VizNetwork Blog » well-formed data http://www.viznetwork.com/blog Musings on improving brainstorming and communication through visualization and storytelling Wed, 01 Feb 2012 15:38:47 +0000 en hourly 1 http://wordpress.org/?v=3.2.1 Well-formed Data – Part 4: Missing Data http://www.viznetwork.com/blog/2010/02/well-formed-data-part-4-missing-data/ http://www.viznetwork.com/blog/2010/02/well-formed-data-part-4-missing-data/#comments Wed, 10 Feb 2010 19:02:23 +0000 thcrawford http://viznetwork.com/blog/?p=97 How often do you get data that needs to be charted or graphed only to find out some of the data’s missing?  We see it all the time.  However, just because it’s missing, doesn’t mean you can ignore it.  You need to know how to make it consistent, how to deal with it if it [...]]]> How often do you get data that needs to be charted or graphed only to find out some of the data’s missing?  We see it all the time.  However, just because it’s missing, doesn’t mean you can ignore it.  You need to know how to make it consistent, how to deal with it if it is missing, or sometimes even how to recover it.  In this 9:12 video, the fourth in our series on well-formed data for charts & graphs, we cover:

  • types of missing data
  • differences between incomplete, space, blank/null, and 0 data
  • dealing with missing text
  • dealing with missing numbers
  • dealing with implied data

Let me know what you think!

Also, be sure to check out the other 3 videos in this series:

Part 1: Consistency (9:43)
Part 2: Concatenation & Extraction (9:57)
Part 3: Normalization (8:52)
Part 4: Missing Data (9:12)

Next up, more tips on using PowerPoint to create a visual impact.

]]> http://www.viznetwork.com/blog/2010/02/well-formed-data-part-4-missing-data/feed/ 0 Well-formed Data – Part 3: Normalization http://www.viznetwork.com/blog/2010/02/well-formed-data-part-3-normalization/ http://www.viznetwork.com/blog/2010/02/well-formed-data-part-3-normalization/#comments Mon, 08 Feb 2010 19:14:23 +0000 thcrawford http://viznetwork.com/blog/?p=90 Our lives are made up almost exclusively of relationships and that includes data.  In fact, it’s hard to think of examples of data that doesn’t involve a relationship.  Customers (hopefully) have many Orders.  Companies have multiple employees.  People (hopefully) have multiple friends, who also have multiple friends, and may even share some of each others [...]]]> Our lives are made up almost exclusively of relationships and that includes data.  In fact, it’s hard to think of examples of data that doesn’t involve a relationship.  Customers (hopefully) have many Orders.  Companies have multiple employees.  People (hopefully) have multiple friends, who also have multiple friends, and may even share some of each others friends.  People may have multiple phone numbers, e-mail addresses, and so on. 

Admittedly, this may be the geekiest of topics on creating well-formed data.  In part 3 of our 4 part series, we discuss one way to make sure your data follows the ”1 Concept per Row” rule.  Normalization is used whenever one concept relates to another concept.  Given that almost all data is about relationships and we want to analyze that data, we have to know a bit about normalization which is all about those relationships.  There are whole college classes dedicated to this topic, so we won’t be able to cover it all in this 8:52 video, but hopefully you’ll understand enough that you can do the basics yourself or talk intelligently to your data warehouse person.

In the video we discuss, 2 types of relationships…1 to many (e.g. 1 person may have 1 or more orders) and many to many (e.g. people have many friends that can overlap).  These require slightly different solutions.  There is a 3rd type of relationship, 1 to 1, however, dealing with that one doesn’t require normalization.  That data can just be stored in the same spreadsheet since it follows the “1 Concept per Row” rule.  The examples use Excel and the vLookup() function to illustrate the concepts.

Let me know what you think!

In the final video, we’ll wrap up our tour of well-formed data with a discussion on how to deal with missing data.

]]> http://www.viznetwork.com/blog/2010/02/well-formed-data-part-3-normalization/feed/ 0 Well-formed Data – Part 2: Concatenation & Extraction http://www.viznetwork.com/blog/2010/01/well-formed-data-part-2-concatenation-extraction/ http://www.viznetwork.com/blog/2010/01/well-formed-data-part-2-concatenation-extraction/#comments Mon, 18 Jan 2010 23:54:20 +0000 thcrawford http://viznetwork.com/blog/?p=87 Continuing our series on preparing data for analysis & visualization, we’ve just released the next video on concatenation and extraction.  The ability to break data apart and put it back together in new ways is essential to preparing data.  By storing data at the lowest sensible level, it can be used separately or combined with [...]]]> Continuing our series on preparing data for analysis & visualization, we’ve just released the next video on concatenation and extraction.  The ability to break data apart and put it back together in new ways is essential to preparing data.  By storing data at the lowest sensible level, it can be used separately or combined with other data for interesting analysis and visualization.  In this practical video, we discuss 6 key Excel functions for extraction including left(), mid(), right(), search(), len(), and trunc().

Check out this 9:57 video to learn how to take the next step in preparing your data for analysis and visualization:

 

In the next 2 videos, we’ll continue the discussion with Normalization (advanced ideas to support One Concept per Row), and Missing Data (how to deal with missing or implied data).

]]> http://www.viznetwork.com/blog/2010/01/well-formed-data-part-2-concatenation-extraction/feed/ 0 Well-formed Data – Part 1: Consistency http://www.viznetwork.com/blog/2010/01/well-formed-data-part-1/ http://www.viznetwork.com/blog/2010/01/well-formed-data-part-1/#comments Sun, 17 Jan 2010 15:23:55 +0000 thcrawford http://viznetwork.com/blog/?p=81 Data visualization and analysis are powerful tools for discovering and communicating stories held in your data.  However, before most of today’s data visualization tools can be used effectively, the data must be cleaned, organized and prepared.  Over the next 4 videos, I’ll be discussing how to prepare your data to be visualized.  The first step in [...]]]> Data visualization and analysis are powerful tools for discovering and communicating stories held in your data.  However, before most of today’s data visualization tools can be used effectively, the data must be cleaned, organized and prepared.  Over the next 4 videos, I’ll be discussing how to prepare your data to be visualized.  The first step in the process is consistency.  Consistency is made up of 4 principles:

  • One Concept per Row
  • One Data Type per Column
  • One Format per Data Type
  • Using the Lowest Sensible Level

Check out this 9:43 video to begin the most important step in preparing your data:

In the next 3 videos, we’ll continue the discussion with Concatenation & Extraction (how to put things together and take them apart), Normalization (advanced ideas to support One Concept per Row), and Missing Data (how to deal with missing or implied data).

]]> http://www.viznetwork.com/blog/2010/01/well-formed-data-part-1/feed/ 1