A trainer’s guide to the Data Analysis overview and Data Visualizer session

What is this guide? This guide is a support document for the trainers of the DATIM session called “Data Analysis overview and Data Visualizer”. This session follows the standard DATIM training approach with 1) a live demo session where the trainer demonstrate and explain the features in DATIM, and 2) a hands­on session with exercises where participants get to practice the same features. This guide will help the trainer prepare for the live demo session.The “Live Demo step by step with guidance” section has a detailed walkthrough of all the steps to demonstrate with explanations and screenshots that should be easy to follow. Use that when preparing for the live demo session. There is also a Quick Guide which lists the steps very briefly and this is meant as a lookup guide or “cheatsheet” WHILE doing the demo, to help the trainer remember all the steps and the flow of the demo.

Learning objectives for this session

1. Understand what the various data analysis features are 2. Understand how data is structured along various dimensions 3. Understand how to create charts in Data Visualiser 4. Understand how to create data analysis favourites and use these in Dashboards 5. Understand how to build up a new Dashboard

Time needed Live demo: 45 mins Hands­on exercises: 1 hour

Background Data Analysis in DATIM is organised around the Dashboards, where data can be displayed in charts, maps, or tables. Dashboards can be tailored to specific groups or users as well as individuals and is a powerful way to disseminate data analysis outputs to all DATIM users. The analysis contents of the Dashboards are created in one of the three Data Analysis tools; Data Visualiser for charts, Pivot Tables for dynamic tabular analysis, and GIS for thematic mapping. All products from these tools can be saved as favourites which allows for re­use of the same products (charts, maps, tables) over time and across sites/areas. Favourites can be shared with other users (in groups or as individuals) and can easily be added to a Dashboard.

The three analysis modules have been developed with seamless integration in mind. You can easily move from chart to table to maps and back, displaying the same selected set of data. The menu system and data selection approach are very similar between the three modules, and Data Visualiser and Pivot Tables are with a few exceptions exactly the same.

Best practices, tips & tricks For many users it is difficult to understand how the analysis tools work, especially creating new products from scratch. Start by showing existing favourites and do minor changes to these, to reduce the initial barriers of getting started with these tools. Allow for enough time for practical hands­on exercises with the analysis tools. Demos are important to explain the possibilities and features of the system, but hands­on exercises is the only way to learn how to use these tools. In order to remember all the nitty gritty details of using these tools it is recommended to practice frequently, e.g. once a week, if not it will be easy to forget what you/they learned. After all it is meant to be a routine tool for data analysis. Spend enough time on explaining the various data dimensions, how data is structured in the system and how users can find the data. Without a good understanding of where to find the data and what granularity is available, it will be very difficult to make use of the data analysis tools. The Dashboard is the central point for training and exercises on analysis tools. Make sure that all exercises end up with new analysis items being added to the Dashboard. This will also help participants to establish a routine of saving and reusing analytics.

Preparations Make sure you have a user account that is member of the “DATIM ToT Team” user group. That will give you access to the dashboards used in this demo. The account should also just have the same access as the participant’s data analysis accounts so that you don’t show features and content that the participants will not be able to see or access when they start to practice. Make sure to remove/hide the “4. Treatment” dashboard, as this demo will populate this dashboard as an example of how a dashboard can be created/populated. It’s a good idea to open (and then close) all charts, tables, and maps in this demo right before the demo so that they are all cached in your browser. That will make the demo much faster. So do not clear browser cache just before this demo, then the system will have to load all this again.

­­­­­­ This page is useful to print out and use while doing the demo ­­­­­­

Quick Guide - overview of steps for the live demo User/login for the demo: Use one of the demo_analysis_NN accounts, same as the ones provided to the participants. During the live demo of DATIM focus on the following steps:

1. Log in and explain Dashboard basics

2. Show and explain contents of Dashboard 1: Overview.

3. Show and explain contents of Dashboard 2: Testing.

4. Make a new Dashboard for Treatment

5. Create new charts in Data Visualiser and explain all the steps

of data selection and chart layout

6. Show drill­downs on org hierarchy as well as PEPFAR

reporting hierarchy

7. Show examples of moving from chart to table and table to

map and add tables and maps to the same Dashboard

8. In pivot tables show how to do tables disaggregated by Sex

9. Explain the concept of sharing Dashboards and show how

that works

Live Demo step by step with guidance (in detail) User: Use one of the demo_analysis_NN accounts, same as the ones provided to the participants.

