Engineering might be one of the most data demanding environments. From design to production to logistics to maintenance and more. Making decisions relies heavily on what data dictates. And now digital transformation is adding new technologies like IoT, AR, VR and AI to the mix. But how do you bring that data to life?
From data to story telling
Engineers always want to understand how their product is performing. They monitor data closely, looking for insights that can prompt actions to optimize or extend the product's performance and lifespan. Often, data drives these decisions.
Many assume that adding more graphs to a dashboard leads to better decisions, but this overlooks the importance of making data meaningful. Is the data positive or negative? What story does it tell? Simply presenting endless charts and tables can result in an overwhelming, ineffective dashboard. Instead, combining IT intelligence with good UX design can turn raw data into smart, insightful visualizations, tailored to specific users.
Examples of effective UX design that make data easier to understand vary from simple heat maps to realistic digital twins.
• A large amount of data on complex demand and capacity can be simplified into a smart, interactive heatmap.
So many users, so many views
Data analysis helps companies spot patterns, trends, and relationships that shape strategic decisions. Decision-makers use these insights to improve outcomes and gain a competitive advantage in today's data-driven world. By analyzing data, organizations get valuable information that guides their strategies.
Industrial engineers have strong skills in mathematics and statistics, which are crucial for handling large amounts of data. Good data analysis is key to making the best decisions.
However, different interpretations of the same data can lead to different decisions. As the saying goes, "the truth is in the eye of the beholder." The user's context heavily influences decision-making, especially in engineering, where misunderstandings can lead to design flaws, safety issues, and higher costs. Good UX customizes data presentation to match individual interpretation styles.
“The benefit of simulation: Being able to produce bad results enables good learnings.”
Simulation as a safe playing ground
Good UX includes multiple steps to support all phases of decision-making. Usually, after monitoring and analyzing data, the next step is simulation. Decisions become easier when you can predict the outcomes. Trends and patterns often involve uncertain variables, so being able to experiment with these variables and simulate different scenarios is key to finding the best solution.
Creating a safe space where users can test different scenarios without real-world consequences is valuable. Allowing users to experience negative outcomes helps them learn and make better decisions.
• Simulation is the third category in Okapion's user needs model for data-driven decision-making from a UX perspective.
How to not f*ck up
Based on our experience, here are some key tips to not f*ck up in your data-driven decision-making process:
- Understand your end-users, their workflows, and their goals.
- Involve UX design experts early, before development begins.
- Design and test using accurate, up-to-date, and clean data.
- Always interpret data in the context of your specific situation.
- Collaborate with data analysts, domain experts, and other stakeholders. Different perspectives offer valuable insights and help prevent errors.
- Continuously test and validate your decisions with real-world results. Work iteratively with an agile mindset.
