The Data to Wisdom Pyramid

“In God we trust; all others bring data.”
– W. Edwards Deming

Table of Contents

Data is the lifeblood of the company. Every day people make decisions that impact the reliability of our plant. They make decisions based on the information they have available. The quality of the information is directly related to the underlying data that is collected in the company’s systems and directly affects the quality of these decisions. The quality of day-to-day business decisions has a real impact on the company’s business performance and long-term success. Therefore, the quality of the data collected is very important, but it is often not given the attention it deserves.

The DIKW pyramid is a concept that helps explain the transformation of raw data into information then knowledge and finally wisdom or decision support.

 The quality and quantity of the raw data collected is at the foundation of this pyramid and directly affects this process. If we collect inadequate, poor quality data, then we can expect to transform this into poor quality decisions for the business. Furthermore, we will be unlikely to measure the results.

 If we focus on collecting a set of consistently good quality and comprehensive data, we can provide the business with precise decision support systems that can guide decisions with a real impact on business results. Besides this, we can accurately measure the results and accelerate the decision achieving results and stop the actions which are not.


Data can be described as discrete objective facts or observations. Data in its rawest form is unorganised and unprocessed to have little or no meaning. It has no context.

Small items of data can make a big different in decision support. A flag not checked to indicate that the work is a follow-on from a PM. The asset reference selected on a work order is has a system instead of specific asset level tag.


Information is inferred from data. Information is data put into a context and therefore has a meaning or purpose. It’s used to answer questions (e.g., who, what, where, how many, when).


  • The criticality rating of an item of equipment.
  • The date a defect was raised in the systems.
  • The number of failures of an item of equipment over the last year.
  • The number of man hours booked to a work order by a craft to carry out a maintenance activity.
  • The cost per craft hour
  • The cost of spare parts used in maintenance repairs
  • The value of production losses resulting from an item of equipment’s downtime.
  • The name of the person who raised a work order.
  • The volume of production losses related to the failure of an item of equipment.
  • The root cause of equipment failures.

Knowledge and Understanding

Knowledge is the result of the interpretation and framing of information. Knowledge is gained through applying framed experiences, contextual information and expert insight into information. It can be further enhanced by adding incorporating human intuition as it originates in the mind of those interpreting the information. This is when it becomes deeper understanding.

If information is described as “organised or structured data”, knowledge could be described as:

“synthesis of multiple sources of information over time”

“organization and processing to convey understanding, experience [and] accumulated learning”

“a mix of contextual information, values, experience and rules” (10)

Knowledge shared across multiple people represents shared understanding within an organisation or group. This is vital for collaboration and a foundation for a learning organisation.

There is often an iteration between knowledge and information and even back to data. More data and information may be required to develop a deeper understanding of why something is the way it is. This data is then transformed into the information and then added to the body of knowledge before finally being used as support to drive decisions on actions that will improve future performance.


The cost of carrying out corrective maintenance on the power generation systems over the last 3 years was £1.2 million. The production losses due to power failures were £10.5 million. The total cost of equipment failures was £11.7 million. There were 11 unplanned breakdowns of this system over that period of time. The mean time between failure (MTBF) is 99 days; this is below the target of greater than 1 year. Planned maintenance compliance for the power generation system is 99%.

The planned maintenance strategy is not preventing failures of the power generation system. These failures are resulting in a business impact increasing repair cost (OPEX) and reducing revenue.

Wisdom (Decision Support)

Wisdom is understanding how to effectively use knowledge. It is concerned with knowing what to do next, what actions to take. Wisdom is knowing the right things to do.


The technicians captured the failure modes of the components that failed on the power generation systems in the CMMS. The root cause of each failure was captured; however, this was a free text summary of the finding of the RCA studies which were carried out. There was no codification of these causes and they were not split into the physical, human and systemic causes.

An engineer allocated the root cause codes to each of the 11 failures based on the information already written in the RCA summary. This provided additional information.

The information highlighted that 7 of the failures were related to sticking or excessive wear. This was because of poor lubrication practices and technician understanding the lubrications required and how to correctly store and apply this lubrication. The cause of this was a failing in the competency management system.

A decision was made by management to role out a program of lubrication awareness and training sessions, starting with those working on the power generation systems. The lubrication schedule for each item of critical equipment was created and lubrication tags were attached to each item of equipment to look when it was last lubricated. The competency requirements for technicians was updated to specifically add lubrication awareness as a requirement.

Additional fields were added to the CMMS to store the physical, human and systemic causes of the failures of future events. These are dropdown data lists for each cause type. This has captured additional information.

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About the author 

Jason Davidson

Jason Davidson is the founder of He is a maintenance and reliability engineer who uses Power BI every day to create dashboards that help him and others make better decisions. In his spare time, he likes running and has recently got into photography. He also loves spending time hanging out and larking about with his two daughters.

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