Step 1: First Login - Dashboard Basics Log in and show the Dashboard on first login. As shown below.

Explain that the Dashboards are linked to the user logged in. Name of the user is displayed in top left corner. Dashboards can be either private (for one user only), shared with user groups, or public (for all users). Sharing of Dashboards will be explained later in the session.

A user can have multiple Dashboards and these are accessible from the Dashboard tabs as shown in the image above. In this case the Dashboards available to the user are: DL 1: Overview, DL 2: Testing, DL 3: Care, DL 4: Treatment, DL 5: VMMC, and DL 0: Training Resources Dashboard. In this session we will look in more detail at the 1: Overview and 2: Testing dashboards, and then build up the 4: Treatment dashboard which is currently missing.

Step 2 - show the Dashboard “1: Overview” The idea of this Dashboard is to give a brief overview of the data in the system, showing key data from MER in Demoland. This Dashboard has a total of 7 items, as you can see below a mix of charts, tables, and maps, as well as a few useful shortcuts at the bottom.

For each dashboard item you are given 5 options:

1. Remove : Removes the item from the dashboard (only works on dashboards you have edit access for)

2. Get as image: Downloads dashboard item as image to your device 3. Share Interpretation: Allows the user to share an interpretation of the dashboard

item 4. Explore: Takes the user to the full analysis tool in which the dashboard item was

built to further investigate the data 5. Resize: Allows the user to resize dashboards items in small, medium and large

sizes Go through each of the 6 visualisations on the DL 1: Overview dashboard and explain what you see: 1. Demoland Numerator Results by District, FY14. This is showing tested, VMMC,

clinical care, new on treatment, and current on treatment aggregated by district for FY 13­14. Data is collected by site level and can easily be aggregated to any level above that.

2. Demoland: HIV­positivity (%) by Agency FY 13­14 (Bar chart). This is a calculated indicator value showing % of positives by total tested for each agency (aggregated values for all mechanisms in Demoland for each agency).

3. Demoland: MER Overview FY 13­14 (table). Show totals for all sites in Demoland for FY 13­14 for some key selected data; Tested, New on treatment, Current on treatment, Clinical Care (min 1 visit), and Voluntary Circumcision (VVMC).

4. Demoland: New on ART by Agency FY 13­14 (pie chart). This shows the New on ART data for the whole demoland (all sites and mechanisms added together) and then disaggregated by Funding Agency. It is clear that USAID is the most active agency in Demoland from this chart.

5. Demoland: HIV yield and HTC total tests FY 13­14 (map). This map shows the number of positives as red points/dots on top of district polygons with the total amount of HTC tests. Larger red dots means more positives and darker blue districts means more HTC tests. This allows us to review the relationship between the amount of tests performed and the amount of positives found.

6. Demoland: All data by site FY 13­14 (table). This table displays all of the data element data that is currently available for each individual site within Demoland

The last three Dashboard is a list of my favourite users, the ones that I contact frequently. You can send messages to users inside DATIM.

Step 3 - Show the Dashboard “2: Testing” Use the Dashboard tabs and click on “2: Testing” to show a new Dashboard.

