Since 2005, Milwaukee Tool has put customer-first innovation at the forefront of its operations, making it a leader in the Fourth Industrial Revolution (aka Industry 4.0). Today, more and more companies in the industrial sector are taking a similar approach, looking for ways to use the massive amounts of data they generate to drive decision-making, improve operational efficiency, and fuel business intelligence.
Milwaukee Tool is ahead of the curve—but do you have a plan in place to maintain your lead? If not, a data strategy map may be just the thing.
Think of the data strategy roadmap as a guide for ensuring that you stay at the forefront of the industrial sector as operating from a data-first perspective becomes the norm among your competitors.
This artifact outlines the route an organization like Milwaukee Tool will take to turn its abundant data into a fuel that powers every part of the organization as it continues to innovate in the manufacturing space.
In this guide, we’ll review what Industry 4.0 is, why Milwaukee Tool has a unique opportunity thanks to its current use of data, and how to use a data strategy roadmap to stay ahead of the pack.
Understanding Industry 4.0
If the Third Industrial Revolution was all about going digital, the Fourth is all about treating the data you amass as a valuable business byproduct that can be turned into fuel that drives business strategy.
For example, if you monitor the humidity and temperature of your facilities, you should be able to tell in real time when storage units are too humid for stored inventory or when the factory floor is too hot for optimal equipment operation and make changes before any damage occurs to equipment or supplies.
The technologies of Industry 4.0 make this kind of workflow possible:
Internet of Things (IoT) capabilities make it possible to connect nearly any physical object to the internet. For example, an organization could place internet-connected sensors throughout its facility.
Cloud computing means organizations can scale their use of internet-based resources without changing the number of physical servers they have to maintain. So an organization’s many sensors might connect to the internet and communicate environmental data to a cloud-based platform that workers could access from anywhere.
Artificial intelligence (AI) introduces the potential of automating significantly more complex work than has previously been possible. An out-of-range environmental reading, for example, might trigger both an alert to human workers and a recommendation of best next steps, based on insights from equipment user manuals, documentation of past events, and other data sources.
Other technologies that drive Industry 4.0 are robotics, machine learning, digital twins, and automation. With Industry 4.0 technologies in place, Milwaukee Tool can optimize operations and make data-driven decisions faster and more easily.
Even when an organization has embraced Industry 4.0 technologies in some parts of the business—as Milwaukee Tool has, with ONE-KEY, for example—shifting the entire organization to run in a unified way on a foundation of data can be challenging. Chief among the challenges is establishing a solid data foundation capable of supporting innovative technologies.
For example, if Milwaukee Tool wanted to prioritize facility upgrades, it would want to base its decisions on hard data. But it’s difficult to know what will have the biggest impact—improving insulation? Upgrading HVAC? Updating specific equipment? Investing in smart devices and software to manage them? Establishing a predictive maintenance workflow?—unless your data is easy to interact with.
In most organizations, which digitized piecemeal and have as a legacy a hodgepodge of disconnected digital systems, unified and uniform data management is far from the current reality. This is where the data strategy roadmap comes in.
Before we get there, though, let’s take a moment to reiterate the necessity of ongoing digital transformation—even if you’re already leading your industry.
The necessity of ongoing digital transformation in industrial organizations
In the context of Industry 4.0, digital transformation means reshaping the structure of an organization such that its data drives every aspect of its operations.
A few examples:
Maintenance
Old strategy: Maintain equipment on established preventive maintenance schedules
New strategy: Build a platform that enables machine-specific predictive maintenance to minimize unplanned downtime and facilitate end-of-life decisions.
Root cause / fault analysis
Old strategy: Juggle multiple screens, spreadsheets, analyses, and experts to conduct fault analysis and identify next steps.
New strategy: Use Industry 4.0 technology like spatial computing to visualize data more easily and perform complex RCFA faster and with greater confidence.
Identify new lines of business
Old strategy: Hope to achieve business growth by following hunches, “best practices,” consultant advice, or ad hoc industry analysis.
