How data can create knowledge and wisdom to improve work processes

The goal of every organization is to make work processes more efficient, lower costs, operate assets safely and reliably while being able to demonstrate compliance to regulations.

Many organizations already have lots of data about their assets. What matters though is the ability to find the right information in the data to achieve the goals mentioned above. The problem is that often this information is hard to find since the data is located in isolated silos and available in multiple formats. Even if you can find the data you are looking for, it might be difficult to determine if it is actual and validated and if it’s representing the “as-is” state of your physical asset.

Data is often located in many different sources: paper archives, local databases, Microsoft Excel lists, legacy systems, 3D models, point clouds, etc. To make it even more complicated, every internal discipline or department often has their own set of data that is not shared with other departments in the organization.

How can this unorganized data be transformed into information, knowledge and eventually wisdom to make it a true asset for the organization?

From data to wisdom

The DIKW (Data-Information-Knowledge-Wisdom) pyramid gives a hierarchical presentation of the relationships between data, information, knowledge and eventually wisdom. Data becomes actionable information once we structure, organize and link it. This provides the basis for creating knowledge, and ultimately wisdom.

The start: from data to actionable information

The answer to how to make data a valuable asset is to create a digital twin: A virtual representation of the physical asset. A single-source-of-truth containing actual, validated and actionable information. This digital twin is the collaboration platform for all disciplines in, and outside, the organization.

Bringing all the available data into this digital twin will allow structuring and linking the data so it becomes information. The digital twin solution should make use of a data-centric approach; unlocking the content from the different documents and data sources and link it by making use of logical relations. This will allow a consumer of the data to find what is needed based on what is known. For example, if you know the name of an equipment it is easy to find it by name and navigate the relations to the related documentation about this equipment.

A data-centric environment will allow an organization to not only create tag to document relationships, but also relationships to plant break down structures, classifications, models and assets. And not to forget relationships to work packages, maintenance procedures and operations manuals. The data-centric model allows you to quickly find what you want by starting with what you know!

From information to knowledge and wisdom

The second challenge is leveraging the information and putting it into the context of work processes. This will create knowledge to make the processes more efficient. Automated work processes in the digital twin are used to create a link between the work process and the related technical information. A straight forward example of this is a review and approval workflow. In addition, more complex work processes, such as management of change and handover, will benefit of this approach as well.

Organizations want to avoid reinventing the wheel over and over again. This can be avoided by using standards in the digital twin, as all the information will be structured according industry standards. There are multiple benefits to this approach – for example, in the process of handing over information, standardization will help to provide better knowledge about the quality of the information handed over. If it turns out that the information handed over is incorrect, appropriate measures can be taken timely to prevent running into surprises later on in the project.

Analyzing the information and combining it with the knowledge will bring an organization wisdom to take better decisions. Status reports and gap analyses will provide wisdom to improve work processes and to become pro-active instead of reactive. Examples of this include impact analyses to manage changes requests and managing non-conformities in a project.

Digital Transformation

This process is also often referred to as digital transformation. This digital transformation often has a three-step approach. Which steps to include depends on the quality of the data available data in the organization. The first step is digitization: making the data available in digital format. The second step is digitalization: the process of linking the data so it becomes (actionable) information. The third step is digital transformation: leveraging the information in the context of day-to-day work processes.

In my next posts I will provide more details about the individual steps described above. In the meanwhile, if you are curious to learn more you can send me an inMail, or visit our website for more information.