The unofficial Cookbook Companion DevInfo 7 for the End Useroldstat.ww.tj/images/DevInfo_7_End_User_Guide_ENG_March2014.pdf · DevInfo 7 End User Guide,ENG.docx (updated February

The unofficial Cookbook Companion

DevInfo 7 for the End User

2014

DevInfo 7 End User Guide,ENG.docx (updated February 2014) Page 2 of 42

Table of Content I. Introduction ........................................................................................................................................................ 3

How to use this companion document ................................................................................................................... 3

Disclaimer ................................................................................................................................................................ 3

II. Looking for the most beautiful data .................................................................................................................. 4

1. Data discovery ................................................................................................................................................. 4

2. Download, save & share visualizations ......................................................................................................... 10

2.1. How to download .......................................................................................................................... 10

2.2. How to share ................................................................................................................................ 10

2.3. How to save .................................................................................................................................. 12

III. Up close and personal with Tables, Maps and Graphs................................................................................ 14

3. Visualization: Tables ...................................................................................................................................... 14

4. Visualization: Maps ....................................................................................................................................... 17

4.1. Single theme map (shows a single indicator) .......................................................................... 17

4.2. Multiple theme map (shows more than one indicator) .......................................................... 19

4.3. Google Map 2-D (DI 7.0) ........................................................................................................... 22

4.4. Google Map 3-D (DI 7.0) ........................................................................................................... 23

5. Visualization: GRAPHS & CHARTS .................................................................................................................. 24

5.1. Visualization: Area Graph ...................................................................................................................... 25

5.2. Visualization: Bar Chart and Column Chart ........................................................................................... 26

5.3. Histograms – a special note about creating and using .......................................................................... 28

5.4. Visualization: Line Chart ........................................................................................................................ 29

5.5. Visualization: Pie Chart .......................................................................................................................... 30

5.6. Visualization: Pyramid Chart ................................................................................................................. 31

5.7. Visualization: Radar Chart ..................................................................................................................... 32

5.8. Visualization: Scatter Diagram .............................................................................................................. 35

5.9. Visualization: Treemap Diagram ........................................................................................................... 37

6. Metadata in DevInfo...................................................................................................................................... 41

6.1. Definition and utility .............................................................................................................................. 41

6.2. Who is responsible to provide metadata? ............................................................................................ 42


Introduction

How to use this companion document

The aim: to make it easier for everyone to use the DevInfo 7 on-line database (TojikInfo vs 7.1.0).

This document is building on and complementing already available DevInfo 7 guides. This companion

document provides tips and suggestions about using these various features.

Official DevInfo training materials to consult include:

User and administration guides (cookbooks) available for download: http://www.devinfo.org/libraries/aspx/DevInfoDownloads.aspx?T=DL7&PN=diorg/di_downloads_7.html

DevInfo e-learning (audio) courses: http://www.devinfo.org/libraries/aspx/DevInfoTraining.aspx?T=EL&PN=diorg/di_elearning.html

Disclaimer This document is not an official product from the United Nations, it has been compiled by the author using selections from the existing DI training material and user guides, as well as presenting other relevant material1 on some of the visualization options available.

Examples, or case studies, in this guide were prepared using existing DevInfo databases available on-line (see

the DI Catalog http://www.devinfo.org/libraries/aspx/home.aspx ). Because these on-line databases are subject to

change, this may have bearing on the ability to replicate some of the examples.

Any observations, corrections and suggestions for improving the utility of this document as a training support

tool, especially as it relates to other training documents, are most welcome (please send them to

[email protected] with a cc to the author [email protected]).