Explain the contents of the “2: Testing dashboard. Go through each of the 11 visualisations. This Dashboard is a good example of how the same data can be shown using different dimensions.You can see that the HTC tested is shown by district as totals, by gender, and test result (positive, negative). Also, the overall age breakdown is shown. Aggregations by different levels in the org hierarchy from site level to district and the country totals. Data is also displayed by quarterly results as well as annual aggregates. This illustrates some of the flexibility of the system. Once the data is in the system with details such as age groups, gender, test results, and at site level, then it can be visualised with the same level of detail or gradually aggregated up as subtotals and totals and for any level in the org hierarchy at or above the level where it was collected. Here are explanations to the dashboard items on “2: Testing”: 1. Demoland HTC Results Received by Sex, Quarterly FY14. Show total tested by

male and female for the four quarters of FY14. Very even distribution across quarters as Demoland is perfect.

2. Demoland HTC Received Results by district FY14 (bar chart). Shows number of tested in demoland aggregated by districts.It is clear that Bird district is the one with by far the most tests, and Dessert district the one with the fewest.

3. Demoland HTC Received Results by Age Group, FY14 (pie chart). This shows number of tested by district disaggregated by age groups. This is a calculated subtotal of what is collected in the MER form via its age disaggregations.

4. Demoland HTC Positive results male vs. female, FY14. This pie charts compares positive test results for male vs females.

5. Demoland HTC Results Received Negative/Positive by District, FY14. Test disaggregated by results (positive or negative) across districts for FY14.

6. Demoland HIV positivity (%),FY14. A fancy “Dashboard chart”. This is an overview indicator taking the number of positives and dividing by the total number of tests received in order to calculate yield.

7. Demoland HTC Positives by age group and sex, FY14. Same as again, but this time showing both age group and sex together for the positive results only.This are

again calculated subtotals of what is collected in the MER form (age and sex disaggregations).

8. Demland HTC Received Results by Sex/District, FY14. Total tested, this time with sex breakdown across the 8 districts. The female to male ratio seems to be almost the same across all districts.

9. Demoland HIV positivity (%) by district FY 14. Same positivity indicator, this time shown with district comparison. Bird and Dessert seem to be the most “yielding” districts, in terms of percentages. If we compare with the chart in item #5 with number of positives we see that Bird has many more positives than Dessert. The relatively equal % positivity then indicates a much higher number of total tests in Bird district, which we can confirm by looking at item #2. Dinner district has very low yield, which is also confirmed by looking at the numbers of positives/negatives for Dinner district in item #5.

10. Demoland HTC Received Results by Site, FY14 (map). The map shows number of received results on a colour scale from yellow to green. Larger, greener dots meaning higher number of received results, while smaller, yellow dots indicate a lower number of received results.

11. Demoland: HTC Positive Results Received by site, FY14. This is a rather wide chart showing all sites in demoland, sorted from high to low. An excellent example of when the Resize option is useful. We can see that a small percentage of the total sites contribute to a very high percentage of the total testing in the country. You can hover over the chart to display the site names and their values. See screenshot below.

Step 4 - Create a new Dashboard for Treatment First show how to create a new Dashboard by clicking on the “Add” button (under Profile in top left corner). Call the new Dashboard for “DL 4: Treatment”.

First we will show how to add a few new visualisation and then we will add these to the new Dashboard.

Step 5 - Create a new chart in Data Visualizer Open Data Visualizer from the App menu.

Now demonstrate how to create a new chart “New and Current on ART by district FY14” from scratch. It is always useful to start by sketching out on a piece of paper what the final visualisation should look like. Here is a sketch for the chart we would like to create: (Note: You can put this image in a powerpoint presentation and show it to the participants at this point in the demo.)

Explain briefly what the different data dimensions are. You can read up on this in the info box called “Understanding DATIM data dimensions”. Also explain what chart types are supported in the tool and how you can change Chart type using the Type bar in the top left corner. Explain the use of the “Update” button. All this is explained in the screenshot below.

Understanding DATIM data dimensions

When selecting what data to visualise in data analysis you need to make selections in terms of “what”, “where” and “when” the data is describing. “What” is found in either indicators (calculated by formulas) or data elements (raw data as collected in the data entry forms). Both are organised as groups, so know your groups to quickly find what you are looking for.