New strategy: Confidently assess new undertakings thanks to data analysis. Establish and maintain a competitive edge.
All of these examples—and many more—become possible when organizations have easy data access and the ability to perform data analysis in virtually any context. When data is at the center of business objectives, it’s much easier to make informed decisions, pinpoint the best opportunities for business growth, and seize those opportunities. In other words, it’s much easier to keep innovation at the forefront of operations.
The role of data management in Industry 4.0
So what does it mean to have data at the “center of business objectives”? For one thing, it means that industrial organizations like Milwaukee Tool must reimagine data as an essential strategic asset.
Whereas the titans of Industry 2.0 may have relied on genius to gain an edge over their competitors, the leaders of Industry 4.0 are those who figure out how to consistently leverage their data assets to drive decisions and fuel innovation.
For example, Milwaukee Tool might analyze data from dozens of sources and discover a reconfiguration of shipping routes that cuts costs by five percent. Or maybe you could build a digital twin of a factory floor and identify a new layout that would boost efficiency by six percent.
To make these discoveries, you first have to pull together structured and unstructured data from multiple sources, normalize this data so that it can be queried, and build data products that let both expert and non-expert business users access the data.
Doing this is no small feat of data engineering. You either need a data team in house with the expertise to organize your data and then build products that make it usable, or you need to partner with outside experts who can do that for you.
And, of course, new data is constantly being created. Part of the challenge of pivoting to be data-driven is ensuring that new data is incorporated into data analytics and products so you’re always making decisions based on the most current and up-to-date data.
This is something the data strategy roadmap can account for.
Developing a data strategy roadmap
While the term “roadmap” suggests you’re headed to a fixed destination, the goal of the data strategy roadmap is to ensure that you’re carrying out your data-related business goals and moving in a direction that makes innovation more and more natural. Even after you “arrive” at effective prioritization, the work of strategizing new business goals is ongoing.
For this reason, the data strategy roadmap should be a living document.
So how and when should Milwaukee Tool develop this document (if you don’t have it already)? One catalyst might be that you’re not making the progress on data initiatives that you anticipated. Maybe you identified the data sources you can pull from and the types of business decisions you’d like that data to help you make, but you’re not moving forward as expected.
This is an excellent time to develop or update a data strategy roadmap. Other milestones that might spur you to develop or revisit your data strategy roadmap:
Launching a new tool or service
Introducing a new data source or dashboard (e.g., updating the ONE-KEY interface)
Introducing a new data initiative
Striving for new business milestones (efficiency, productivity, profitability)
Aiming to develop something entirely new (i.e., innovate)
Let’s take a look at the steps Milwaukee Tool can take to develop or update a data strategy roadmap.
Steps to create or update a data strategy roadmap
Throughout the process of creating or updating a data strategy roadmap, it’s important to consider who the key stakeholders are:
Which internal business stakeholders can contextualize the business needs?
What internal technical stakeholders can provide insight about your technological capabilities and gaps?
Will you need to bring in external vendors or consultants?
Making sure the right team is involved is essential to success when creating a data strategy roadmap.
Here’s what the process typically includes:
Assess your current data capabilities. What you’re able to do with your data depends largely on your current level of data maturity. Once you’ve established where you are, you’ll want to make plans for developing data infrastructure and data architecture that let you operate at the level you aim to reach—and let you continue to out-innovate your competition.
Identify data-driven business opportunities. How much down time could you prevent if you had better visibility into equipment health metrics? How much more efficiently could you operate if you could model various factory layouts with a digital twin? In this step of creating your data strategy roadmap, it’s best to both identify opportunities and then prioritize them by effort and impact.
Define data governance and data quality measures. Your data governance framework will include guidelines for data access, data privacy, documentation, standardization, and more. A robust data governance policy should make it much easier to adhere to state, national, and international data privacy and security regulations. High-quality data can also make it easier to assess compliance with emissions regulations.
Develop a data integration plan. Your technology architecture will define how various physical and digital components (from physical and cloud-based servers to software to sensors and more) connect and interact with data.