1 various on-line sources

http://www.devinfo.org/libraries/aspx/DevInfoDownloads.aspx?T=DL7&PN=diorg/di_downloads_7.html

http://www.devinfo.org/libraries/aspx/DevInfoTraining.aspx?T=EL&PN=diorg/di_elearning.html

http://www.devinfo.org/libraries/aspx/home.aspx

mailto:[email protected]

mailto:[email protected]


I. Looking for the most beautiful data

1. Data discovery The Home Screen for the User Application

TojikInfo is built with the DevInfo platform and available to end users via a web interface. To start the

TojikInfo database click the following link: www.tojikinfo.tj

Note: this end user guide has been developed using the global DevInfo MDGInfo database. Simply go to

http://www.devinfo.org/libraries/aspx/Home.aspx to access this database.

How to search for data:

Quick Data Search: It is easy to search for and select multiple indicators. In this example using HIV as the key

word three indicators are listed with data available for Tajikistan.

It is possible to view these indicators one by one by selecting the ‘visualize’ option, but the ‘Add to cart’

option gives greater convenience as all indicators remain accessible.

http://www.tojikinfo.tj/

http://www.devinfo.org/libraries/aspx/Home.aspx


By clicking the red ‘Data’ button all indicators are available in the visualize interface. It is easy to select which

of the indicators to work with (one or all), which time periods to use (here the ‘Most recent data’ option is

selected), to filter for variables like sex, location, age, source, etc.

The left panel of the visualization page gives a lot of options to the user.


Here in the below screen shot one indicator and all available time periods for that indicator are selected.

Note: the various visualization options (tables, graphs, maps, treemaps, etc.) are discussed elsewhere in this

document.

Browse by Topic: it is easy to browse data and indicators through lists or groupings



Browse by Area: in this method select specific areas and any available sub-regions or areas.


The Visualization Page

Let’s use an example (net enrollment ratio in primary education) to illustrate.

Click Visualize to see the default (suggested) visualization. It is possible to change the visualization by clicking

on the different format type.

The Settings tab allows the modification (customization) of the visualizations for colors, legends, scales, font,

layout, etc. A more detailed discussion is presented in the sections below.


2. Download, save & share visualizations In the visualization page in the upper right are three options: save as, download, share.

2.1. How to download There are different options for downloading the visualizations (TMG).

PNG = Portable Network Graphics format SVG = Scalable Vector Graphics (SVG), an XML-based vector image format XLS = Microsoft excel spreadsheet, with graphics page.

2.2. How to share Before sharing, consider what policies (if any) guide how data and information products are disseminated or communicated by your organization:

a) Data dissemination b) Data security c) Publication procedures / product release as formal or official product d) Social Media (SM) guidelines for direct release of content to Twitter, Facebook, etc.



2.3. How to save When using the global DevInfo database, the save as option sends the visualization to a ‘private on-line gallery’ managed on the global DevInfo website. To save to the MDGInfo Gallery the user must first register to the site. Afterwards it is easy to save and share visualizations using the on-line DI 7 software.

When using a customized DevInfo database (e.g. TojikInfo), a different gallery option will be used. The TI database Administrators will prepare gallery objects that are saved to and shared from the TI website.

The general public visiting the TI web site can also prepare gallery objects but these will need to be saved onto their own computers.



II. Up close and personal with Tables, Maps and Graphs

Visualizations are created images, diagrams or animations designed to communicate a message.

This section provides a detailed discussion about the 11 visualization options available in DI 7

The aim is to provide information that will help an end user design and prepare good quality Tables,

Maps and Graphs.

Using different visuals (TMG) to present and drill down into a topic (e.g. a graph might show a trend line,

but a companion table could emphasize leaders or lagers).

3. Visualization: Tables DevInfo 7 facilitates the preparation of very readable tabular output. The look and feel of a DevInfo table is

distinctive, well balanced and very readable for both on-line and in print formats. When preparing a table

there are many font, format and color choices available to the user.

Select data to put in a table:

Make a search selection (as described earlier) and click on

Select the Table icon

Select the Settings Tab (next to the Visualize tab)

Pivot: changes the focus of how data is arranged (structured) in the table.

