What is the point of data without knowledge?

Data surrounds us in everything we do. However, without further analysis - it is useless. The numbers 2 and 5 are data, but on their own - they mean nothing. Perhaps it is the number of items in my online shopping basket, if so that means a little more, especially if we know whether they are bikes or bananas and whether I have bought them before. Knowledge is the output of the analysis of data and that is where I believe the true benefit lies. And, if knowledge is power then shared knowledge is shared power, and that is where the real fun begins. 

Max Bowyer

Data vs. knowledge

The dictionary definition of data is, ‘facts and statistics collected together for reference or analysis’. Knowledge, on the other hand, encompasses facts, information, and skills acquired through experience or education. Knowledge ranges from analysis to insights leading to effective decision making.

We now live in the digital age. We have mobile phones, email addresses and store cards that continually generate data across a multitude of behaviours. Data alone is insufficient, however once converted to knowledge there is a massive amount of value to be gained worth many millions in the market place.

Knowledge generated from store card data leads to very tempting offers that are personalised based on previous purchases. We are provided recommendations based on data mined from our browsing history. We are now receiving localised offers in stores based on the data generated by our phones as we walk around. 

But this knowledge is not just relevant on consumer basis, organisationally, there are many benefits to be gained by exploiting available knowledge.

Knowledge in the workplace

Just as data is generated by the devices we use as part of our daily routine then employees as part of their daily activities also generate data. As we all work on projects or operational outputs we are conveying snippets of what we know in emails, documents and tasks we work on. 

Quite often simple questions starting with the words “do you remember…” or “have you got…” can lead to a lengthy search across email, hard drives, web portals and even what is left of paper folders. This takes time and does not always come up with the right result. 

This is mildly annoying when looking for a process diagram that would take an hour to recreate. However this becomes massively important when trying to resolve a system failure that “Bob fixed last time”, especially when Bob is no longer around. 

As we move further into the digital age we have a greater reliance on converting corporate data into knowledge for retention and reuse. The digital age surrounds us with data from every direction including personal data, cloud data and even big data. Corporate knowledge and Organisational Memory does not just appear out of all this data. Knowledge develops over time, needs to be managed and continually updated.

Knowledge development

There is a clear structure as to how knowledge is developed that was clumsily stated by Donald Rumsfeld at a  Department of Defense news briefing, 12 February 2002.

“As we know, There are known knowns; there are things we know we know. We also know, there are known unknowns, that is to say, we know there are some things we do not know. But there are also unknown unknowns, the ones we do not know we do not know.”

Rumsfeld wasn’t wrong in what he says, in this quote he references the essential stages of learning we as humans, operators, workers, and leaders go through in order to develop knowledge. Although the stages were not mentioned in the right order. These levels of organisational learning, also known as the four stages of competence, apply to individuals and organisations on a regular basis in the following way. 

Imagine being asked to ride a bike for the very first time, when you did not even know what a bike was? Where would you start? Well there are four stages of awareness and competence that relate to this and any other developmental processes regarding knowledge acquisition as illustrated below.

data vs knowledge diagram

The small pieces of data associated with the skills for bike riding (balance, pedalling, steering and stopping) all contribute to knowledge acquisition. So now you can ride a bike it has become natural and does not need thinking about. Even so, Learning still progresses and skills are developed almost unconsciously. Showing others how to ride a bike the same way will reduce the learning curve (and maybe less falling off) rather than them having to learn the whole process in isolation. This is an aspect of sharing the knowledge. 

Knowledge development occurs in exactly the same way across the work place across all industries including leveraging data from email, social media in addition to specific project or operational deliveries. Knowledge shared amongst employees is an essential part of organisational risk management. Clear examples of this are: 

  • Having easy access to the right knowledge will reduce systems outage when Bob is not around. The benefit would be to save time, money and reputation that would have been caused by outages 
  • By being able to share knowledge across the organisation there will be plenty of competent cyclists. The benefit being that by capturing the right level of knowledge there are many who are able to develop their skills.

To achieve this, there needs to be structured development of organisational memory with access to pockets of knowledge management using appropriate tools. It is not enough to simply harvest the data as there is no added value (e.g the number 5). There must be a discipline of converting the data to knowledge. Whilst having access to a complete customer shopping history by associating purchases with a store card then personalised offers and promotions can be delivered whether it be items relating to bikes or bananas. The emergence of big data provides more scope for delivering knowledge on which to build business decisions. The continuity of knowledge management must be encouraged through organic growth and behaviour changes to ensure knowledge capture and delivery becomes a natural part of delivery.

A key difference between knowledge and data is that in the digital world, with too much data then it really is a case of not knowing what you already know as the relevant detail is hidden. Providing there is appropriate capture, retention, association and recall then you cannot have too much knowledge. 

As with anything these days, change is inevitable and whatever your approach for knowledge management, your solution must be dynamic to keep up with the tides of change. Through delivery of a robust knowledge solution, knowledge holders within an organisation must be able to access and contribute (and even collaborate) to the organisational memory banks within the organisation as a location-independent way of working to continue to deliver effectively.

Max Bowyer is a specialist software testing and business change consultant with over 20  years’ experience across  Technology, Retail, Government, Supply chain and finance  industries. 

To find out more about Amsphere’s business analysis services or change management support, please get in touch: info@amsphere.com.