“Where” is found under Organisation Units, that is the location hierarchy from global to country and finally to sites, with all the intermediary levels in between. Know your orgunit levels to quickly navigate to the correct locations. “When” is found under “Periods”. There you can specify what periodicity or time you want to visualise data for. Periods are organised by Period Types (“frequency groups”), so to find “January 2014” you must look under Period type “Monthly”. “January­March 2014” is found under “Quarterly”, “2014” is found under “Yearly”, and “October 2013 ­ September 2014” is found under “Financial October” which is the one to use for Pepfar financial years. In addition to these “core dimensions” there are quite a few additional dimension providing more granularity to the “what” dimension, e.g. the age, sex and test results dimensions. The PEPFAR reporting hierarchy is also represented here. The three dimensions “Funding Agency”, “Implementing Partner” and “Funding Mechanisms” can be used to look at different levels of aggregation up the “PEPFAR hierarchy”.

Step 5a - Select options for the “what” dimension (indicators/data elements) Explain again the difference between indicators and data elements. New and current on treatment are both data elements (raw numbers collected in the data entry forms), so we look for them under the “Data elements” dimension. Data elements are organised by groups to make it easier to find. For Demoland all relevant data elements are grouped in a group called “DL all demo data”, so for all data analysis in Demoland you can always find what you need in this group. You will see that after the group has been selected a list of data elements appear under Available (left side list).

Understanding data element disaggregations and nomenclature in DATIM

To select the correct data elements for data analysis it is important to understand the structure of the data elements in DATIM. Some data elements in DATIM are used to only capture a total value (sometimes referred to as the “Numerator” value). Other data elements are used to capture more fine­grained data disaggregated by age groups or gender, or test results etc. It depends on the context which disaggregation that is used. In DATIM each disaggregation category can be used in the data analysis tools as a data dimension. You will see that e.g. Age groups and Sex are both listed as possible dimensions in Data Visualiser or Pivot Tables. If you combine a data element and the Age dimension it will only show any results (values) if the selected data element is actually used to capture data disaggregated by Age. So it is important to understand what disaggregations that are available for the various data elements. The naming convention used for Data Elements is such that it is possible to derive from the name itself what level of granularity or disaggregation is available for the data element. This is sample list of available data elements for HTC that are used in Demoland:

HTC_TST (N, DSD): HTC received results HTC_TST (N, DSD, Age/Sex/Result): HTC received results The first part of the name; “HTC_TST” refers to the PEPFAR “indicator” for HTC Testing. The part of the name in brackets provides useful information on the granularity of the data, meaning which disaggregation categories that is available for this data element. The last part of the name (after the brackets) provides more details to the “PEPFAR indicator”, in this case “HTC received results”. So in this case the data elements listed above provide the number of received results from HTC testing. The difference is found in the disaggregations (information in brackets): (N,DSD) : This is the Numerator (N) value, which means the total. This one does not have any disaggregation. It is also DSD, which refers to the type of support provided, in this case Direct Service Delivery (DSD). All the three above are for DSD. (N, DSD, Age/Sex/Results): Again you can get the numerator value. And again it is DSD. But here you can also get values disaggregated by Age, Sex and Results, so there is an additional level of detail here. For this data element it is possible to get the total results, the disaggregated data by positive and negative test results, male/female sex disaggregations and the age groups used on the MER forms. This means that you can use the Dimensions called “Age (all groups),” “Sex,” and “Test Results” when analysing data for this data element if you want to view data by certain disaggregations. In the full DATIM environment there are many more disaggregation categories for MER (and other data), but they all follow the same naming convention so it should be possible to understand what the available disaggregation dimensions are for data analysis by looking at the names of the data elements.

Explain how to find the correct data elements for New and Current on treatment. From the list of available data elements look for TX_NEW and TX_CURR, the abbreviations for new and current on treatment. In this case we don’t need any disaggregation by Age or Sex, so we pick the data elements with Numerator (total) values only ­ the ones with (N,DSD) in the name. You can read more about data elements and available disaggregations in the infobox called “Understanding data element disaggregations and nomenclature in DATIM”.

