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The Wisdom Hierarchy For Collaboration


The data, information, knowledge, wisdom hierarchy is interesting to me and how we can use this model in collaboration.  One aspect of collaboration is the development of a robust and accurate knowledge base for your employees.  Giving employees a place to go first to take the information they have and make it meaningful, understand next steps and ideally add to the knowledge base with more details for future users.  Being able to access this information through a variety of means becomes more important as users look toward the knowledge base for answers.  Let's look a little closer at the hierarchy oft abbreviated to DIKW.

Data is the most simple form are variables that are quantitative or qualitative.  In a networked world they may come from different sensors and report whatever they're measuring.  Or in a business world data may come from different sources such as employees, news reports, and surveys.  As you can well imagine, data coming from so many different sources means there's a lot of data to be collected and stored.  By itself, data doesn't have much meaning.  For example if I say it's 72 degrees that doesn't mean much unless you have other data points that you can correlate and turn into meaning.  Let's make a list of data points:

  • It is 72
  • The month is July
  • The degree measurement is in Fahrenheit
  • The measurement is outside
  • It is Midnight GMT

These data points don't mean much until we consolidate, correlate, and turn them into meaningful information.  Interestingly enough, this is done by the collection of enough relevant data points.  At this point, we don't even know if the data collected is from the same location, but by adding just a couple more data points we then can understand the scenario:

  • All data points have been collected from the same source
  • The source is at the North Pole

Now that we have data points and context, we have information.  Just like data, this information doesn't mean much until we turn it into knowledge.  Just as we require more data to make information, we require more information to make knowledge. 

By adding more information, such as historical trends, we can then consolidate, correlate and identify anomalies, or just as important identify everything is as expected. If we add more information to the scenario, we get a greater sense of what's going on and what it may mean:

  • In the Winter the North Pole average temperature is -29F and in the Summer is 32F.
  • The North Pole is covered in ice that is floating on the ocean waters.  The ice is anywhere from 7-10 feet thick.
  • The freezing temperature of water to ice is 32F.

We're now in a position to use knowledge to draw conclusions and understand the impact of these conclusions.  As we consolidate data to information to knowledge the relevancy to other pieces of information grows.  The foundation for "Scientia potentia est" or "knowledge is power".  Our knowledge tells us:

  • The northern polar ice cap is currently melting.
  • There will be environmental impacts.
  • This may raise the water levels of the oceans significantly.

Wisdom allows us to apply perceptions, judgements and actions to our deep understanding and knowledge of the situations occurring.  Ideally, if our perceptions are correct and our wisdom optimum, we can take action to better our environment and from our perspective meet business goals.  In our scenario different courses of actions may ensue:

  • Some will look to reverse the abnormally high temperatures at the North Pole and stop the polar ice cap from melting.
  • Some will accept it for what it is and minimize future impacts by enacting policies and processes that limit the exposure of greenhouse gases. If they in fact are the cause of the abnormal temperature increase.
  • Some will purchase landlocked real estate with the expectation it will become beach front property they can then sell for a premium.

Through the collection of a lot of data, we're able to correlate into information and compare to additional information that enables us to use our knowledge of what is and what's supposed to be to draw conclusions that require action based on our learned wisdom.  In this scenario there are different actions depending on what you're looking to achieve, but the one who is wisest with the greatest perception is able to take in all the data, information and knowledge they've amassed and say "Replace and calibrate the sensor, it's obviously malfunctioning.". 

From a collaboration stand point this means your data points must be valid and validated regularly.  If the data is wrong, the information will be off and the knowledge you develop will also be off.  The goal is to build up a knowledge base that is accurate and current then use the collective wisdom in a collaborative fashion to make the best judgement and course of action.  The less time your best and brightest need to concern themselves with the accuracy of the information they have and can focus on what to do with it, the stronger your organization's position will be.  As you're able to automate the collection of data across the Internet of things and make that information available in the proper collaborative environment with the right people, the more you'll be able to take proactive actions and not be reactive to change.

How are you enabling the wisdom hierarchy in your organization?  How are your people best collaborating to take your company to the next level?  What are the tools that you've employed and what tools do you see yourself employing in the future?


Thank you John for your great post! Here, I'd like to know your opinion about a point:

In many organizations, just "data administrator" and/or "database administrator (DBA)" have the responsibility and the authority of data validation; and in some cases the criteria they determine for data validation are not transparent enough to the employees. So, some of these employees cannot make sure about the accuracy and validity of the organizational data and information they are using in their tasks, while, they have no athority of being involved in the related assurance process.

My question is: how can collaboration help addressing this problem? 


It is a challenge and one that's often difficult to effectively address.  Data administrators have the responsibility for assuring the data they hold is not tampered with or modified in any way unless authorized and appropriately logged.  This responsibility may extend to the communications link, ensuring no man in the middle attacks compromise the data in motion.

Anytime data is being collected you need to have trust in the source.  The best way to establish trust is through verification.  Most common form of verification is through redundency.  Having two sensors in the same general area reporting data is not always an option.  This is where collaboration helps.  Collaboration technologies enable verification in new ways.  People and machines can proactively verify a data point that extends beyond a set threshold.  Or, the data store can initiate a query to a local representative when an exception is reached.  People can also use collaboration services such as rating to further establish trust.  Consider reporting dead links on a web page or a simple question "how useful was this information to you on a scale of 1-5?". 

The more collaborative ways of verifying data, the more we can trust the source.  If the data from that source seems amiss, then we need to collaborate across boundaries to either confirm the data as accurate, or determine why it's not correct and resolve it.


Absoluely! Finding and implementing appropriate approaches and mechanisms like the ones you mentioned, redundancy, verification or validation at the sensor level, DBMS-level integrity and security mechanisms (such as triggers), can all contribute to addressing this problem.

Let me, however, remind that in addition to the above ways, there are also some solutions that are more related to the business aspects of the organizational work. As highlighted by Ron Ricci and Carl Wiese in their book "the collaborative imperative," collaboration can play an important role in addressing of the organizational problems. In an open, collaborative, multidisciplinary, and cross-functional enterprise, with an enhanced organizational culture, employees have strong feelings on their work and on doing that in a more effective way. So, they care about the things like correctness, accuracy, and effectiveness of the data they produce or use during the regular tasks in their work. Noting this, if some new ways, along with a number of appropriate incentives and motivations, are established in organizations (and with the support of the collaboration technologies), it would lead to creating more value.

Employees involved in the processes related to the validation, verification, and the integrity of the organizational data they are employing will contribute to the organizational growth a great deal.

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