Building better businesses
True innovators will rise with the revolution
Our research indicates most product innovators recognize the product revolution is happening, and many are investigating ways to address their data shortfalls. After all, the technology investment priorities of the organizations in our research suggest they want to do things differently.
55% of product innovators say that integrating test data into the product development process will be a key priority over the next 12 months
40% identify this among the initiatives that could bring the most value to their business
It is clear that companies with limited data strategies recognize that they are lagging—and are now playing catch-up.
Accordingly, over the past 12 months, 70% have invested in product data and analytics as a priority, while advanced companies, who already have the data foundations in place, are more likely to prioritize cutting-edge technologies such as machine learning, digital twins, and robotic process automation (RPA).
But the focus and investments for every organization, limited or advanced, should be fueled by connected data across the product lifecycle. Unfortunately, some organizations remain daunted by this, seeing it as too costly, risky, or time-intensive to implement; 55% told us the cost of transforming their current product lifecycle is too high for them to justify the investment.
More than half of organizations say transforming the current product lifecycle model is too expensive
Chart shows the number of organizations that "agree" or "strongly agree" that the cost of transforming the current product lifecycle model is too high for them to justify investment
But transforming your product lifecycle is not an all-or-nothing endeavor. Let’s look at the steps your organization can take to build better data strategies for better-performing products and businesses.
How to build a good data strategy
1. Identify areas for improvement. Whether at a corporate, organizational, or even lab level, securing collective agreement on which areas of your operation need to be improved is a critical first step.
2. Work backward to identify data sources. Plan out the analysis needed to address the improvement vectors and then map out the data needed to fuel your analytics tools. Find those data sources within your operations, and, if they do not exist, determine how you can make them available. In particular, regularly consider test data as a central source to help drive product-level improvements.
3. Implement a standardization strategy. Agree on a set of processes, systems, software, and data formats for initial implementation. Consider scalability and flexibility, and use your decisions to define a standardized solution architecture. This will allow the current project, future projects, and projects across teams to be compatible and more easily executed.
4. Build a product-centric data pipeline. Invest in technology to create, collect, and store the required data. This includes any updated data generating assets, new networking connections to data sources, and the software architecture to transform all data into a usable format. Look across process, equipment, test, inspection, genealogy, ERP, and more, to ensure you have access to the right data to solve specific problems.
5. Analyze and act. Implement data-driven change by processing data with the standardized solution architecture and designing collaborative workflows to use the insights. This is the key step that converts collecting and storing data from a cost/liability into a powerful asset that adds value back into the organization.
6. Scale across the organization. Use the standardization strategy as a catalyst for broader business impact. Find and communicate success in each solution space before moving to standardize the next. Identify new areas of improvement and onboard new teams and new data to grow the data-driven footprint within the organization in a structured, scalable, and connected way.
It's time to transform your thinking
Innovation, by definition, requires a willingness to change and embrace new ways of working. By extension, product innovation requires innovation within the product lifecycle. It means using all the product data resources at your disposal to refine and streamline operations. Unless you can truly transform the way you think about product data and, more importantly, the way you incorporate it into your product lifecycles, you’ll only get so far. The most successful organizations will rise to the challenge.
Building better businesses
True innovators will rise with the revolution
Our research indicates most product innovators recognize the product revolution is happening, and many are investigating ways to address their data shortfalls. After all, the technology investment priorities of the organizations in our research suggest they want to do things differently.
55% of product innovators say that integrating test data into the product development process will be a key priority over the next 12 months
40% identify this among the initiatives that could bring the most value to their business
It is clear that companies with limited data strategies recognize that they are lagging—and are now playing catch-up.
Accordingly, over the past 12 months, 70% have invested in product data and analytics as a priority, while advanced companies, who already have the data foundations in place, are more likely to prioritize cutting-edge technologies such as machine learning, digital twins, and robotic process automation (RPA).
But the focus and investments for every organization, limited or advanced, should be fueled by connected data across the product lifecycle. Unfortunately, some organizations remain daunted by this, seeing it as too costly, risky, or time-intensive to implement; 55% told us the cost of transforming their current product lifecycle is too high for them to justify the investment.
More than half of organizations say transforming the current product lifecycle model is too expensive
Chart shows the number of organizations that "agree" or "strongly agree" that the cost of transforming the current product lifecycle model is too high for them to justify investment
But transforming your product lifecycle is not an all-or-nothing endeavor. Let’s look at the steps your organization can take to build better data strategies for better-performing products and businesses.
How to build a good data strategy
1. Identify areas for improvement. Whether at a corporate, organizational, or even lab level, securing collective agreement on which areas of your operation need to be improved is a critical first step.
2. Work backward to identify data sources. Plan out the analysis needed to address the improvement vectors and then map out the data needed to fuel your analytics tools. Find those data sources within your operations, and, if they do not exist, determine how you can make them available. In particular, regularly consider test data as a central source to help drive product-level improvements.
3. Implement a standardization strategy. Agree on a set of processes, systems, software, and data formats for initial implementation. Consider scalability and flexibility, and use your decisions to define a standardized solution architecture. This will allow the current project, future projects, and projects across teams to be compatible and more easily executed.
4. Build a product-centric data pipeline. Invest in technology to create, collect, and store the required data. This includes any updated data generating assets, new networking connections to data sources, and the software architecture to transform all data into a usable format. Look across process, equipment, test, inspection, genealogy, ERP, and more, to ensure you have access to the right data to solve specific problems.
5. Analyze and act. Implement data-driven change by processing data with the standardized solution architecture and designing collaborative workflows to use the insights. This is the key step that converts collecting and storing data from a cost/liability into a powerful asset that adds value back into the organization.
6. Scale across the organization. Use the standardization strategy as a catalyst for broader business impact. Find and communicate success in each solution space before moving to standardize the next. Identify new areas of improvement and onboard new teams and new data to grow the data-driven footprint within the organization in a structured, scalable, and connected way.
It's time to transform your thinking
Innovation, by definition, requires a willingness to change and embrace new ways of working. By extension, product innovation requires innovation within the product lifecycle. It means using all the product data resources at your disposal to refine and streamline operations. Unless you can truly transform the way you think about product data and, more importantly, the way you incorporate it into your product lifecycles, you’ll only get so far. The most successful organizations will rise to the challenge.
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