Step 5b - Select options for the “When” dimension (Periods) Go to Periods and explain how to find the “September 2013 ­ October 2014” period. All data in demoland is for the financial year of September 2013 ­ October 2014.

The default selection is “Last financial year.” We can either use this or specify which period of interest we are interested in. If we want to specify, make sure to deselect Last financial year. In DATIM the PEPFAR financial years are grouped under the Period Type called “Financial October”. In the Period Type drop­down (says “Select period type”) select “Financial October”. From the list of available financial years look for September 2013 ­ October 2014. Either double­click on the name or single­click + use the arrow to move the period across to the Selected list. As an alternative to using the so­called fixed periods as explained above, you can also select either the “Last Financial year” or the “Last 5 financial years” periods by using the relative periods listed under Financial years, as show in the screenshot below:

The Last Financial year will always be the last completed FY, so as of March 2015 that will be FY13­14 (Oct­Sep). Relative charts are very useful when used in favourites that you would like to automatically update when new periods of data become available. There are similar options for Weekly, Monthly, Quarterly and Yearly (calendar years), that can be used when data is available in the system for those period types.

Step 5c - select options for the “Where” dimension (Organisation Units) Go to the Organisation units tabs in the left side data dimensions menu. Explain the different selection modes for orgunits:

1) Select organisation units 2) Select levels 3) Select groups

Select organisation units ­ this is the default way of selecting orgunits, simply selecting the units in the hierarchy that you want to use in the chart. By default the “root” or top level unit is selected in the hierarchy. For most users this will be the Demoland unit or a country unit on the real production environment. If you just want the country totals simply leave it like that with Demoland selected.The orgunit hierarchy supports multiple selections, so if you press Ctrl key at the same time as you click on orgunits you will be able to select multiple

units. So one way to select all districts is then to expand the two region units and select all the districts in each of the two regions, one by one using the Ctrl key on the keyboard and the left mouse button, as illustrated in the screenshot below:

This is the most flexible way of selecting orgunits as any combination of selected units is allowed, mixing units from different levels etc. Select levels mode may be faster and more convenient when selecting all units at the same level.

*A Note on Organisation Unit Levels

Past the Province/Region level, countries may not use the same naming convention or hierarchy. Levels after Province/Region could include district, zone, etc. While further lower levels could include site, health facility, etc. Therefore, after the Province/Region level, Organisation Unit levels have been given generic names such as In­country level 3, 4, etc. These follow the hierarchy of the organisation units in DATIM.

First change mode to “Select levels” by clicking on the settings symbol: .

Then select In­country level 3 as the level (in the drop­down box) and make sure Demoland is selected in the hierarchy. This will give us all units at the District level in Demoland. If we just wanted districts in Food Region we could change the selected unit in the hierarchy from Demoland to Food Region. In this chart we want all districts in Demoland, so the selection should be as in the screenshot below:

Select groups is the third way of selecting orgunits. Orgunits can be grouped in as many orgunit groups as you like, and these groups can then be used e.g. as a filter to select all units of a particular type or some common set of characteristics. This could e.g. be a hospital group for all hospital units or a public group for all public health facilities. This grouping feature has not yet been utilised in demoland, so there are no examples to show here. But the idea is the same as for levels, you select a group and then select a unit in the hierarchy and all units of the selected group within the sub­hierarchy of the selected unit will be used in the chart. If you now click on the <Update> button you will see the following chart:

We can see that although the data elements look correct, the chart layout does not exactly reflect the sketch we are working after. In the next step we will see how to modify the layout to visualise the districts on the X axis.

Step 6 - Define the chart layout To modify the layout of the chart, click on Layout in the top menu (next to the Update button). In the layout window you can drag and drop data dimensions across the 3 main chart areas; Filter, Series, and Category. The Dimension are is more like a “waiting area” and is not used in the chart itself, but convenient when you would like to remove a dimension from the chart quickly, or add it back later. Series: A series is a set of continuous, related elements (e.g. periods or data elements) which you want to visualize in order to emphasize trends or relations in its data. Categories: A category is a set of elements (e.g. indicators or organisation units) for which you want to compare its data. Filter: Since most charts are two­dimensional, a filter must be used on the third dimension in order to use only a single element for the chart to become meaningful. Here is an example chart showing the layout with this terminology: (Note: This is another image that it is useful to put in a powerpoint presentation and show to the users at this point in the demo.)