Swap: simply exchanges (or swaps) columns and rows but does not change the data structure.

Arrange: limited to table formatting.


Example:


Rearrange and reorganize data in a table using the features in settings.

When a table is enough

Sometimes a table on its own is enough to convey to the end user (the content consumer) the key

information about the subject at hand. Choices about which visualizations to use (on their own or in

combinations) will be driven by the data itself as well as the message being delivered and the audience.


4. Visualization: Maps

Using the following indicators to illustrate:

4.1. Single theme map (shows a single indicator)

A single indicator is visualized using a single ‘theme’ like colors, hatch patterns, dot density patterns. To

build a single theme map click and then select the time period to visualize, in this case = most recent data


Then select the single theme map icon to generate a map.

Note: there is a way to select what indicator is being mapped by using a drop down list in ‘Select Indicator’


The Settings tab is where the options for legend, titles, fonts, colors and so forth are controlled.

4.2. Multiple theme map (shows more than one indicator)

A multiple theme map shows more than one indicator on the map. This is possible by having one indicator in one theme (like colors) and another indicator in another theme (like hatch patterns). To

build a multiple theme map click and then select the time period to visualize, in this case = most recent data

Then select the multiple theme map icon to generate a map.

Next, click the Settings tab to be able to assign themes to the desired indicators. In this case the total value is the shaded colors and the female value is the hatch pattern overlaid onto the shaded map.

To add in a new theme click the add theme icon


This is the Total values (color shaded)

This is for the Female values (hatch pattern)


This is for the Male values (dot density)

This is the map showing multiple indicators using different themes (colors, hatch patterns, dots)


4.3. Google Map 2-D (DI 7.0)

Check that the Google Earth plug-in is installed on the computer. By clicking the icon the DevInfo map is overlaid onto the Google Earth map.


4.4. Google Map 3-D (DI 7.0)

This option offers a 3-D look, and overlays the same DevInfo map onto a Google map.


5. Visualization: GRAPHS & CHARTS Definition: Charts and graphs are used to present numerical information in a pictorial or illustrative form,

allowing better comprehension of the data.

Different situations call for different types of graphs, and it helps to have a good overview of what graphs are

available. Many times the type of data determines what graph is most appropriate (best) to use: http://fotios.org/files/fotios.cc/papers/gen/Graph_Resolution.htm

Chart: The purpose of a chart is to organize and display information by one or more categories or fields.

Charts are graphical representations of data via symbols such as bars in a bar chart, lines in a line chart, or the

wedge-slices of a pie chart (http://en.wikipedia.org/wiki/Chart).

The basic types of charts are:

Line chart: e.g. data that presents numbers that change over time (such as "annual revenue from

1990 to 2000")

Pie chart: e.g. data that presents percentages in different groups (such as "satisfied, not satisfied,

unsure")

Area chart: e.g. allocation of funds in a budget over the years

Table chart: e.g. figures of budgets related to various years

Bar/Column chart: e.g. income of groups of people

Histogram chart: are useful to illustrate the relative proportion of each category with all the others.

This type of chart is different from other more typical bar charts mainly due to the required

proportionality between values and their graphical representation

Treemap: where the areas of the rectangles correspond to values, and colors may indicate other

dimensions

Bubble chart: a two-dimensional scatterplot where a third variable is represented by the size of the

points

Geographical chart: a map of a region (physical, nautical, political, economic, etc.)

Graph: A graph is used to plot out functions of two variables on a Cartesian coordinate system, (think analytic

geometry, with a horizontal x-axis, or abscissa, and a vertical y-axis, or ordinate). The most common type of a

graph is a line graph (or dot graph), depicting the relational variations of entities (variables) using x-y

Cartesian Coordinates2 with:

horizontal axis: independent variable (x)

vertical axis: dependent variable (y)

According to some sources, technically charts and graphs may be considered to be different things, but

commonly these terms are used almost interchangeably (depending on the source). In the DevInfo 7 user

interface there is no specific reference to “graph” or “chart”, visualizations are simply labeled as: table, line,

column, bar, area, pie, pyramid, tree, radar, scatter and map.

