ARTICLE

The Rise of the Citizen Data Scientist in Manufacturing

Data Scientists are the new professional bubble.
Here’s how Industry 4.0 can level the analytics playing field for everyone at your organization.

As you’ve likely noticed, there’s a powerful shift taking place within the manufacturing sector. Buzzwords like Industry 4.0 and digital transformation are more popular now than ever. In the wake of the COVID-19 pandemic, more and more manufacturers are focused on bringing accessible Data Science tools to everyone so they can remain competitive. This phenomenon is often called the Rise of the Citizen Data Scientist. 

Technologies already exist that make it possible for anyone at your manufacturing organization to utilize advanced technologies, including Artificial Intelligence and Machine Learning, in their daily roles. 

Let’s examine how the manufacturing sector reached this point and where it’s heading next. We’ll also show you how you can empower your employees to become Citizen Data Scientists, too. 

Manufacturing’s Talent Problem: How It Started and How It’s Going

Twenty years ago, the first workers to adopt new technologies into their daily life quickly overtook their colleagues. This led many manufacturers to believe that knowledge embodied power. We believed that we were turning more and more to an economy of talent, specifically for digital data tools. Time and time again, a young engineer using Big Data tools with ease often made more progress in the factory than a longstanding expert still utilizing standard processes, tools, procedures, and methodologies.

Companies pay top-dollar for new recruits while simultaneously watching tenured, experienced employees walk out the door.

It’s a tough position for manufacturers in every industry.

Employees with specific technological skill sets and advanced digital knowledge are highly valued and sought after. While this motivated some colleagues to increase their technical skills, others fell behind or lost interest, adding to manufacturing’s ongoing career attrition. On the corporate side, companies go head-to-head trying to recruit top talent who, oddly enough, are likely just graduating from college. 

Companies pay top-dollar for new recruits while simultaneously watching tenured, experienced employees walk out the door: it’s a tough position for manufacturers in every industry. It also makes it increasingly clear that talent is not our economy’s greatest competitive advantage: data is the new power.

Data is the New Competitive Advantage

Data, in the form of business information, is a company’s most valuable asset. When an organization masters capturing and enhancing its data, it finds itself in a position to rapidly gain strategic intelligence and be a competitive force in the marketplace. 

Data helps teams make decisions and changes using facts (note: the data must be valid and usable). Data also makes it possible to prove what changes are working correctly and if they are driving the desired outcome. Data points teams towards the right path, acting as a compass to help them understand what they should continue doing and what they should modify or stop doing. 

In fact, without data, there isn’t a good way to know what changes need to be made first. Teams may set off addressing a problem that is the result, not the cause. It can be a waste of time, energy, and resources to tackle secondary problems instead of the root cause. The right data can help illuminate where a problem originates, enabling teams to focus their efforts on addressing the cause instead of the effect. 

Operators talking with process engineer on a factory floor

This is why so many manufacturers are making the move to Industry 4.0 and undergoing digital transformation. The onset of data transformation usually leads to standardized key goals throughout the company: 

  • collect everything so as to not lose any opportunities; 
  • enhance raw data by recalculating different strategies (via Digital Twins) such as factories, products, and services;
  • benefit from data as much as possible using new applications that offer quick and easy control of Artificial Intelligence and Machine Learning

Contextualized information deserves a standardized process. It is a precious, intangible asset that cannot be understated. So in the context of a company’s complete transformation (including management and information systems) how can we explain the manufacturing sector’s craze for Data Scientists? 

Why In-House Data Solutions Aren’t (Always) the Right Solution

During a time when both intellectual and physical tasks are becoming automated, many manufacturers are still asking talented Data Scientists to build custom in-house data solutions from scratch.  

It is true that there are open-source solutions and tools for creating custom in-house solutions. For example, Microsoft Azure and AWS are accessible solutions for building in-house cybersecurity or network infrastructures. Other Data Scientists at your organization are likely talented enough to manually code AI solutions on a case-by-case basis.

Initially, these solutions can help you solve certain challenges and move the needle slightly. These wins encourage teams that this way of working is acceptable. 

The truth is that these solutions are likely temporary. And time is fleeting. 

One-off internal solutions are often highly customized to specific tasks, making them difficult to scale or replicate for future challenges. A custom-coded solution that works for one of your products, lines, or facilities might not work for others. These solutions are laborious and time-consuming to scale (let alone, build), resulting in ongoing changes for each specific use case. Essentially, the bulk of the work lands back in the hands of your valuable Data Scientists.

These solutions may also not be flexible enough for future challenges. If a production process changes, an in-house solution may need to be modified or rebuilt in order to incorporate a new process. Once again, your Data Scientists are responsible for both building a new tool and using it, putting immense strain on their time and energy. 

Data democratization is nearly impossible under a “built it in-house” analytics strategy.

Data access is still blocked to the majority of employees and limited to just a few specialized users. 

Additionally, these tools are often built by Data Scientists, for Data Scientists. Oftentimes, there are only a select number of employees who understand how to use an in-house solution. These employees become the ultimate data gatekeepers, holding the keys for every discovery waiting to be unlocked at your organization. 

