Knowledge a Key Component of Modern/ Future Operational System: "Data vs Information vs Knowledge Understanding the Difference"

 

For the last couple of weeks,  I have discussed in this blog the concept of “expertise, knowledge,” how to multiply this, and why harnessing and bringing knowledge as a natural part of your Operational Architecture is key to dealing with the changing operational human assets due to age, culture, length of time in role, digital.

I was at a customer on a white board and discussing some of these concepts, and I realized that there was confusion between DATA, INFORMATION and KNOWLEDGE.

Sitting over coffee afterwards I agreed that this was not uncommon, and actually comes down to the fact that many people have not had the time to think through what does their operational landscape look like in 2020. That afternoon I was reviewing another customer in Europe vision, and they had a set of functional maps relative to activities on how they expect to operate in 2020 and what is needed, and reflecting on this knowledge is the foundational part built out of many parts.  

I was reading this article from Tom Davenport at Harvard on how knowledge workers collaborate (his book is called Working Knowledge)

, thought it is worth bringing up in the blog I have extracted some points from an article  "Working Knowledge: How Organizations Manage What They Know" By Thomas H. Davenport and Lawrence Prusak

 

   

Firstly this comment on why knowledge is key:

“Knowledge, by contrast, can provide a sustainable advantage. Eventually, competitors can almost always match the quality and price of a market leader's current product or service. By the time that happens, through, the knowledge-rich, knowledge-managing company will have moved on to a new level of quality, creativity, or efficiency. The knowledge advantage is sustainable because it generates increasing returns and continuing advantages. Unlike material assets, which decrease as they are used, knowledge assets increase with use: Ideas breed new ideas, and shared knowledge stays with the giver while it enriches the receiver. The potential for

new ideas arising from the stock of knowledge in any firm is practically limitless -- particularly if the people in the firm are given opportunities to think, to learn, and to talk with one another.”

 

Then the difference between Data/ Information/ Knowledge.

 

Data is a set of discrete, objective facts about events. In an organizational context, data is most usefully described as structured records of transactions. When a customer goes to a gas

station and fills the tank of his car, that transaction can be partly described by data: when he made the purchase; how many gallons he bought; how much he paid. The data tells nothing about why he went to that service station and not another one, and can't predict how likely he is to come back.

 

Unlike data, information has meaning -- the "relevance and purpose" of Drucker's definition. Not only does it potentially shape the receiver, it has a shape: it is organized to some purpose. Data becomes information when its creator adds meaning. We transform data into information by adding value in various ways. Let's consider several important methods, all

beginning with the letter C:

• Contextualized: we know for what purpose the data was gathered
• Categorized: we know the units of analysis or key components of the data

• Calculated: the data may have been analyzed mathematically or statistically
• Corrected: errors have been removed from the data
• Condensed: the data may have been summarized in a more concise form

 

Knowledge derives from information as information derives from data. If information is to become knowledge, humans must do virtually all the work. This transformation happens through such C words as:

• Comparison: how does information about this situation compare to other situations we have known?
• Consequences: what implications does the information have for decisions and actions?
• Connections: how does this bit of knowledge relate to others?
• Conversation: what do other people think about this information?

The article goes into a lot more depth, but I think it is this transformation from information to knowledge that is key as it enables the all important action to be taken. Too often we get caught up in hype and miss the reason why we are capturing data and information it is really to enable faster, and more efficient decisions and actions.

So this leads to the concept of “Intelligent work” which has the information in a knowledge form, with experience and associated actions, the leaders will design their systems to centered on this approach.  

 

Information vs Data Leads Discussion on the Future of Operations!

“Gather all the plant data and analyze afterwards” are common words you hear about the market, but when the discussion happens this approach a “putting head in the sand” approach, with limited bigger picture consideration. Today the key to agility is empowerment of decisions and actions in the NOW. This does not require data it requires trustworthy, in context information.  The last couple of weeks has enabled some fascinating and productive engagements. In a discussion,  last week at a Mining Thought leadership on the future a sizable group of interested people attended and took part in discussions.  

A key concept of “mine of the future” and actually for most industries oil and gas, power, food etc. is the agility to take more holistic operational view of the system and day to day operations. This requires alignment at 3 loops (the diagram illustrates these loops) of operations with the alignment in decision and actions. Foundational to this is the information that decisions are based on, requires not HISTORIANS but Plant/ Operational Information Systems, that align information and actions for effective use my different operational roles. Companies that make this foundational move will have a system where data structure, validation is “managed” not coded, that the system is trust worthy so people will depend on and use the system.

To many times the discussion with mining and process end users who are implementing an information system for increased decisions support, that they require a re look at the data sources and how to put it in context, and validated. An example of this was a coal company in South Africa they had spent significant time working on an information system and the historians and data warehouses, but lost effectiveness through:

• Data alignment across sources, E.g.| Finding different data streams that effected the same asset calculation for say energy.
• Data validation
• Data structure

The conversation remarkably quickly ended up going back to redo of the structure of data coming into the historians, and getting this data structured, validated before it went into historian. They had two choices either going to source in this case PLCs and adjusting the running code (not a smart idea), or put a structuring layer in which would structure the data, validate the data, and provide high availability and single names space to manage over the distributed historians.

This is a departure from the story he was told that just put a historian in and capture the data worry about analysis afterwards, that is old and in effective saying. As discussed in the “mine of the future” discussions  the challenge is to “federate” the existing data sources on a plant, E.g.| Historians, alarm event data logs, operator logs, and delay, downtime data bases, E.g.| The alignment of the data sources into effective information that actions can be taken. The concept of “self service” becomes necessary, as there was a lot of comment around of trying to minimize the process analysis phase and role in the data, and try to get effective information to operational people quickly. Another example is an oil and gas plant’s decision system that is effectively been run 24 hours out of phase with the plant, by only have decision able reports/ dashboards from the past 24 hours at 2 to 3 in the afternoon so. Again this delay was due to data gathering, data alignment, validation, in MS Excel manually done by 3 people, the company was exploring ways to eliminate this manual creation, so the whole process is “near real time”.

A clear message from the last couple of weeks is we need to step back, align the existing systems, to provide that key foundation for operational empowerment, absorb significant milestones such as advancement in the communication infrastructures; Example putting 4G communications in the Pilbra mining area (remote north western Australia), providing significant data capability. (A topic for next week). This need to absorbed into the industrial / operational Architecture, internet will be a natural part of the backbone, leveraging computing power remotely for functions, such as storage, analysis, model running, help accelerate the decision support.