Wednesday, October 13, 2010

101 tips of representing data


Figure 1

Can someone tell me what could possibly be wrong with the above slide?

--A lot of things ---a. The font size of heading and the content is different b. The bullets are funny c. The content font size is weird d. There are no possible images on the slide

If this slide is given to you for improvement, what possible steps would you incorporate?

Perhaps you would try to do it in the a better way below?


Figure 2

There is a definite improvement with the Figure 1, however, I feel the Figure 2 is still loud, and needs further refinement. This is where I would add my 2 cents.

In data representation such as these, one first needs to connect individual slides to make a story. Hence, it would be useful for you as a smart analyst, to first understand the broader picture or have a 10,000 feet view of what you are actually trying to archive. This would help you to 'connect the dots' and hence build your slides in much more analytical way. Easy to make, Easy to understand, satisfies the broader audience.

As a smart analyst, you also need to know your audience. If you audience comprises of senior level executives, who only will give you 5-min of your time, throttling down a 50 slide presentation deck will not help. You would need to understand that they don't have the time to read through text after text. So a simpler way would be depict your text as pictures, use less more focused words, and to add the right blend of colors to put your message across.

For instance, Figure 3 is clearly pictorial -- it uses less text on the slides..comfortable for an executive to digest.



Figure 3

Which one these data slides would you like to keep? Well we need to decide between Figure 2 and Figure 3, as we have almost ruled out Figure 1, isn't? Both Figure 2 and Figure 3 are good ways to represent the data. These were made using MS Powerpoint smartgraphics tool.
What is Analytics?

Analytics is a very broad field, and it ranges from abstract qualitative data analysis to more specific quantitative data analysis. Each of these arena has its own tools and techniques, however the main purpose is synthesize information in a way that it is easily digestible by the senior audience.

Structure of this blog:

1. Data collection using primary and secondary research
2. Undestanding quantitative data using SAS and Excel
3. 101 ways of representing data on a powerpoint
4. Creating effective presentation using story boarding