The foundation of Industry 4.0: crafting your data strategy roadmap

Understanding Industry 4.0

If the Third Industrial Revolution was all about going digital, the Fourth is all about treating the data an organization amasses as a valuable business byproduct that can be turned into fuel that drives business strategy.

For example, if a manufacturer monitors the environment of its facilities, it 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 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 broadly. With Industry 4.0 technologies in place, organizations can optimize their operations and make data-driven decisions faster and more easily.

However, making the transition to Industry 4.0 is challenging. Chief among the challenges is establishing a solid data foundation to build advanced technologies. For example, if an organization 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 an organization’s 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 digital transformation.

The necessity of 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.

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 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 will be those who figure out how to consistently leverage their data assets to drive decisions and fuel innovation.

For example, a logistics company able to analyze data from dozens of sources might discover a reconfiguration of shipping routes that cuts costs by five percent. Or maybe a manufacturer could use its data to build a digital twin of its factory floor and identify a new layout that would boost efficiency by six percent.

To make these discoveries, industrial companies 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. Industrial organizations either need a data team in house with the expertise to organize their data and then build products that make it usable, or they need to partner with outside experts who can do that for them.

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 your organization always makes 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 an organization is carrying out its data-related business goals. 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 an organization develop this document? One catalyst might be that you’re not making progress on the data initiatives you’ve outlined. 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 you expected.

This is an excellent time to develop a data strategy roadmap. Other milestones that might spur you to develop or revisit your data strategy roadmap:

• Launching a new product or service

• Introducing a new data source or dashboard

• Introducing a new data initiative

• Striving for new business milestones (efficiency, productivity, profitability)

• Aiming to develop something new (i.e., innovate)

Let’s take a look at the steps an industrial organization can take to develop a data strategy roadmap.

Steps to create a data strategy roadmap

Throughout the process of creating 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:

1. 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.

2. 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.

3. 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.

4. 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.

5. Establish roadmap timelines and milestones. For example, you’ll want a data roadmap with a timeline for cleaning and organizing your data. You’ll also want to identify when you expect to achieve various business goals, when business and technical users will be onboarded to the new tools you’re using, and so on.

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. Two 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.