“Digitalization” is an emerging trend, which represents the integration of multiple technologies into all aspects of daily life that can be digitized. A few examples of digitalization include smart homes (for entertainment, security, childcare, electrical, and heating), e-healthcare, smart mobility, and smart cities. The widespread impact of digitalization affects everything from personal relationships augmented by social media and their services, to other relationships such as how citizens interact with support services in e-government.
Gartner defines digitalization with a more business-oriented focus: “Digitalization is the use of digital technologies to change a business model and provide new revenue and value-producing opportunities; it is the process of moving to a digital business.” (gartner.com/it-glossary in August 2015). This definition spans relationships between different businesses, in addition to business and government, and the vital relationship to customers. A goal is to realize digitalization such that there is a clear relationship between the services offered by businesses and the actual needs of customers.
There are many domains that will benefit greatly from digitalization: cultural artifacts and assets (e.g., artwork, historical relics, and documents) can be digitized and therefore preserved and shown to the masses, even if they were stolen, destroyed, or are just not directly accessible. Scientists can now digitize their experiments, such that their experimental setup and results can be repeated much more easily to allow for further analysis and scrutiny.
Digitalization includes Abstraction
At a first glance, digitalization seems to concentrate on data, such as the trend toward Big Data where large amounts of data are made available through the Internet and analyzable in the cloud. This kind of digital data itself represents a model of the world that it describes. Most of the data collected explicitly describe artifacts of the real world, often in a raw and unstructured format. However, if we want to draw information and knowledge from these data sets (beyond a mere observation), we need to aggregate relevant characteristics and abstract from many irrelevant details to answer general questions. These aggregated abstractions are also models that can describe the shared and thus generalizable phenomena of artifacts, mechanisms, and situations, as well as their individual unique characteristics that allow scientists and the general citizenry to understand specific aspects of the world.
This is true for the world, in general, as well as for individual domains. For example, in the business domains, digitalization often informs what and where to buy and sell, how to advertise, how to efficiently produce and transport, and how to keep contact with the customer. In production mode, digitalization also means to design products in a digital form, to virtually compose and exercise components before producing the product, and to maintain the relationship between a sold or rented product, its users, and the producing company. In water and energy supply, as well as transportation domains, digitalization can inform the status, physical distribution, or position of the things and people of interest, providing analytical capabilities about necessary and future capacities related to sustainability and scalability within a specific domain.
It is obvious that digitized data describe a model that represents a part of the real world. Models will be needed to store knowledge about all interesting things, people, and events and their relationships. Models are needed to describe procedural knowledge, such as calculation algorithms, business processes, critical loads or capacities, and their typical appearance, among many other needs.
Digitalization relies on Models
Although we may agree that models are an important asset for future digitalization efforts, the questions remain regarding where do the models come from and how are they denoted? In the Big Data context, models are extracted from an abundant volume of data that is represented in a variety of formats, where models may not be very explicit. Often, this also means that the underlying modeling language is relatively implicit, even though many modeling languages share a lot of similarities with either state machine for behavior and object diagrams for structural elements and their relationships. These implicit modeling languages will differ in details, because some models will need more expressivity. Other models may explicitly incorporate uncertainty or non-determinism of their underlying data. We think it is necessary to investigate appropriate approaches for language definition to describe the principal and possible forms of the models, which will then describe the real-world assets or derivations of interesting knowledge that can be inferred from the data represented in the models.
“Digital transformation” is meaning the active act of transforming a business, process, or product towards becoming increasingly digital. The phrase is also emerging as a buzzword that allows different stakeholders to inject various forms of innovation into their respective company, business, government, academic institution, or other public services. The nuances of digital transformation are broad and have not yet been defined precisely, but even job advertisements often contain the phrase.
Deconstructing the term from its two primary words, we identify two well-known concepts. “Transformation” describes a general process that starts with some initial situation that moves toward a changed, and supposedly better situation. May be that in this case the word transformation is not the best word choice because the underlying transformation may never meet a stable end, but rather undergo a continual set of evolutionary optimizations related to new forms of business, production, logistics, medicine or other changes within the targeted domain. “Digital” suggests that many changes in society, business and industry will be driven by information technologies that allow data to be processed in real-time and even used to intelligently derive information to finally to provide stakeholders with improved knowledge about their processes and products. Downstream digitization would also allow optimization, automation activities and production techniques of various forms.
We already discussed, models have much potential toward achieving the goals of digital transformation. Below are a few possible contexts for application:
- (M1) One of our colleagues, Ulrich Frank, recently stated that models can be used beneficially to mitigate the differences and challenges that emerge between different worlds that speak very unique languages. This becomes obvious when considering the various stakeholders that come into contact with Digital Products or services. Each stakeholder may have individual domain-specific terms to describe his or her needs, capabilities, and unique information resources.
- (M2) Digital transformation often co-exists with large data sets that are associated with some processing need of the transformation context. Data has structure. For an explicit, well-founded handling of this data, models are necessary to describe the data structure, but also how to manipulate the data and retrieve it efficiently. Transformation models describe how to slice, select, join, or aggregate data to retrieve useful information. Beyond manipulation of data, there is much software that is necessary to handle, manage and visualize this data. In the future, traditional software engineering techniques may use models to design such systems, or models at runtime will describe specific techniques within a rather generic software package (e.g., database, statistics packages or visualization components).
- (M3) The design of digital products and the development of product lines using digital technologies will lead to a very challenging integration problem for the physical components of a system, as well as the development methodologies and their tools. Many of these tools use very specific forms of models, written in proprietary or semi-standardized modeling languages, that will need a syntactic, semantic and tool-based integration.
- (M4) While traditional engineering uses human-generated models to prescribe aspects of the system under development, machine learning and data mining techniques have the potential to reverse this relation by extracting models from sets of running data. It will be interesting to see how prescriptive and extracted models fit together, if at all.
There is a deep list of research topics that need to be explored in order to derive the understanding that will bring a pure data-driven world together with prescriptive design models. Particularly, the application of machine learning currently too often relearns already well-known models, because prescriptive models and machine learning are not well integrated. Colleagues recently applied big data analytics in a larger industrial project that at first and foremost re-uncovered basic physical laws from a set of production data. While this result may be interesting from a machine learning perspective, the end result was not very helpful given the physical laws were already well-known.
Digitalization relies on Model Standards
Currently, several standards for describing various physical or behavioral assets of the real world are in development. Such standards contain a meta-model that shapes the models allowed by the standard. For example, buildings are described through building information models using one of the various BIM standards. Furthermore, models of electrical circuits as well as mechanical machines and business processes are standardized in a series of partially still-evolving standards. However, these standards are now becoming digitalized, allowing the models to be accessible in digitalized form to analyze, synthesize, evolve, compose, and compare them before buying or developing the product.
The modeling community needs to embrace the move toward digitalization by helping in the definition of new and evolving standards. The modeling community also needs to help in shaping the tooling infrastructure for these standards, such as the creation of meta-models for specific domains of the digitalized world. We firmly believe that all the effort that the modeling community has undertaken to define modeling languages, semantics, and pragmatics, to understand what makes a good model, and to analyze, synthesize, compose, merge, slice, and transform models, will give us a great opportunity to contribute to the digitization of the world and its assets.
[GR17a]In: Journal Software and Systems Modeling (SoSyM), Volume 16(2), pp. 307-308, Springer Berlin / Heidelberg, 2017.
[GR15b]In: Journal Software and Systems Modeling (SoSyM), Volume 14(4), pp. 1319-1320, Springer Berlin / Heidelberg, 2015.