2 Two dimensional graphs have X-Y values. A three dimensional graph will have values on a 3rd axis

http://fotios.org/files/fotios.cc/papers/gen/Graph_Resolution.htm

http://en.wikipedia.org/wiki/Chart


5.1. Visualization: Area Graph

Definition: an area chart or area graph displays graphically quantitative data. It is based on the line chart.

Data arranged in columns or rows on a worksheet can be used in an area chart. Area charts emphasize

the magnitude of change over time and can be used to draw attention to the total value across a trend http://en.wikipedia.org/wiki/Area_chart

Area charts can be used to answer the following questions:

a. Show trends over time among related attributes

b. Compare two or more quantities (the parts)

c. Show the relationship of parts to the whole

d. Example: data that represents profit over time is plotted in an area chart in order to emphasize

the total profit

Structure: These charts are typically used to represent cumulated totals over time and are the

conventional way to display stacked lines. For example, data that represents profit over time can be

plotted in an area chart to emphasize the total profit.

Area chart made using DI7. DevInfo database: MDGInfo 2012; Area: Caucasus and Central Asia

http://en.wikipedia.org/wiki/Area_chart


5.2. Visualization: Bar Chart and Column Chart

Definition: a bar chart is a chart that uses either horizontal or vertical bars to show comparisons among

categories. A bar graph (or chart) contains a bar for each category of qualitative data in a dataset. The

bars can be arranged in order of frequency, so that more important categories are emphasized. Note

that in DevInfo 7 the bar chart option is specific for bars drawn horizontally, and the column chart is

specific for bars drawn vertically.

Draw a vertical line with a numbered scale. The numbers on the scale will correspond to the height or

length (horizontal) of the bars. The greatest number needed on the scale is the category with the highest

frequency, label the column base with the title of each category

Structure: One axis of the chart shows the specific categories being compared, and the other axis

represents a discrete value. Some bar graphs present bars clustered in groups of more than one (grouped

bar charts), and others show the bars divided into subparts to show cumulate effect (stacked bar charts). http://en.wikipedia.org/wiki/Bar_chart

http://en.wikipedia.org/wiki/Bar_chart


Bar chart made using DI7. In this example the data series for the year 2000 was highlighted in a red color. The series was first

prepared in a grey color pallet and then the featured item for highlighting was changed to red.

This is an example of a stack bar chart made using DI7 and the default color pallet.


This is an example of a column chart made using DI7 and then working the visual further by editing it in powerpoint, adding new features

like these red circles to help guide the readers’ eyes.

5.3. Histograms – a special note about creating and using

The selection of graphics available in DevInfo 7 does not include the automatic production of histograms.

Definition: A histogram is a graphical method of presenting data in vertical bars to reflect the distribution

frequency. Histograms can be a very good visual tool to reinforce data analysis.

http://searchsoftwarequality.techtarget.com/definition/histogram

http://www.brighthubpm.com/six-sigma/13445-understanding-histograms-and-their-uses/

Example: 100 pumpkins are harvested for sale and they will be sold by weight category. The objective is

to know how many weigh 0-2 pounds, 3-5 pounds, 6-10 pounds, etc. By grouping data into these

categories and plotting them with vertical bars along an axis it is possible to see the harvest’s distribution

according to their weight.

Structure: The first step in constructing a histogram is to decide how the process should be measured

(i.e. what data should be collected). The data must be variable data or data that is measured on a

continuous scale, such as: volume, size, weight, time, temperature.

Typically in a histogram the independent variable is plotted along the horizontal axis and the dependent

variable is plotted along the vertical axis.

