Digital transformation in the industrial sector is not purely a matter of swapping out on-premise servers for the cloud. It’s not just about replacing analog processes with digital technologies. And it’s not about launching a single product or technology that will transform an organization.
In fact, digital transformation at an ITW Test and Measurement company is as much about culture as it is about tech. It’s as much about the mindsets with which employees and leaders approach their work as it is about the products they sell.
For the last 20 years, we’ve been working side by side with Dickson as their team transformed the company from a powerhouse of analog environmental monitoring to a leader of Industry 4.0. One reason Dickson’s digital transformation strategy has been and continues to be a success is that the entire team has embraced these five mindset shifts that let it innovate on an ongoing basis:
Embrace data and analytics.
Embrace change.
Prioritize customer needs.
Be willing to collaborate.
Strive for continuous improvement.
Read on for a closer look at each of these mindset shifts, a glimpse into how they manifest at Dickson, and a look at how you might embrace them for similar results.
1. Embrace data and analytics
For digital native companies, data and analytics are often core to the operation: decision-making is fueled by data, leaders from every department lean on insights from business intelligence platforms, and everyone is working toward optimization.
For 85-year-old manufacturing companies, though, the digital transformation journey is an enormous undertaking. These companies were founded before desktop computers (never mind smartphones). It’s hard work to transition their core operations to modern technology.
One struggle many organizations like ITW Test and Measurement companies share is finding ways not just to collect data and analytics but to use those metrics to power decision-making at every level on an ongoing basis.
Let’s take a look at how Dickson embraced a data-driven mindset as part of its digital transformation and how you can do the same.
How Dickson embraces data and analytics
In the early days of our engagement, Dickson’s sales team wanted more insight into how people used the company’s brand-new website: what were they clicking, how long were they staying on pages, etc. Today, we take for granted that websites can and do collect those kinds of metrics––and that organizations will use them to power business decisions. Not so back then.
We worked with the sales team to understand the behavior of website visitors and used that to track which campaigns were successful. We tied orders that came in to both web activity and legacy catalog campaigns.
Crucially, we helped the Dickson team interpret this data and understand how to use it as a source of business intelligence to fuel decision making–– like when to ease off catalogs and ramp up their web presence.
Over time, as the use of data and analytics became more universal, we recognized that these metrics were not just back-end utilities that could power Dickson’s own decision making; they were also potential features we could incorporate into the products Dickson sold to customers.
One big data opportunity might be unifying data that lives in various silos from around the organization and from other divisions of the organization. Another might be finding ways for equipment to track use and performance in real time (e.g., via cloud-connected sensors), feed that data to software, and translate it to recommendations for customers (e.g., when to recalibrate, when to contact your company about servicing, how to forecast new equipment purchases, etc.).
Data and analytics takeaways
Ask everyone on your team what they need to do their job better.
Translate needs to metrics.
Start measuring.
Build a data-to-insights toolset.
Empower decision makers with access to insights.
Make decisions based on data-driven insights.
Of course, this is all easier said than done. If you’re not sure how to, say, translate your team’s needs to specific metrics, consider working with an innovation consultant who has done this work before and can help you navigate the data analytics process.