Creating trusted data is at the core of Parasyn’s purpose and passion. Trusted data creates certainty for data consumers and the recipients of decisions and outcomes relying on good systems.
What is trusted data?
The dictionary states that trust is a firm belief in the reliability, truth, or ability of someone or something. Relations have to be built on trust. A further extension to this is the acceptance of the truth of a statement without evidence or investigation. Data becomes trusted when data management is mature, i.e. when we know we can rely on the data because the data is managed effectively.
How does “Trust” apply to process data?
In application of the definition above, trusted data may be developed because an individual or organisation has confidence in the system and the source of information that feeds the system. A system includes the people, technology and infrastructure. Acceptance of the technology is paramount in any data system. This can be achieved by a process of verification. Acceptance, however, may be less scientific and be based on intangible criteria like industry acceptance or strong branding. Unfortunately, the trusted data element of digital transformation is usually the least most understood aspect of the system due to is variability. Not wanting to deal with this variability, platform providers sometimes limit the scope of what their front-end data systems are suitable for or ignore it altogether. Understanding what that really means is a highly technical and requires expert advice.
Despite bias on the technology stack and infrastructure, the people aspect including re-tasking to activities that only humans can perform, change management and performance analysis is critical. Humans are required to interpret process performance and if the system is doing its intended job. Complex data systems should be designed to simplify how humans interact and operate the applied technologies.
How do we create Trusted Data?
Data management is a methodology that humans can rely on to produce a level of certainty about the results. The better the data management and its communication about its performance the more trust that is developed by its users. A good platform may be implemented but will under deliver if the system does not model the “real world”. The data collected about the system must closely match the operational profile of the infrastructure and ideally this is done in real time. The system must cater optimising the operational model. It must be providing reliable and accurate data which represents how the infrastructure is currently operating and how it has performed in the past. The system should provide an environment that is open to other smart systems for future proofing the environment. The success of most digital transformation initiatives hinges on the maturity of the organisations approach to managing their existing data and their acknowledgement of the value of their current data sets.
Why Digitise a system or a business?
When we digitise systems, often the data quality initially appears to be worse. The formalisation, normalising and standardising of existing data exposes the organisation’s previous approach to managing information. This is usually not good news. Shortly behind this revelation comes the realisation that it is going to be expensive to standardise everything. One strategy is to reach a point where the existing data is “valued”. There is “nice to have” data, “good to have” data and “essential to have” data. Only the organisation can intimately value its data. No external party should independently set the value of data to an organisation. The data consumers already know data value even if they are using paper systems or antique “digital systems” which have been working for 30 years! They know best. In terms of new initiatives and new practice, perhaps this is the place where external consultants add the most value including categorising data types for the crucial decision-making process. Starting with “essential data” is a wise approach to measuring the cost of transforming to a new way of managing assets without paper. You might be saving the planet by consuming less trees but it won’t be very useful if parting with old ways of doing things cannot be surrendered because New is not as good as Old.
Why do we need better systems?
In general terms, better systems is about simplifying and improving how we manage something. This could mean less routine activities for humans, increased autonomy for humans who can be reassigned to more important tasks and better information to base strategies upon. When applied to Trusted Data and Industrial Automation, we are implying moving forward in terms of speed, data accuracy, sampling period, reliability and repeatability, ease of consumption, ability to contextualise data including its inherent quality and the list goes on. Today, we can have better systems, and we do, and we can move onward and upward if we understand at least in principle the historical challenges of past systems, so then mistakes and in particular poor data capture are not replicated into the future.
Why is trusted data integral to better systems?
Better systems are tightly coupled to better outcomes and wider thinking. Trusted Data leading to Better Systems is a feeder for artificial intelligence and other applications yet to be conceived. It cannot be overstated that the best application is useless with poor inputs, synonymous with a race car using low octane fuel or a computer with no internet connection. When a system is designed with good data management, the capability is expanded because its limits are known, it is deterministic, and the reliability is well established. This is the perfect base for improved understanding (learning), growth (expanding capability) and future proofing (business continuity). You are then ready to take advantage of the new technologies without limitations.
