Hyper Analyzer

Software

Articles about Hyper Analyzer

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Design the dataset for multiple analysis

Same data, several viewing angles

 

One important feature of Hyper Analyzer is that the data is not organized/saved in predefined cubes. It contains highly compressed raw data and a list of analysis. Each analysis is performed in seconds. Consider this feature as a great opportunity to interrogate just once the source (the database for instance) and to query then the resulting set in several ways. Do not limit the primary data extraction to just a few columns. Extract instead as much as possible information (columns) in order to be able to create many analyses. The analyses are organized as mind paths and they are saved together with raw data. Mind paths themselves can be saved and loaded in top of other datasets.

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Improve readability

Use colors with care in your reports

I am personally against large printed reports. Obviously nobody reads them all, in a lot of cases the paper ends up in the trash. But in most cases a few report pages are printed. Readability is important, so we have to enhance the visibility of the important figures. The reader has to focus quick on important figures rather then parsing them all. This can be achieved in several ways.
- Highlighting text font using colors. Use no more then one-two colors for different highlights and only for important figures (either good or bad)
- Use micro drawings. Horizontal bars, bullet graphs, shapes can be quickly added to the numbers to improve the readability.
- Use very light colors grids (light gray) to let the reader to focus on figures not on drawn lines.
- Avoid using gradient labels or fancy images in the Report Layout which usually just distract the reader attention.​​

Developing a complex information flow

Using scripts to automate the information flow process

 
Data usually has to be imported, processed and transformed in readable analyses. Hyper Analyzer can automate every single step of this process using the scripting extension. The analyst's work can be saved and applied on new data in a automated way using just a few lines of code. Two versions of task scripting are provided. A simple visual task scripter, which just executes in a specific order a group of tasks (import data, apply calculation, apply analysis, save dataset, save reports, export pdf, export spreadsheet, etc) and a fully featured programming environment with an incorporated debugger which can do everything the simple task scripter does and many more. Few possibilities are: direct manipulation of data by using dynamic filters, sending e-mails with attachments, opening and manipulating Excel or Word documents via OLE Automation, transfer files via http, ftp or other protocols. More information about the incorporated module can be found in the electronic documentation and on the author's site http://www.remobjects.com/ps.aspx

 

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Importance of details

Using right formatting for figures

In Hyper Analyzer, by default the numbers are formatted with 2 optional decimals. For visual comparison replace the default formatting #############0.## with ###########,##0.00 (add thousand separator and force 2 decimals, even if they are not necessary) and use a good font for numbers. Tahoma seems very good for this, each figure 0..9 having the same character width. Data is more readable and less difficult to compare. Default number width space in analysis is 75 pixels which is ideal in most of cases. These spaces are to be adjusted by the analyst during the design process. By adjusting the width of the measure, the analyse will remember the size and in case new data has larger figures, there is a chance that the whole number cannot fit in the designed space. Make a good estimation of the space to avoid such of surprises.​​