Horizontal (X-) Axis: shows the range or the values in between variables. They are commonly called class

intervals which represent or summarize large data sets.

http://searchsoftwarequality.techtarget.com/definition/histogram

http://www.brighthubpm.com/six-sigma/13445-understanding-histograms-and-their-uses/

http://whatis.techtarget.com/definition/independent-variable

http://whatis.techtarget.com/definition/dependent-variable

http://whatis.techtarget.com/definition/dependent-variable


Vertical (Y-) Axis: represents the range values of frequencies or the number of times the class intervals

occurred.

Bars: the bars represent the person, object, item or event with the variable measures. Bar height denotes

frequency measures (this dimension will vary). The width, however, stays the same for all bars.

The data columns include: class, intervals, frequency

Example for individual height:

5.4. Visualization: Line Chart

Definition: A line chart displays information as a series of data points called 'markers' connected by

straight line segments. A line chart is often used to visualize trend data over time (time series). Line

charts are often drawn chronologically.


The horizontal axis is called the x-axis and the vertical axis is called the y-axis. The y-axis represents the

dependent variable and the x-axis represents the independent variable.

This is an example of a line chart made using DI7

5.5. Visualization: Pie Chart

Definition: a pie chart displays qualitative data in the form of a pie. Each slice of pie represents a value in

the data set and is shown proportional to the total amount.


This is an example of a pie chart made using DI7

5.6. Visualization: Pyramid Chart

Definition: Pyramids are useful to illustrate distributions, hierarchy or stratification. Most commonly

these graphs are used to represent population where one horizontal bar graph shows the number of

males and the other shows females in particular population age groups. Males are conventionally shown

on the left and females on the right, and they may be measured by raw number or as a percentage of the

total population.

Structure: two back-to-back horizontal bar graphs with one variable (i.e. population) plotted on the X-axis

and a second variable (i.e. age) plotted on the Y-axis.


This is an example of a stack bar chart (aka pyramid chart) made with DI7 (one indicator, 2 time periods)

5.7. Visualization: Radar Chart

Definition: a radar chart is also known as a web, spider or star chart, star plot, irregular polygon, polar

chart, or a “kiviat” diagram. Radar charts are used to plot the values of each variable along a separate

axis that starts in the center of the chart and ends on the outer ring.

Radar charts can be used to answer the following questions:

a. What variables are dominant for a given observation?

b. Which observations points are most similar or dissimilar? Are there clusters?

c. Are there outliers?

Data arranged in columns or rows on a worksheet can be plotted in a radar chart. Radar charts compare

the aggregate values of multiple data series. Each radar/star represents a single observation. Typically,

radar charts are generated in a multi-plot format with many stars presented together on a page, each

star representing one observation. A well cited example being:


http://www.itl.nist.gov/div898/handbook/eda/section3/starplot.htm

Structure: a radar chart is a sequence of spokes, called radii, with each spoke representing one of the

variables. The data length of a spoke is proportional to the magnitude of the variable for the data point

relative to the maximum magnitude of the variable across all data points. A line is drawn connecting the

data values for each spoke, giving the polygon shape.

http://www.itl.nist.gov/div898/handbook/eda/section3/starplot.htm


This is an example of a radar chart generated with two indicators with 21 data points (1990-2010) made with DI7


This is an example of a radar chart generated with two indicators with 21 data points (1990-2010) made using DI7.

Select specific elements to compare (click to select = suppress from view the other data)

5.8. Visualization: Scatter Diagram

Definition: a scatter diagram, also known as a scatter plot, is a tool used to analyze relationships between

two variables. Scatter diagrams are a useful tool to help determine association. Note that if any

association exists, the resulting scatter plot may or may not suggest underlying cause-and-effect. Thus

scatter diagrams do not themselves "prove" cause and effect.