For the current chart we can see from the screenshot below that the Period dimension is in the Category area, and that is why the “Oct 2013 ­ Sep 2014” period is listed as a Category on the X axis. We also see that the Organisation units dimension is placed in the Filter are and that is why all the districts names are listed in the heading of the charts and not shown on the chart itself. the “Data” dimension (the data elements) is located in the Series area and we can see the legend showing blue and green for the two data elements we have selected. So the Data dimension is correct, but we need to swap position for Period and Organisation units.

The layout window should instead look like this:

When we click Update the chart will change and look like this:

This layout is the same as on the sketch (see below) we wanted to replicate, so we are done!

Step 7 - Save the chart as a favourite To put the chart on your dashboard or to be able to open it later inside the Data Visualiser, you need to save it as a Favourite. Click on Favourite and then on “Add new”. Write in the name of your favourite in the pop­up window as shown below.

You will soon realise that there will be many favourites in the same system and that good naming conventions are important in order to make it easier to search and find what you are looking for. Prefix the name with some keywords that easily identify your favourites In this case we have used “Demoland” to easily identify all favourites linked to our Demoland and we have also used “Treatment” as a way to quickly find all charts related to treatment. The name should also provide enough detail on what it contains, in this case the two data elements New and Current on ART, the orgunit dimension “by district”, and the period “FY14”. If you used the Last Financial year you should replace FY with “last FY”, e.g. “Demoland: Treatment New and Current on ART by district last FY”. With this name it is easy to both search for the chart as well as understand what it contains by looking at its name in a list. Favourites can also be opened inside Data Visualiser so that there is no need to create the same chart from scratch every time. Just click on Favourites in the top menu again and a list of available favourites will appear. You can use the search field to quickly get to the favourites you would like to see, e.g. type “Treatment” or “HTC” to get all favourites that include those keywords.

Step 8 - add the chart to the new “4: Treatment” dashboard Show how to add the new chart favourite to the new Treatment Dashboard. First exit the Data Visualiser by clicking on the Home button in the top right corner. That will take you back to the Dashboard. Make sure the new “DL 4: Treatment” Dashboard is selected. To add a new dashboard item to a dashboard simply search for it in the Search field. Type “Treatment” to look for the one we just added. Click on the Add link to add the chart to your dashboard.

The new dashboard will then look like this:

Step 9 - Add a new chart “New on ART by Agency” Now we would like to add another chart to our new dashboard, and a pie chart showing the distribution of new on ART by agency. Something like this very rough sketch:

From the new Treatment dashboard, click on the chart for New and Current on ART to open it in Data Visualiser. Starting from that chart go ahead and make a pie chart for “New on ART by Agency FY14”. Explain how it can we convenient to open existing favourites and manipulate the charts from there instead of starting from scratch every time. Explain how to make the necessary changes to the data element and organisation unit dimensions:

Data elements: Remove the “TX_CURR (N, DSD): Receiving ART” data element from the selected list by double clicking on it. Organisation units: We want the total for demoland, so change selection mode to “Select Organisation units” and then select Demoland in the hierarchy. Period is still FY 13­14 (or Last Financial year), so leave it as is. Agency dimension: Click on Funding Agency and select all the four options there; DOD, USAID, Peace Corps, and HHS/CDC. (If you have more agencies visible in your list then you have more privileges/access than the inter­agency data analysis user role that the participants in Demoland will use.) Change the chart type to Pie chart and click on “Update” to see what the new chart looks like. The pie has only one color (green) with a legend saying Demoland, and there are no slices per agency as we would like to see. Click on Layout to see what the current layout is. We can see that the Funding Agency was automatically put in the Series area.

