We are currently at a significant juncture in regard to the manner in which companies orchestrate the work. There is little doubt that there is a general agreement on this point; much ink was spilled exploring ways of working, great resignation, organizational resilience, and other contemporary challenges. But today, I would like to delve into two specific trends and discuss how they intersect.
One of the emerging trends is the democratization of various engineering, advanced analytics, and other intelligent solutions through the use of low-code and no-code platforms. This is driven by the shortage of highly skilled experts in data analytics and a growing demand for these capabilities in companies. This has led to the concept of "citizen development" and "citizen data science", where non-technical employees are empowered to perform tasks that were previously only in the domain of highly skilled engineers [1]. According to Gartner, a "citizen data scientist" is defined as someone who creates or generates models that use predictive or prescriptive analytics but whose primary job function is outside of statistics and analytics.
Another trend that is on the rise is the growing complexity of operations and the increasing competition that is pushing companies to become more efficient. As a result, there is a growing number of solutions available to address operational challenges, ranging from well-known business process management solutions to process mining and robotic process automation. We should also include in the list another type of solution, such as operations digital twin, a type of digital model of a company's processes and operations required to produce value or deliver a service. The key benefits that the operations digital twin can provide to companies are:
- Describe what happened with operations.
- Diagnose why a bad thing happens (ed) with operations.
- Predict with high accuracy what is going to happen with operations.
- Prescribe what action should be taken to address identified issues of operations.
Historically operations optimization, operations research, and digital twin solutions were very engineering-centric, often requiring advanced degrees in mathematics or operations. However, similar to data scientists, experts in operations are in high demand and are difficult to find. This creates a supply and demand imbalance and drives innovation towards democratizing operations analytical capabilities. As a result, the solutions that were once only accessible to a select few are now becoming more widely available and user-friendly for all. This allows companies to make data-driven decisions and optimize their operations without having to rely on a scarce pool of experts.
We can find indirect substantiation of the tendency through the predictions of the digital twin solutions evolutions [2], I would like to linger on 3 of them:
Generative AI meets digital twins
Generative AI techniques, such as ChatGPT is being developed to work with digital twin models, allowing not only the shape of things to be described but also how they work. The democratization of this technology will create opportunities for digital twins and simulations to expedite the implementation of operations digital twin when the data is not clean enough or is not available so it is possible to synthesize it given the behavioral description. This generated operational data can be used to train, and process robotization solutions, complex computer vision, and recommendation systems.
Digital twin ecosystems open new use cases
Matt Barrington, emerging technology leader at EY Americas, predicts that digital twins will be at the forefront of this transformation, revolutionizing how companies make strategic decisions and manage risk. Digital twins will support the management of new products, supply chain resilience, and overall safety and sustainability. Of course, achieving this transformation will require companies' investment in foundational digital capabilities such as data management and DevOps for data engineering, as well as taking a comprehensive approach to security. However, this will also definitely require changes in the talent structure toward the notion of citizen development when regular employees can embrace the power of decision-making leveraged by new categories of low-code and no-code solutions.
Enterprise digital twins take off
As Bernd Gross, CTO at Software AG points out, process mining and process capture tools have advanced to the point where they allow enterprises to create simulations for entire departments or clusters of business processes rather than just a single process. This ability to create a more complete and accurate digital twin can help leaders make better decisions and drive more accurate outcomes by incorporating various technologies such as process mining, risk analysis, and compliance monitoring. However, creating a truly accurate enterprise digital twin requires a significant amount of data, including relevant KPIs, causalities between processes, and the life cycle of a business unit. This is where citizen development can play a critical role in helping companies collect and interpret the data they need to build their digital twin. By empowering employees to develop their own solutions and automate their own processes, companies can ensure that they have the breadth and depth of data they need to create a truly accurate digital twin.
Summary
The rise of citizen development is aimed at empowering non-technical employees to perform tasks that were traditionally done by highly trained personnel engineers, analysts, or data scientists. This kind of flexibility and teamwork builds camaraderie and satisfaction as individuals work together toward a shared goal, making the organization more agile and resilient to various crises.
We believe operations digital twin combined with citizen development will play a key role in transforming the way companies run their business operations, providing new horizons to optimize operations and increase efficiency.
