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Data vs. Information, Same Thing Right?

In the ever changing world we live in, I notice that the distinction between data and information is often blurred. One of my recent projects was to focus on helping a scientific research institute create greater results by moving its data from a collection of facts to actionable steps. As we started to determine some processes internally, here are some aha points we came across:

  • Data is ‘raw’ and needs interpretation. Individual ‘bits’ of data provide no insight without context. What are the conditions of the situation that the data was collected, is it reproducible, when conditions change how will other data change and more.
  • Information is interpreted. Data with some quantity and context allows for information to be interpreted.
  • Information needs to be something that can be acted upon. Collecting enough data and adding enough interpretation allows data to mature into information. Information is the beginning of something that can be acted on.

We developed a process for converting Data to Information

  • Collection of individual points. These need a process to understand what the conditions where when the collection occurred.

 

  • Data is a grouping and aligning the collection TaosGS2013in a meaningful way (think rows and columns) to allow for looking at it. This is when notebooks become charts. When it is important to list your units (especially if you are working in a team) and specifics. When you list a time is that UTC or headquarters time or agency time? When you count sales is that in USD or local currency? Are you listing every transaction or just completed sales or completed sales and returns within 7 days

 

  • Analysis – once individual points are Social_Network_Analysis_Visualizationorganized enough to call it data, it is time to start to review and determine some core issues. What is the correct scale to measure, What data should be tossed out (150degree room temperature may be an error when it was 50degrees yesterday and the day before), What data is missing (what is the outside temperature relative to inside temperature?

 

  • Interpretation – What are the trends, Lle_hlle_swissrollWhat should we measure next, what data is missing, what conclusions can be drawn, what suspected ideas can be seen. Typically it is the ideas, theories and hypothesis that are real at this point, but in the business world, they are often listed as conclusions. Business will label them conclusions because they ‘were true’ at the time with the given data. Science would still label them ideas.

 

  • Sharing – Taking these ideas and converting them into a presentable format. Tables, sparkline_twittercharts and slides. Here you are purposefully slanting the data to show your conclusions but with enough footnotes that others can question your conclusions. The tools and best practices here continual to evolve at a rapid rate. But it becomes a fine balance between over geeking and stripping any real information out at this point and just showing the conclusions without the data behind it. Edward Tufte has moved this aspect of data communication vastly forward.

 

  • Discussion – Now the collection of individual points have been aligned, reviewed and presented. But your may not be perfect, so time to start reviewing with others. What was missed, what was ignored, what is the ‘truth’ behind  the data points.

 

  • Process – Great data, nice interpretationrefineryflow but So What? This step is where we take the conclusions that were discussed and use that in future work. Whether it is ‘when it is going to be hot outside, turn the AC on earlier’ to ‘when there is a festival, start selling T-Shirts with the band names 2 weeks before the concert’ or 94 days before Christmas hire 5 new shipping clerks so they are full up to speed before the mad rush happens.

 

  • Implementation – Once we define the process, there are many steps on how to implement that process. That is where data has become Actionable Information (and hopefully increased profits).

 

So no, Data is not information. Data leads to information. Data is only the 2nd step towards being able to have real information. And that is just part of the journey to the implementation of that information.

Hopefully, you found this information informative…

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November 5, 2015 Posted by | How To, Uncategorized | Leave a comment