Drive Change with the Industrial Data Journey Model
Discover our 5 step Industrial Data Journey Model helping you to balance out Use Cases, Technology and (most importantly) Organizational Maturity
Achieve big through small.
Easy, right?
Going for the big bang never works out. In his book “Sooner Safer Happier”, Jonathan Smart describes what happens:
“When change is introduced in a big-bang matter [..] a slight initial rise with either excitement or denial is followed by a deep depression when reality hits.”
“We need to start with small teams, small slices of value and small investments. The time to value, to learning, to achievement, to improving outcomes [..] is sooner.”
The resulting morale of team & users will look as depicted in the graph below. The top line represents a ‘small step approach’, the bottom line represents the ‘big bang project’. When taking small steps, you will still encounter dips, but they will be small and easy to overcome (caveat: this does not exclude you from having a clear vision/goal in mind!). When going for the big bang, the dip will very likely be deep and in many cases even too big to overcome, ultimately leading to a failed project.
To achieve these small steps, you find the balance between
Finding achievable Use Cases,
Implementing the minimum amount of Technology needed and
Increased Organizational Maturity in order to accept the solution.
If you find the right balance, you might end up in the holy grail of Digital Transformation, namely: an upwards spiral of growing applications, morale and competence.
To help you find where to start, we have developed the Industrial Data Journey Model.
The 5 step model describes what steps to take (crawl first, then step, then run) and what challenges you will face (yes, there will still be setbacks). The model will help you to measure your current state & progress and identify your ambitions. Although being made for industrial use cases, it can be applied to many challenges you might face.
The Industrial Data Journey Model
Stage 0: The Dark Ages of Data
This is our starting point, our "ground zero". We should assume that anyone with some digital ambitions has at least invested in a SCADA system, PLCs, a DCS system or just a whole bunch of digital meters (counters, flow, pressure, temperature, vibration…). In reality, however, we see that even in the biggest companies, a lot of measurements continue to be written down in the "good old way" (on paper, and we can't do anything useful with that).
Stage 1: Initial Visibility, or the “Watch” step
Once we measure, we have to start using the data.
Our first challenge is to make this data available to everyone using a Process Historian, creating an historical record of how the processes evolved over time.
Other systems storing transactional data such as Manufacturing Execution Systems (MES) are equally important. It all depends on what you are making, for example, in discrete or batch manufacturing an MES system will probably be more important to you, but in a continuous chemical process you really want to have as much time series data as possible.
However, connected & collected data is useless until it gets used. That means that you will need to train as many people in your organization as possible. You need to introduce easy to use trending tools (eliminate friction), give users the ability to easily share reports to peers and why not share success stories within the entire organization?
More about Time Series data in this article:
Stage 2: Discover, or the “Diagnose” step
Or the “Be amazed step”, because step 2 is truly the most important one!
Once we have access to the raw data, the real work begins. When people complain in Stage 1, it is typically because there is still quite some manual work involved in building a report. Stage 2 takes us to smart and insightful dashboards that enable us to do our work easier, better, faster.... Just try to calculate the energy consumption of a particular product... not easy!
When we are able to integrate different data sources with each other (e.g. Historian + MES) we have information in front of us that works for us, not against us. We call this: Introducing Context.
This step is what almost every industrial operator dreams and preaches about, but where hardly anyone has made any really big steps yet. Besides the tooling, it mainly requires a mindset and a dedicated team of experts.
We have dedicated a full article on Context and Data Platforms, read it here:
Stage 3: Get Informed, or the “Predict” step
This is where the data journey starts getting exciting
Having established a clear understanding and visualization of your data in the previous stages, you're now ready to venture into prediction. This involves implementing more advanced analytics tools that can interpret patterns, trends, and relationships in your data. This is also where Artificial Intelligence enters the stage.
Two crucial factors determine the success of this step.
First is having access to the process context. The data doesn't exist in a vacuum. It is interconnected with and influenced by the various processes in your operation. Therefore, any predictive model must consider this contextual knowledge to be accurate and reliable.
Secondly, the quality of your data can significantly impact the results. In the previous two stages, errors in data are typically spotted by people looking at trends and dashboards: “Why is this KPI suddenly at 9999%?” or “Why are there NULL values in this table?”. But once we enter Stage 3, we now rely on mathematical models to do the number crunching for us. Unreliable or erroneous data can lead to misleading predictions and hamper the overall decision-making process. Therefore, continuous monitoring and improvement of data quality becomes indispensable at this stage.
You might want to review our earlier article on AI here:
Stage 4: Get guided, or the “Optimize” step.
This is the ultimate stage of the data journey where we “close the loop”
At this point, you're no longer just passively observing data. Instead, you're actively using it to guide decisions, enhance operations, and drive improvements.
Yet, success at this step relies heavily on integrating business context. That is, your data-driven insights must align with your overall business objectives, strategies, and constraints. Only then can they lead to tangible, meaningful changes that truly optimize your operations. For example, you might want to use the current energy prices or availability of raw materials to decide what and when to produce.
Final Take: Change as the common denominator throughout all steps
If you go back to the visual representation of the model, you’ll find the horizontal “Time & maturity” axis, as well the vertical “Value” axis. As you have also noticed, every Stage builds upon the previous one. For example: you can never “only do predict” as the wisdom from the previous stages feeds into the next.
Remember, each stage of this journey represents a different facet of change - technological, organizational, and cultural. Embracing digital transformation means not just adopting new technologies, but also fostering a data-driven culture within your organization.
Encourage your teams to think differently, question existing procedures, and seek out new opportunities hidden in your data. Equip them with the tools, training, and support their need to navigate this journey effectively. After all, the true power of data can only be unleashed when it is championed by the people who use it.
It is important to note two additional things:
Your organization can be in multiple stages at the same time: Different Teams, Products or Projects can be in a more advanced step than others.
We do not proclaim that you need to go through all 5 steps to be best in class, changing stages is purely a value driven process. You might get the most value in step 2 and therefore decide that this is your goal.
The data journey is undoubtedly challenging, but the rewards it offers make every step worthwhile. As you progress, remember that the goal is not to simply collect data, but to extract value from it - turning raw numbers into a compass that guides your organization towards greater efficiency, productivity and success.
In summary:
For our Dutch speaking audience, I have a keynote about the Digital Journey which you can watch here: https://www.benelux.avevaselect.com/landing/role-of-artificial-intelligence-in-the-industry/ (starting around 14:00)