Scatter diagrams can provide answers to the following questions:

a. Are variables X and Y related?

b. Are X and Y linearly related?

c. Are X and Y non-linearly related?

d. Does the variation in Y change depending on X?

e. Are there outliers?


http://www.itl.nist.gov/div898/handbook/eda/section3/scatterp.htm

Structure: one variable is plotted on the horizontal (X) axis and the other is plotted on the vertical (Y)

axis. The pattern of intersecting points can graphically show relationship patterns.

X-Y (scatter) charts and line charts look similar, especially when a scatter chart is displayed with

connecting lines. However, there is a difference in the way each displays data. The main difference is the

way each of these chart types plots data on the x-axis.

A line chart distributes category data evenly along the horizontal category axis (x-axis), and distributes all

value data evenly along the vertical value axis (y-axis).

A scatter chart has two value axes, showing one set of numerical data along the x-axis and another along

the y-axis. It combines these values into single data points and displays them in uneven intervals, or

clusters3.

Scatter charts and line charts also differ in the way they use axis types and scaling:

The x-axis of a scatter chart can only be a value axis. This means that only numeric data is displayed on

this axis. To display this numeric data with greater flexibility, you can change the scaling options on this

axis.

The x-axis of a line chart can be either a category or a time axis, both of which can display non-numeric

data. Depending on which data is used, the scaling options are limited compared to the scaling options of

the scatter chart's x (value) axis.

You can see what axis type is used in your chart on the scale tab of the Format Axis dialog box. Select the

axis, and then click Selected Axis on the Format menu.

When the dots line up – the four parameters of linearity:

i. Correlation: measures how well data line up. The more the data resembles a straight line, the higher

the correlation to each other. A scatter plot is useful to see how two comparable data sets agree with

each other. The more the two data sets agree, the more the scatters tend to concentrate in the

vicinity of the identity/trend line.

If the points are tightly clustered along the trend line, then there's probably a strong correlation. If it

looks more like a shotgun blast, there is no correlation.

Correlations may be positive (rising), negative (falling), or null (uncorrelated).

ii. Slope: measures the steepness of the data. The steeper the data slope, assuming the correlation is

good, the greater the importance of the relationship. A change in the "X" variable will have a larger

impact on the "Y" variable.

3 http://office.microsoft.com/en-us/excel-help/creating-xy-scatter-and-line-charts-HA001054840.aspx

http://www.itl.nist.gov/div898/handbook/eda/section3/scatterp.htm


iii. Direction: the "X" variable can have a positive or a negative impact on the "Y" variable. As one factor

goes up, the other goes down.

If the pattern of dots slopes from lower left to upper right, it suggests a positive correlation between

the variables being studied.

If the pattern of dots slopes from upper left to lower right, it suggests a negative correlation.

iv. Y Intercept: where a line drawn through the data crosses the "Y" axis. For a positive correlation, it

represents the minimum "Y" value; for a negative correlation it presents the maximum "Y" value.

5.9. Visualization: Treemap Diagram

Definition: Treemaps display hierarchical data as a set of nested rectangles and helps users deal with

problems commonly encountered when dealing with qualitative information.

Treemaps are one of the most important visualizations available today for analyzing multi-dimensional

data and enabling the presentation of highly complex information without sacrificing detail, and easily

visualizing more than numerical attributes in a data set. This is possible because they take advantage of

the human ability to quickly comprehend size and color variations4. Treemaps can answer questions

such as: What are the patterns, clusters, gaps, or outliers? But

Treemaps are NOT IDEAL for some types of numerical data, including:

Data sets with millions of small equally sized nodes o Treemaps are better for analyzing data sets where there is at least one important quantitative

dimension with wide variations.

Time series data o Treemaps are good to display a set of data as it exists at a specific point in time. They can include

dimensions based on historical attributes but they are not suitable for examining historical trends and

patterns in detail. Treemaps are often used in conjunction with time series data visualizations (e.g. Line

Graphs, Stack Graphs) to provide a comprehensive view of the data at a point in time selected from the

time series visualization.