Establish roadmap timelines and milestones. For example, you may want a data roadmap with a timeline for cleaning and organizing your data. Or maybe you’ll be more focused on timelines tied to various business goals, like when business and technical users will be onboarded to the new tools you’re using.
Implementing the data strategy roadmap
We mentioned earlier that the data strategy roadmap should be a living document. That’s important to remember as you implement your data strategy. At the start of your journey, it’s a good idea to establish key performance indicators (KPIs) for implementation. Those KPIs will likely look different for different stakeholders and at different stages of implementation.
What's more, defining KPIs should be a collaborative effort, ideally with input both from experts on data strategy roadmapping (i.e., your in-house data team or the consultants you’re collaborating with) and experts on various business implementations (e.g., line workers expected to adopt new ways of working).
Also crucial here: project or team leaders should monitor KPIs on an ongoing basis and adjust course as needed.
Over time, as business goals and needs change, project leaders can and should adapt the data strategy roadmap. Three years ago, for example, incorporating generative AI into a data strategy roadmap might have been uncommon. Today, that’s changed.
The beauty of an effective data strategy roadmap is that, by increasing an organization’s data maturity, it facilitates an organization’s ability to jump on new opportunities as they emerge and pivot midstream as circumstances change. In other words, data maturity is essential to any organization that wants to stay at the forefront of Industry 4.0.
Overcoming common challenges to data strategy roadmap implementations
Maybe the biggest challenge in creating and implementing a data strategy roadmap is getting buy-in from leadership and adequate resource allocation to make the project a reality. That’s partly because a data strategy roadmap is a big-picture, long-term undertaking.
The potential business gains you get from being data driven are enormous, but so is the investment required to become data driven—even if your organization is already partway there.
So how can internal champions overcome these obstacles? While not all of these tactics will apply at Milwaukee Tool, we’ve found the following to be generally helpful:
Get buy-in from a key stakeholder who can advocate for the cause at the highest levels. Once top-level leaders understand the implications of embracing the data-forward position that helps you retain your position at the helm of Industry 4.0, you’ll have a much easier time securing the resources necessary to plan or update your data strategy roadmap.
Develop a vision collaboratively and market it internally. Any change in direction or way of operating can be met with resistance—even in an organization that’s used to embracing data. One of the most effective ways of overcoming that resistance is to bring many voices into the conversation early on and let that input shape the roadmap.
Highlight the successes of competitors and adjacent companies to inspire action and commitment. These are slightly different but related strategies. The former, highlighting how competitors are leveraging their data, can inspire FOMO (fear of missing out) and spur leaders to action. The latter, where you showcase data feats at organizations you don’t compete with directly, can inspire an ah-ha excitement in leaders who get energized at the prospect of trying new things. The right technique will depend on who you’re persuading and how they’re best motivated. Or you may find a combination of storytelling modes helps get your point across best.
Start small and celebrate incremental wins to keep people motivated. Creating (or updating) and implementing a data strategy roadmap is, again, a long-term process. To keep everyone motivated and invested, publicize and celebrate incremental wins along the way. There's also a strategy here to structuring the roadmap such that you can have a win early on that feels meaningful and impactful for your team.
The future of innovation for Milwaukee Tool: it all starts with data strategy
Maybe the most powerful argument for investing in or revisiting your data strategy roadmap is that doing so will also prepare you for whatever technologies come next.
What the dominant technologies of today (robotics, machine learning, digital twins, artificial intelligence, etc.) have in common is that they are fueled by data. Industrial organizations that can mine their data for insights and use those insights to drive business forward will become industry leaders regardless of what specific technologies come to dominate factory floors in the years to come.
When you double down on your commitment to data as a strategic business asset, you set yourself up for an industry leadership position that will put you in a place to leap first at whatever opportunity comes next.
If you’re ready to discover where else Milwaukee Tool’s data might take the organization, set up your Data Maturity Accelerator. In just three weeks, we can provide you with a clear picture of where you are today and what you need to do to maintain your innovation edge in the power tool and equipment space.