As a result, your company’s data—and hidden opportunities—are trapped in an access bottleneck. All analyses, investigations, discoveries, and possible process changes must pass through a few select individuals at your organization. Data democratization is nearly impossible under this framework, as access is blocked to the majority of employees and limited to just a few specialized users. 

Technology has advanced beyond maximizing open-source data solutions or using Python code for prototyping software applications. These tools alone will not facilitate a complete transformation of a company’s information system. A better solution exists.

Industry 4.0 Tools for Data Democratization

Many companies understand that cleansed, structured, and contextualized data is crucial for continuous improvement efforts. Even so, few companies understand how to get their data to this point, and even fewer companies know how to fully leverage it across the organization.

This is where Industry 4.0 technology really shines. Industry 4.0 tools (particularly, Digital Twins and IIoT Platforms) make clean, structured, usable data more accessible to everyone at an organization. 

These tools gather data from your IT/OT sources and make sure it is usable for analysis. In the case of Braincube’s Digital Twin, data is gathered, cleansed, structured, and contextualized for every individual product that comes off your production lines. This saves your Data Scientists valuable time, enabling them to focus on more complex problems instead of cleansing data for analysis.

Hand clicking on tablet application and opening up to multiple manufacturing icons, showing the power of apps for citizen data scientists

IIoT Platforms take this organized data and make sure that it can get to everyone at the organization in a straightforward, usable way. Instead of navigating massive, open databases, users can easily plug into the data they want to see and use. Everything they need is right at their fingertips, making   

Access and visibility into your operational data is a key step towards moving everyone forward. However, access is only part of the solution. Teams still need a way to utilize data if they are to understand where they can make process improvements. 

What are Citizen Data Scientists?

Just as Data Scientists model and analyze data, your teams need the ability to dig deeper into data in order to uncover new opportunities. After all, this is what it means to be a Citizen Data Scientist. A Citizen Data Scientist is someone who performs moderate to advanced diagnostic analysis by creating or generating models and analyzing that data. 

A key difference between a true Data Scientist and a Citizen Data Scientist is that a Citizen Data Scientist’s primary role (and typically, training) isn’t directly related to statistics and analytics. Instead, Citizen Data Scientists can be anyone at your organization: for example, plant managers, operators, or process engineers. 

A Citizen Data Scientist is someone who performs moderate to advanced diagnostic analyses by creating or generating models and analyzing that data. 

They can bring greater context to analyses because they are familiar with the daily workings and nuances of their factory.

Putting data into the hands of more employees is the same as providing them greater knowledge about their daily tasks. In fact, this is one of the key benefits of Citizen Data Scientists. Since they spend the majority of their time performing other operational tasks within the factory, they can bring this intrinsic knowledge to their data discoveries. They can bring greater context to analyses because they are so familiar with the daily workings of their factory. Over time, they can become power users of advanced analytics tools, typically through on-the-job training and is enabled by easy-to-use technology. One key value of a Citizen Data Scientist is their ability to evaluate those augmented analytical tools with the context of what happens in the factory in mind. 

But if a Citizen Data Scientist doesn’t have a background in statistics and analytics, how can they contribute to new data-based discoveries? The answer: low-code and no-code applications that simplify and streamline analytical capabilities to everyone at your organization.  

Ready-to-Use Low/No-Code Business Intelligence Applications

Forrester and Gartner both estimate that low-code and no-code options will overtake and make up more than half of the enterprise software ecosystem by the end of 2021. This shift to democratization and utilization of data across an organization, regardless of skill sets, demonstrates the proliferation of this new breed of analysts. 

Ready-to-use and no-code/low-code business intelligence applications. These apps perform highly complex statistical and analytical tasks on the backend. On the front end, they present an easy-to-use interface to users. 

Apps also include advanced digital technologies such as AI and Machine Learning. These self-learning technologies learn more about your specific process as teams make changes and guide the algorithms to correct solutions. In turn, AI and ML provide additional recommendations that level up your findings.  

Applications enable you to leverage the power of many minds at work, including those with specialized, intrinsic knowledge about nuanced processes. These tools put powerful solutions into their hands and enable them to use their knowledge. 

This results in a snowball effect of new discoveries. More people have access to the same (accurate) data, increasing the probability of discovering new opportunities and making impactful process changes. In essence, your company can see the Rise of Citizen Data Scientists within your organization. 

Conclusion

In order to guarantee your business’s sustainability, you need to design a solid and efficient information system based on the best digital solutions that are already readily available. This ready-to-use infrastructure and application suite enables the democratization of data across an entire organization, empowering employees to become Citizen Data Scientists. In turn, this enables your entire organization to expedite analyses and optimize processes. 

These solutions guarantee a maximum level of cybersecurity and the absolute integrity of the company’s strategic information. A company pursuing digital transformation must learn from qualified and mastered data to quickly reach its futuristic vision, even before leveraging the power of Machine Learning.

Digital transformation presents an immense opportunity, both for your company and your employees: don’t let it pass you by.  

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