Data sets containing negative values for important quantitative dimensions? o Because Treemaps use the size of rectangles to signal importance, it is not possible to show negative

values in the size dimension.

o Treemaps can show negative values with color codes (e.g. red colors; Treemaps thus become very

useful in cases where negative values are present.

4 There are many different algorithms used to create Treemap structures. These algorithms influence how the rectangles are defined/drawn, making Treemaps very distinct. Not sure which algorithm DI 7 uses, possibly squarified or sliceanddice). http://panopticon.com/images/stories/white_papers/wp_treemap_data_visualizations_for_multi-dimensional_data.pdf

http://panopticon.com/images/stories/white_papers/wp_treemap_data_visualizations_for_multi-dimensional_data.pdf


Structure: Treemaps break areas into rectangles and are analogous to “branches of a tree”. Each

rectangle is further divided into sub-rectangles or sub-branches. Each sub-rectangle is associated with

the following dimensions (color & size being most utilized):

Color: can associated with urgency or some other qualitative parameter

Size: can be associated with the importance of the data point (take care with the definition applied to the term

“importance”, and determine if it refers to a valuation or to a quantity/size.

Select Visualize and go immediately into the table structure.


Then select the Treemap icon to get the following visualization (showing the most recent data available):

Example from DI7: incidence of HIV (%) in the Caucasus and Central Asia by country (appears to be ordered by size only, assigned colors

are random). Explore the data for the Caucasus and Central Asia further by selecting filter options from the left pane. The example below

filtered for most recent data.


Select for all time periods and then double click on the box for Tajikistan

Example from DI7: incidence of HIV (%) in Tajikistan

Double click in a year box and drill down further into the data to reveal any disaggregation available. For example in 2009:


In DI7 the Treemap chart is the only visualization output that cannot be downloaded, or that cannot

have labels (due to technical limitations). It is an interactive visualization. It is possible to share the

visualization by clicking on the Share icon in the graphic, getting the visualization URL or emailing it to

yourself, and sharing it via Facebook/Twitter. You can also save it to the Gallery.

Another option to use the Treemap is to take a screen shot (e.g. using the snipping tool) and copy/paste

it into a document or report. This method was used to bring the Treemap illustrations into this report.

Also see an example from finance: http://www.nytimes.com/interactive/2010/02/01/us/budget.html?_r=0

6. Metadata in DevInfo

6.1. Definition and utility

Metadata is information about various data elements in the DI database. It applies to statistical data (the indicators) as well as the spatial (geographic) data.

Metadata is also about managing the user’s expectations by documenting for them information about what is in the database. For example: origins of the indicators, as well as periodicity, completeness, accuracy, methodology for how the source data behind the indicators was gathered, methodology for how the indicators were calculated, and information on reliability and fitness for purpose.

Metadata in DI may also offer a first level of knowledge management. DI metadata provides basic information about definitions, nomenclatures, source publications, etc. In the DI metadata tool the database administrator can choose to include contact information and hyperlinks to data producers/data owners, additional hyperlinks to relevant methodology reports or other documents or publications linked to the indicators in the DI database.

General rule: any data that comes without any basic metadata should be viewed with caution by the data consumer.

http://www.nytimes.com/interactive/2010/02/01/us/budget.html?_r=0


6.2. Who is responsible to provide metadata? The DI Database Administrator (DI DBA) is responsible for ensuring metadata is entered into the DI database as well as to make edits, changes and updates to the metadata in a timely way. But some aspects of metadata (e.g. descriptions about an indicator, the methodology notes about how the indicator was developed, as well as information about the producer/provider) should come from the data producers themselves.

The unofficial Cookbook Companion DevInfo 7 for the End Useroldstat.ww.tj/images/DevInfo_7_End_User_Guide_ENG_March2014.pdf · DevInfo 7 End User Guide,ENG.docx (updated February

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