What does it mean to create value by using your data?

- by Florian Vogt

Value is generally described in dollars, euros, or any other currency. In short, value = money. Ultimately, from a business perspective, this is where all the threads come together. Let’s take a closer look at the threads originating from data in more detail. What is it that brings more value (i.e., money) to businesses?

• Higher productivity: The most obvious and most desired. Analyzing data to find operational sweet spots that increase output.
• Lower costs: The second most sought-after thread. Finding settings that could possibly decrease the amount of raw material, energy, etc.
• More time to innovate: It is common for data analysis work to consist of ~80% data access and preparation. One can spend 30 minutes or longer to access the latest data (I feel you, people). Imagine having the ability to automate that process – and instantly gain time. Time that you could spend getting coffee with colleagues, rethinking the experiment, walking down the production line…time to innovate!
• Less stress: Yes, less stress. Automating the lengthy, tedious parts of the work. Having predictable experimental workloads by using statistically designed experiments. Having clear understanding by improving the communication of data and results. Less stress improves wellbeing and the productivity of an individual contributor.
• Better reputation: Having standardized ways of replying to customer complaints. Using modern methods of analysis and experimental procedures can attract good staff and make them happy by providing the freedom to do good work.

Lessons in Innovation: Reflecting on Expert Interactions

Looking back, I’ve had many valuable exchanges. Two of the most memorable were the Time to Innovate events I moderated. I was lucky enough to have the opportunity to interview industry experts on their perspectives of what brings value to their businesses.

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In May, I interviewed Oliver Pickburn , Innovation Excellence Manager at Synthomer, on the topic of solving problems in industrial R&D and manufacturing . Interestingly, he did not mention cost saving or productivity increase, the most important outcomes of using design of experiments. Rather, the top three benefits he listed were:

1. Creating reusable models: Models built from well-structured DOEs can continue to answer questions after the initial project delivers – e.g., what if xyz? If needed, initial DOEs can be augmented to answer the new question.
2. Communication of process understanding: Profilers (such as JMP Prediction Profiler) are useful to show what happens when changes are made, so that you can then study why a factor has a certain impact or why a certain interaction exists.
3. Predictable experimental workloads: Once a problem is defined, DOEs give a well-defined number of experiments to address the problem at hand.

Later, in October, I was fortunate to host two experts at a Time to Innovate webinar. Philip O'Leary and Jarmo Hirvonen from Murata joined the discussion, sharing their thoughts on the tools and skills needed to extract value from industrial data sets . They highlighted the key elements and benefits of strategic adoption of data analytics. The top three aspects were:

1. Standardize: A standardized way of working will relieve individuals, speed up time to results, and lead to better reputation among customers because they receive consistent and reliable content. Standardized data will also enable and promote the use of analytics as inconsistencies and access barriers are limited.
2. Strategy: Incorporating all parties involved in the data generation and data consumption processes. Designating people for specific tasks and making time for training, for exchange, and for skill development.
3. Sharing: Sharing what is already achieved and what is currently being worked on. Creating a community that will continuously grow the use and the value of data-based working.

From an organization’s perspective, this is where the loop closes. Building a solid foundation in R&D begins with analyzing the best. DOE efficiently provides new solutions that can be passed to production. Standardized data and workflows in production lead to more efficient problem solving and consistent communication both internally and externally.

Thank you, Oliver, Philip, and Jarmo. Of course, they shared much more detail in the live event. If you wish to revisit these events, go to:

?? Synthomer: Making Data-Based Decisions in R&D and Manufacturing with Modern Design of Experiments

?? Murata Electronics: Leveraging Data Skills to Drive Innovation and Quality Across Your Organization

What are your key takeaways from this exploration of data-driven value creation? Share your insights in the comments! Stay informed with our latest updates on data analytics by subscribing today ??