For pie charts the dimension to use for the slices of the chart needs to be in the Category area of the layout, as shown in the example pie chart below:

So this explains why the pie is showing one big green orgunit and the legend says “Demoland”. So put Funding Agency on Category, Organisation units on Filter, and the Data on Series, and click Update. The new chart should now show a Pie with one slice per agency, like this:

Save the chart as a new favourite, call it “Demoland: Treatment New on ART by Agency, FY14”. Go back to the Dashboard and add the new chart to the “4: Treatment” dashboard.

Step 10 - Orgunit drill-down in Data Visualizer Open Data Visualiser from the Apps menu. Demonstrate how we can drill down in the orgunit hierarchy and go from a national total of New on ART to show the same data element by region, then by district and finally by site level. Modify the selections to show a bar chart with New on ART, FY 14 and Demoland with demoland on the Category (X axis). The chart should look like this:

Then go to Organisation unit dimension and drill­down to the Regional level by right clicking on Demoland and “Select sub­units”. This will select both regions under Demoland. Click Update. The chart should look like this:

Animal region has the by far highest number of New on ART. Let’s drill down into that region to see how this is distributed across the districts. In Organisation unit dimension, right click on Animal Region and “Select sub­units”. Click Update. The new chart should look like this:

Bird district has by far the highest value with 1228 New on ART. Drill down to see sites in Bird District. The new chart should look like this:

The site by site comparison in Bird District shows that the Parrot site, followed by the Woodpecker site are the biggest contributors to the high value of New on ART in Bird District, as well as in the whole Animal Region. To see how these sites compare to all other sites in Demoland we can try to show all sites at the same time. In Organisation unit dimension, change selection mode to “Select levels”, select the “In­country level 4” in the level drop­down and Demoland from the hierarchy. Update. To sort the sites in ascending order, go to Option on the top menu and under Sort order select “Low to High”. Then Update again. The chart should look like this:

Not easy to read here in this document, but you will see in your chart that most of the Bird District sites (with values of 120 and higher) are to the far right of this chart among the sites with the highest number of New on ART in FY14 in Demoland. You can also see that there is a very long tail of sites with very low activity.

Step 11 - Create a table for New and Current on ART by Age/Sex disaggregation The Chart above does not display all the site names properly so it is a good idea to open this chart in the Pivot Tables to see this properly. This also demonstrates how the Data Visualiser and the Pivot tables tools are integrated and allow for easy swapping between charts and tables for the same selections of data. From the analysis tool shortcuts in the top right corner, select Table, and “Open this chart as a pivot table”.

After showing the site level table ­ show how to clear the table. To clear the table go to Table in the top right corner and select “Clear pivot table”, as shown below:

Now we will create a new table for “New and Current on ART by Sex disaggregation”. We already made a chart showing New and Current on ART by district so it makes sense to start from that when creating the table. Show how to go back to the Data Visualiser by using the link in the top right corner and select Charts­>Go to charts. Show how to open an existing favourite “Demoland: New and Current on ART by district FY14”. Then open that chart in Pivot Tables using the link in the top right corner, Table­>Open this chart as table. That will take you straight to the Pivot Table tool and visualise a table like this:

We want to see these data elements disaggregated by Sex. So we need to add this data dimension to our table. Go to the left side data dimensions menu, which is exactly the same as in Data Visualizer, and locate Sex. Select the appropriate options, and then click on “Update”. You will get a pop­up saying “No values found”, like this:

This is simply telling us that there are no existing values for our data selection. The combination of data dimension options must be wrong somehow. The changes we made were related to the Sex dimensions so likely this introduced a problem. Remember from earlier steps that not all the data elements have available disaggregations, so when asking for Age, Sex or Test Results disaggregations make sure to select the data elements that actually have these. Go to Data elements and look at the selected data elements and what the other elements in the “DL all demo data” group are.

