• David Griffiths

Six ways to evaluate your data to knowledge flows

As a Knowledge Manager, what can you do to monitor and evaluate your data to knowledge flows, and what actions will you take to influence and positively impact these flows? This challenge came up during my recent keynote at the UK KM Summit. In response, and to help stimulate your thinking, I'm going to introduce you to my 6Qs and 6Vs of data to knowledge.

The opportunity exists for Knowledge Management to influence the acquisition, sharing, application and development of data to knowledge (D2K) flows to accelerate sensemaking, problem-solving and decision-making in your organisation.

To achieve this, Knowledge Managers need to monitor and evaluate internal and external data to knowledge flows, something I have been helping Knowledge Managers to do for the last 12 years. At its heart, 6 Vs drive the way I benchmark organisational data to knowledge flows, and it is an approach you can quickly adapt for use in your organisation.

Before the 6Vs of data to kowledge come the 6Qs

Before getting to the 6 Vs of data to knowledge, you first need to consider that data collection starts with intent (i.e. the reason for the data collection). People drive that intent, which brings conscious and unconscious bias into the collection process that you can begin to expose and understand using the 6 Qs of data to knowledge: who, what, where, when, why and how?

More than this, questioning the data can develop profound insights into the underlying beliefs, values, attitudes and standards of your organisation - you might find this story about gender bias in Amazon interesting

In understanding intent better, you can use the 6 Vs to deep dive into your organisation's data to knowledge flows.

The 6Vs of data to knowledge flows

Visibility: Example | where is the data, who owns it, who can see it, and how accessible is it?

Velocity: Example | the currency of data can perish in fast-moving environments, how quickly does data become visible to decision-makers?

Volume: Example | is there sufficient volume to provide an appropriate level of confidence or certainty (e.g. confidence level and confidence interval) in the data?

Veracity: Example | what is the level of credibility, transferability, dependability and confirmability of the data, and what bias is attached to the acquisition and sharing of the data (e.g. dynamics of politics and power)?

Variety: Example | does the data have the requisite variety to represent the knowledge domain in which the decision or problem is situated (simple, complicated, complex)?

Value: Example | what is the currency of the data, and how does it inform influence and impact? [see my blog on how the best report on the value of KM]

Additional tip: If data is created with intent and exists as a means to influence problem-solving and decision-making within the organisation, then you need insights into its influence. For this reason, I find it useful to include data as a node in organisational network maps as this develops insights into the positive or negative impact of a given data set on decision-making and problem-solving. [see my blog on Mode 1 and Mode 2 KM for more info]

If you want to know more about my work on data to knowledge flows, or high-impact Knowledge Management principles, drop me a line and start a conversation(david@k3cubed.com).

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