
This blog post is not a political post, but considers how winning may be assessed, particularly in a business context. (more…)

This blog post is not a political post, but considers how winning may be assessed, particularly in a business context. (more…)
A question that regularly arises is “What does a facilitator do?” which is a very pertinent question, especially in a business context. I will now try and provide a little more clarity on what, in my experience, good facilitators should do.
This is Part 5 of a 5 part blog post. If you have not read them yet, see:
In this final post of this series we briefly consider how the different data personalities work together and suggest some strategies to change how people behave. (more…)
This is Part 4 of a 5 part blog post. If you have not read them yet, see:
There are three species in the high compliance area of the Data Zoo: the Jobsworth, the Useful Person and the Data Evangelist. (more…)
This is Part 3 of a 5 part blog post. If you have not read them yet, see:
There are two species in the medium compliance area of the Data Zoo: the Plodder and the Data Beaver. We will also cover the Data Ostrich who does not appear in the main area of the Data Zoo.
This is Part 2 of a 5 part blog post. If you have not already read it see Part 1 – The shape of the Zoo
There are three species in the low compliance area of the Data Zoo: the Data Whinger, the Data Squirrel and the Data Anarchist.
In this five part blog post I will present a number of different types of data personality that people may recognise from the organisations that they are involved in. I will provide examples of how these personalities operate and suggest ways that we need to engage them to improve how data is managed.
I have titled these posts “The Data Zoo” as there may be many different species of data user within the zoo, there are also different sub-species, which may form part of future blog posts.
One of the good aspects of social media and social networking is the relationships you are able to form with other professionals, who you may or may not have met physically. One example of this is the relationship Phil Simon and I have developed through various on-line interactions, comments on blog posts and phone calls. Phil is a respected technology author, blogger, consultant and self-confessed Rush fan.
One result of this has been that Phil has included an interview with me as part of his Technology Today series of podcast interviews. In the interview we discuss:
See the page on Phil’s site for more details and the interview itself.
Clearly, when computers are required to perform “straight forward” calculations they are accurate. For example, when adding up a series of values they will get the correct answer. A recent Dataspora blog post postulates that we are not far from the point where data flows around the world helping to make everything happen, but without involving humans.
I take a slightly different view based on real world experiences of data, analysis systems and human behavior. In summary, I believe that complex analysis systems are inaccurate, to a certain degree, so outputs need to be treated with caution and reviewed for suitability before being acted upon.
There are a number of analogies for issues associated with data quality. Ken O’Connor’s recent blog post likened data quality to the quality of water in a river. I also sometimes use an analogy based on the quality of water in a swimming pool.
A different analogy that I use, and is the subject of this blog post, is to liken your data to a piece of cheese!