From the list above we can see that the selected data elements both are of the type (N,DSD), which means it is numerator only. In the available list there are two similar data elements for TX_NEW and TX_CURR that both have (N, DSD, Age/Sex) as part of their name. These will give us the disaggregations we want. Remove the two Numerator type elements and add the other two with Age/Sex disaggregations and then click “Update”. The table should now look like this:

Now we can see the Sex disaggregation for the two data elements. There are still a few minor adjustments we can do to this table to make it look better. The Total on the right most column does not make much sense as in combines New and Current. Go to Options in the top menu and untick the option called “Show row totals”. Leave the other totals and subtotals as is, they are useful. The option selection should look like this:

After clicking update the row total column should be gone.

Save the table as a favourite with the name “Demoland Treatment New and Current on ART by Sex and District FY14”. ĺ

Step 12 - Create a table for New on ART by site FY14 Continue from the previous table and make the necessary data dimension changes to show New on ART by site in descending order, with sites listed on rows down the table.

Data elements: Remove the TX_CURR data element. The new table does not need any disaggregations, so replace TX_NEW data element with the (N, DSD) version of it. Organisation units: Change the selected level from In­country level 3 to In­country level 4. Make sure only Site is selected (the list allows for multiple selections). Sex Dimension: Remove the selections for this dimension Click Update. You will see that the table is sorted alphabetically. Sort by the New on ART values simply by clicking on the small arrows next to the data element name on the column header in the table. The top of your table should then look like this:

Save the table as a favourite with the name “Demoland Treatment New on ART by Site, FY14”.

Step 13 - Create a map with New on ART by site In this step we will demonstrate how you can quickly turn a table into a map and create a new map for the Treatment dashboard showing New on ART by site for FY14. Still in the Pivot Table tool and with the previous table open, click on the Map link in the top right corner and select “Open this table as map”.

This will take you straight to the GIS module and open the same data as a map showing New on ART for FY14 on a map with points where the location of the points are based on the coordinates for the sites and size of the points are based on the values of the New on ART data element for the sites. The map should look like this:

The chart above is a very pure map showing the site locations. We can add the district boundaries to turn this into a more useful map. This can be done using the Boundary layer, from the top menu look for the globe­like symbol to the left of the “1“, select it and click Edit layer. Select the In­country level 3 and Demoland from the hierarchy, just like the level selector for Data Visualizer or Pivot Tables. Click Update. Now you will see district boundaries on the map. Click on Options, tick the Show labels option, increase the font size to 14 and click Update. Demoland is located in the Atlantic Ocean so the Google Maps background is kind of blue. To remove the Google Maps background locate Layer Stack in the top right corner and untick the selection for Google Streets. Now your map should look like this:

This is more useful. From the legend on the middle right you can see the range of values for the different colors of the points, as well as the selected data element and period. Not surprisingly Bird district has the sites with the highest values for New on ART for FY14. Save the map as a Favourite with the name “Demoland Treatment New on ART by site FY14”.

Step 14 - Add the new tables and map to the Treatment dashboard From GIS click Home in the top right corner to return to the Dashboard. Make sure the “4: Treatment” dashboard is selected and then search for your recently created dashboard items. Type “Treatment” and see what hits you get. Should be something like this:

Explain how the Dashboard items are organised by the different types; Charts, Maps, Pivot tables. Add the map and the two tables to your Dashboard using the Add links. You can rearrange the order of the items on your dashboard by dragging and dropping them. Put the bar chart in the first position (top left), then the map, then the pie chart, followed by the Age/Sex table and the finally the table with ART new by site. Your Dashboard should now look like this (given your screen size and zoom level give you a 3 items per row dashboard):

Step 15 - Share the Treatment dashboard with the Demoland Analysis User Group Start by explaining the sharing concept. Dashboards (as well as other DATIM objects) can be private ­ visible only to the user who made it, or shared with other users through the user groups. You can share either view only rights or full Edit (Read and Write) To allow other users to see or edit the Treatment dashboard we can share it.

You can now ask the participants to refresh their dashboard page to check whether they can see the new Treatment dashboard that was shared with them.