In this comprehensive guide, we look at all aspects of data engineering to understand how this fundamental discipline is the foundation for all things data.
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  • Industry 4.0 – it’s a term that’s been around for over a decade now. No really, it’s been that long. Like most hot topics in tech, it’s a term that’s mired in hype, heightened expectations, and an underlying threat of being taken over by robots (a la Terminator).


    But behind the buzz, there’s a powerful digital transformation undercurrent that is legitimately modernizing even older technology like SCADA for the 21st century. It’s exciting, overwhelming and even a bit scary, but it will prove to be a critical component in the ever continuing drive towards manufacturing optimization and improvement.

    Let’s get started. 

    And it’s happening today.


    So what is Industry 4.0, and more importantly, what can your company expect to get from it today, tomorrow, and over the next decade?


    In this comprehensive guide, we look at all aspects of Industry 4.0 to understand how this wave of innovation can be harnessed for good.



  • Origins of 

    INDUSTRY 4.0



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    If we’re at 4.0, it’s worth asking the question about the three other phases.


    The first industrial revolution happened long before it was cool to add a “.0” to your versioning system, way back in the 18th century. The headline innovation of this age was development and adoption of steam-powered technology, with significant production increases in areas like textile manufacturing.


    The second industrial revolution began in the late 19th century with the mass development of railway and accompanying telegraph networks, along with an electrification of industry.

    The third industrial revolution started in the post-war period of the 20th century with the introduction and mass adoption of digital computer technology.  


    Not surprisingly, each revolution has set the conditions for the following transformation, and the 4.0th industrial revolution is one that leans heavy on the previous proliferation of computing. Industry 4.0 then is the application of this raw computing power towards advanced manufacturing use cases, including direct machine-to-machine communication, mass proliferation of sensor technology, cloud computing and machine learning and artificial intelligence.  

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    Transforming the

    physics to digital



    Two trends serve as the driving force behind Industry 4.0: rapid proliferation of cheaper and cheaper sensors, and the relentless increase in computing power.


    The first of these trends, cheaper sensors is leading an increase in the amount of data that can be collected from a plant, machine, vehicle or any other real-world equipment. Interestingly enough, the trend towards smaller and cheaper sensors is driven by the success of another industry – smartphone manufacturing. These devices have been compared to pocket supercomputers, but they are so much more. With over a dozen sensors including magnetoscopes, GPS, gyroscopes, accelerometers, barometers, proximity, ambient light, fingerprint sensors and the like, these devices are more akin to mobile labs than mobile computers. It’s the scale of production with billions of phones per year that drives innovation and builds out supply chains for these sensors, some of which can be shifted towards other applications like industrial measurement. Furthermore, these sensors are designed to produce real-time streams of data 24/7, which when stored raises the tantalizing opportunity to work with millions of hours of production data.


    Computing power, the engine that processes all this data has been growing at an exponential rate for the past 50 years. This phenomenon, first observed by Intel founder Gordon Moore and so named Moore’s law, states that processing power and/or capacity will double approximately every 18 months.


    To say that this relentless growth has been transformational would be a ridiculous understatement (https://twitter.com/kenshirriff/status/1450164674494750743?lang=en).


    For Industry 4.0, this continuous growth in computing power provides the horsepower to process the ever-increasing steam of data from every plant, machine, and sensor.


    The convergence of these two trends – sensors and computing – ultimately result in a more accurate and comprehensive digital model of physical machines, equipment and processes.


    And once something is digital, it can be copied, replayed, simulated, optimized, and so on. Mathematic-oriented disciplines like computer science and sub-categories like data science, machine learning and artificial intelligence provide techniques that can manipulate, identify patterns and make predictions on any stream of data independently of what that data represents. Algorithms developed for social media processing are equally useful for complex factory environments.


    Industry 4.0 will digitize everything.

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  • Use cases – end to end

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    Without a clear data strategy, companies run the risk of carving off small data projects (or worse yet no projects at all) that don’t connect, resulting in a sum that is less than the parts.

    Forward thinking companies understand the value of knowing where they are, and where they want to go. From a data perspective, this assessment involves understanding where they fit in the analytics maturity model.

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    The majority of companies that have not formally developed a data strategy tend to fall in the descriptive category, performing basic reporting. Those that make a conscious decision to focus on improving their reporting will advance to the diagnostic phase of their journey, where they not only centralize data

    to improve the efficiency of their reporting, but also examine the question

    of “why” when doing analysis. More advanced stages of maturity include predictive and prescriptive, but in reality few companies achieve this level

    of expertise across the entire organization.


    One common mistake made by many companies is to jump into advanced stage use cases without getting the basics right, or even knowing where

    they are in terms of analytics maturity.

    Doing so not only potentially ignores lower-hanging opportunities for improvement, but in the worst case may set the company up for failure if it chooses to take on a project that ignores earlier stage prerequisites. For example, a company looking to do predictive analysis to support production planning will not be able to efficiently or reliably perform analysis if all the source data is spread across hundreds of spreadsheets managed by dozens of people.


    At 3AG, we developed the Data Coach with the specific purpose of helping companies to assess their current analytics maturity level, their target, and the necessary steps required to achieve this goal. Other organizations also offer similar roadmap development services – the important this to consider is that you have a clear plan of attack.

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  • Self driving factory ​

    Data governance is where your company gets to set it policies related to

    how data is collected. It is tightly integrated with data strategy in that

    it is the implementation of the rules defined in the former.


    A solid data governance policy will not only look to identify which sources to consolidate, but it will define who will be able to access data, when the data is pulled, and what do to when conflicts might occur. The governance policy also defines other issues such as retention policies and how to integrate new sources of data as they come online.

  • Wrapping Up

    We hope that you have found this resource helpful. Data engineering is a complex topic, made more so by the fact that there are countless ways for an organization to tackle it.



    For organizations looking to get more control over their data but who lack experience in this area, a guide can be invaluable. Consider bringing in experts who can give an unbiased assessment of your current situation, and of course we would be honored if you were to consider the 3AG Data Coach as an option.


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