Insights Blog
Transforming Data into Digital Assets
In 2020, "Digital Transformation" and advanced manufacturing strategies like industry 4.0 are top of mind for everyone in the process industries.
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- It is transforming how we look at plant operations. It is impacting how we work and collaborate.
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- It is creating demand for new skills in data engineering and data science.
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- It calls for new sensors, edge data processing, new technologies and better data management practices.
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- It is changing how business processes are designed and run.
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- It is causing adaptive changes in information systems across the value chain of the enterprise.
It has also become a forcing factors for companies like Pimsoft to innovate in the domain of OT data management which is the driving force behind the development of the SigmafineHub.
The ripple effects of Digital Transformation have just begun
The good news is that users of Sigmafine have been on this journey since the mid 90's. They know that the first challenge we face in decision making and performance improvement in the process industries is the transformation of raw data and events originating from sensors, manual data entries, control systems, LIMS, material movement systems, and other disparate systems into trustable Digital Assets we can easily manipulate, analyze, share, distribute, reuse, etc. to drive a wide spectrum of business and operational processes.
According to futurologist Alvin Toffler, the information age began somewhere in the mid to late 50's when the number of white-collar workers started to exceed the number of blue-collar workers. Therefore, we are entering the 7th decade of technological progress during which we experienced many disruptive changes.
Because of where we are in the information age, the work Sigmafine and Sigmafine users have been doing for years can now rightly be called "Transforming data into digital assets".
The output of Sigmafine, known as "Cases" is nothing other than a dataset which embeds knowledge (model and engineering principles) and is the result of an analysis (checks & balances) done by the Sigmafine software aided by the users.
This output is a Digital Asset in its own right. Why are we saying this? Being digital refers to data and information stored digitally. Being an asset refers to having control of the data and information to produce positive economic value. Being a digital asset consequently refers to data & information we own and control and from which we can generate sustainable and tangible benefits. This has been the job of Sigmafine and its users for almost three (3) decades now.
Given where the industry is today and the real-time nature of process and manufacturing operations, the generation and management of such digital assets must evolve to face new challenges like:
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- Being more sustainable and reliable so digital assets can be trusted by users, other systems, and business process.
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- Embedding enhanced self diagnostic capabilities to avoid the downside of measurement degradation as well as capture the upside of improving measurement systems and data management practices.
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- Becoming more automated when there is no dependency on manual data entries and questionable data sources.
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- Supporting better and seamless collaboration between the professionals responsible to curate and condition data (data engineers) with the professionals responsible to use digital assets (data scientists, subject mater experts, equipment specialists, etc.) in advanced manufacturing strategies.
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- Facilitating the reuse and repurposing of datasets into new digital assets to reduce the marginal cost of curiosity and discovery and accelerate innovation and continuous improvements.
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- Expanding the modeling options to address new use cases beyond the classic conservation principles typically used in data reconciliation to this day.
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- Provide traceability of information through auditing of changes
The vision for the next generation Sigmafine, known as SigmafineHub, is to address these challenges while preserving the investment of its users in the codification of operational knowledge in models and to lower the barrier to the adoption of the Sigmafine technology for new users and for solving new use cases.
In 2017, it was estimated that 50 % of the digital transformation initiatives in the process industry were data driven[1] which means that massive amounts of data must be turned into usable digital assets. That pressure has only increased and will continue to increase in the foreseeable future.
This shift also means that IT and OT professionals and data scientist must work together, understand each other's requirement for transforming data into usable Digital Assets putting major emphasis on collaboration.
Today, we are introducing SigmafineHub which is the foundation for the development of Sigmafine in the years to come. The launch of SigmafineHub will be done in waves over the next few years and will manifest itself in a series of webapps and core infrastructure components enabling SigmafineHub to fit in place in the dematerialized and virtualized data processing and management environment of customers.
We welcome you on this new leg of the Sigmafine journey in transforming data into digital assets.
Trusting Data for Action
In process and manufacturing, information systems should be implemented with a system of checks and balances to maximize the quality of data and the economic potential of data. Creating an environment where data can be trusted is not an afterthought. Data quality, similar to product quality, needs to be built into our business and data management activities so that we can trust data for action at all times.
Defining Data Quality in the Process Industries
Data Quality is an abstract concept until we are confronted with bad data or information which is not usable, not credible, not presented correctly, not accurate, etc.. Then Data Quality becomes a very concrete experience. While, there are many definitions for Data Quality, all of them converge to the same focal point. “fitness for use” by people, applications and business processes.