feat in itself. But test modernization is tied to something more far-reaching and forward-thinking. Staying competitive and bringing the most value across the product lifecycle will require smart leaders to think more broadly about digital transformation efforts. A product-centric digital transformation, meaning your organization’s ability to make product or unit level data open, flexible, and secure to the right people, will enable you to make the best decisions that drive innovation. A digital thread through your workflow seamlessly integrates data from your test systems to provide you with actionable insights to optimize product—and, ultimately, business outcomes.

But in the haste of getting increasingly complex products to market faster, many organizations are not taking advantage of the value that test can bring across a whole organization. Sometimes, test is considered an “afterthought,” leaving test engineers to validate increasing requirements on shortened schedules. However, in industries like automotive, which have rigorous and evolving regulations, test can take no shortcuts since failure is not an option. For example, NI partnered with General Motors on its Ultium EV battery platform to improve test standardization processes, including testing earlier in the product lifecycle, and data management. Engineers transformed how they use and share data, accelerate schedules, and optimize battery safety and performance.

Today, test teams must do more with less to deliver the best products. When test data is siloed to an individual department, it can be difficult to showcase how instrumental it is to overall business outcomes. So, how can test leaders who want to drive innovation bring enterprise-level visibility to their copious amounts of data and use it effectively across the whole product lifecycle? You’ll need to influence your organization to take on a product-centric digital transformation, which in some ways is both like a science and an art of persuasion.

The idea of instituting operational changes can seem daunting, expensive, and time consuming. However, many organizations can’t afford the cost of fractured test data and inefficiencies over time. Sometimes, test leaders are met with friction when advocating for upgrades to obsolete systems or implementing new processes. Resources may be limited, or teams might feel like they don’t have the bandwidth to take on new projects, especially when the perceived return on investment is immediately unclear. Likewise, for test experts, there may be very little desire to build, migrate, and manage a data initiative from scratch on your own.

A total overhaul of your processes is likely unrealistic. Change will probably have to come in increments. As a leader, you’ll need to take analytics into account to effectively achieve cross-organization digital transformation—and getting there is easier said than done. Eighty percent of the hard work needed to get to the right analytics includes pre-analysis setup. Standardization, automation, and analysis tools can help you properly connect your people, processes, and technology. 

For example, in terms of connectivity, it can be challenging for older legacy systems to automatically ingest, aggregate, and transform data. After integrating legacy and new systems, data will be abundant, but mapping data into a universal data model that adequately addresses access, storage, traceability, and searchability functions is another complicated obstacle. Your first steps toward connected data can start with a review of your current implementation and brainstorming on minor areas that need improvement. Start with an audit that assesses data collection, preparation, visualization, analysis, and sharing:

• Data Collection—Do you have a process to harmonize data from different sources, or could you even consolidate data in a single location?

• Data Preparation—How easy is it for you to find and view results? Especially as your operations grow to different labs and locations with an increasing amount of equipment and sensors, can you easily compare data and metadata with consistency across all sites?

• Data Visualization—Can you set user permissions to limit access to dashboards and results? Have you thought about whether you need on-premises or cloud-based security measures to control access? Can you easily make changes to your visuals to align with any changes in your testing and reporting needs?

• Data Analysis—To ensure the right analysis is performed, will you need to integrate complex tools like AI/ML? Are all your tools compatible with each other? Can you document a history of changes?

• Data Sharing—Can you easily report data to key stakeholders in different departments? Can you perform a root cause analysis if an issue is identified from your test results?

Taking action to break down the silos between design, validation, and production, along with ongoing usage and building a good data strategy will require you to work backward. After issues are identified, you can be more proactive about creating a better digital thread across your organization through collaboration and honest conversations, which can sometimes be difficult depending on your company’s culture. After discussions, standardization becomes easier. Your team and greater organization need to agree on a set of processes, systems, software, and data formats before implementing and driving a data-driven footprint through your whole organization. Getting buy-in from your organization will get easier as the importance of connected data and analytics to your bottom line becomes undeniable. 

As new technology trends emerge, even just a willingness to transform your thinking sets your test team up on a path to success. Are you ready to embark on a product-centric digital transformation? NI can show you how to harness the power of test data across the product lifecycle and use powerful insights to optimize your product and business performance.