Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. The process of data analysis is Data requirements, Data collection, Data processing, Data cleaning, Exploratory data analysis, Modeling and algorithms, Data product and data presentation.
We spent so much time on collection, reporting and analysis. There is one crucial part we often don't invest in sufficiently. The last mile. Data presentation! There are some advises about how to present data analysis results.
Setting about making charts without understanding your own questions means you will spend a great deal of time creating a final product with questionable results. Done right, visualizations are more impactful. However, done wrong, visualizations can make data even more confusing.
a.Check raw data for anomalies prior to performing your analysis;
b.Re-perform important calculations;
c.Confirm main totals are the sum of subtotals;
d.Check relationships between numbers that should be related in a predictable way, such as ratios over time;
e.Normalize numbers to make comparisons easier, such as analyzing amounts per person or relative to GDP or as an index value relative to a base year;
f.Break problems into component parts by analyzing factors that led to the results.
There are four types of data analysis: Network and Tree (Whom),Topical (What), Geospatial (Where) and Temporal (When).
Temporal data analysis addresses the question of “when” by helping the user identify time-based information.
“When” questions are answered by analysis of time-series data. Geospatial analysis uses location information to identify position or movement over geographic space. This type of analysis is commonly accomplished with thematic maps by overlaying data on a geospatial substrate.
Topical analysis is the process of “extracting a set of unique words and their frequencies to determine the topic coverage of a body of text.
It is critical to know the different types of data analysis you will need to facilitate, and the charts needed for each of them, so that you can check for their availability in the component you are evaluating.
Once you've selected the right type of chart for your data, make sure you don't do your data a disservice by forgetting some basic design tips. Kill the grid lines unless they're absolutely necessary, or at least make them subtle so they don't distract from the information you're trying to present. Make sure your chart is centered on the data you want to present, your axes are clearly labeled, and your axes have units on them where necessary, so no one has to guess or infer what you're trying to say. Remember, your goal is that anyone can pick up your chart, whether you're there to talk to it or not, and understand what information the data is trying to communicate.