 Good morning. Good afternoon. Good evening everyone. Welcome to the day eight of DHS two analytics tools level one Academy. So now we are kind of closing towards the end of our Academy we are now on to the day eight and we just have two more days. And in these two, two days will be mostly doing the comprehensive exercise and the final exam. So, in that sense today will be the final day that will be covering content related to the analytic tools. So I hope that you have learned a lot during this seven days so far. And today is going to be an be the day that you will have the most amount of engagement with the facilitators and yes, because we have we have gone through the reviews and identified few, a few points that you have been constantly highlighting so one of them is sometimes that you feel that there is lack of engagement with the peers like fellow participants as well as facilitators sometimes. And if we agree that, I mean, in fact, like, if any of you have attended our one site academies, we tend to find that those are more engaging, but unfortunately in this, because we have to just use the online method of conducting and there are some inherent limitations when it comes to engagement with other participants due to technology as well and also we are somewhat constrained with time. In fact, like I was mentioning to you I think on day two or three that we will try to like even bring you more content on the two locations that that I mean the two countries who are conducting this are Sri Lanka and India and we'll have some couple of virtual tours but also there was some feedback from you coming that I mean mentioning that most of you find it difficult if we exceed three hours time in a given day so we had really to get more on covering the content and give you as much as possible time to cover the exercise as well. So that's why we could not in fact have even that kind of engaging activities we sometimes do. So apologies for that. Right. So today what we hope to cover, like there are two main topics first thing is about interpretations and the data to action framework. I think like what we will be doing is we will we will introduce you what this data to action framework is and how to write interpretations in the DHS to platform. And then we will have a group activity, where you all, all of you will be divided into eight groups. So they'll be at home. So four to five members per group and you we give you a small assignment where you have to discuss within the group and make a one slide presentation and volunteer, a person who can volunteer from the group it's supposed to present what you have discussed in the group within like two to three minutes time so we can have a small discussion like all of us can contribute even the facilitators. We hope to have eight presentations so that is why we really have to stick to the time. So that's the first part and then the second is going to be about implementation considerations in implementing analytic tools in your DHS to instance. So today like the two topics that we'll be discussing is kind of advanced like you need some background knowledge about DHS to as well as a domain. So in the sense like for especially for the interpretation is about more of the domain expertise on health, whereas for implementation considerations who might need some background on ICT as well to understand fully but we will try our best. So that like all of, I mean, so that we can make it as simplified as possible so none of you will be finding difficult to grasp what we are trying to present. So that's the plan for today. So I think we have decent number of participants to start so let's start the session. The first session on data to action framework. Right. So, the learning objectives for this first session is to understand the rationale of using data to action framework and the components that we have in the data to action framework. And to develop a data to action framework by yourself. So ideally at the end of this session, you should be able to design a data to action framework by yourself. And then we will try to add data to action frameworks to existing DHS to visualize. So that's the plan for today. The initial session is going to initial half of the session is going to be a presentation and I will be demonstrating and then you will have time to engage with others and design your data to action framework. Right. So, first, we have an image a photo here. So I give you the chance to comment on what you see on this particular photo. Why do we put this kind of a photo in DHS to presentation. What do you think about it? I mean, when you look at this photo, what can you say about it? Any guesses? What is this photo about? What is the significance? What can you say about it? Anything? Free talk or need to group discussion. Pamod. Sorry, I could not get your general. Free talk or need to group discussion. No, you're free to talk. Yes, please. Please tell me, like, what do you like seeing this photo? Anything significant? I guess it is, I mean that the passenger, the driver need to ask the passenger where to go. Which location, which direction to go. Okay, great. Yeah, because like the driver is looking backwards and we assume that the passengers, they are on the backseat so maybe like he's talking to the passenger and discuss about the directions or something like that. Right. Anybody else who want to contribute? Like also look around and I mean see what is there in the background and everything. So background really matters. It seems like she's driving the car but she's talking to the passenger behind her. Alright, so while driving the car she's talking. Yeah, she's not focusing on the road. She's not looking at what is ahead of her. She's just talking to the passenger. Yeah, kind of a dangerous thing that she's doing. Any other answers? I think she's looking at something. She's trying to look for something. She's trying to look for something where maybe the one who's taking the photo somewhere else. No, somewhere else. Somewhere else. Okay, right. Alright, all are very valid answers, right? Let's also try to pick where like this is, I mean like from which country kind of this photo might have came from? One of the Arab countries. One of the Arab countries and why so? Judging by her dress and then by the date palm in the background. Good observations, right? Okay, so anybody else who want to contribute? Okay, I mean it's really good like we kind of had a lot of inputs to what we are seeing in this photo. So let me go one step further. In fact, you can see like this is coming from one social media site. You are seeing you have the photo here and you have a small comment or a description that comes along with this photo. Now what do you see here? What do we see here? In Saudi Arabia, women are not legally allowed to drive. So she is trying to make a statement here by driving a car. Exactly. Anybody else? And maybe she is only the female with the driving skill maybe because the rain begins with a single drop. It's something like that. Yeah, in fact, like if you can also focus on which year it is in 2017. So right now what do you see? Initially we were presented with a photograph, a picture taken by someone and we tried to interpret it. So the thing is like we are just constrained with what we are seeing on this photograph and we also have some background information with us. Like the background, it may be like say for example, if I am, if by looking at it, I don't understand if I don't able to make it out that this is from Arab country. I'm going to interpret it in a different way because like that information is something that we are living with our living experience. Some experience that we have gathered from our day to day life and news and things like that. So this is something which is very subjective. So that is why if we just present someone a photograph or a picture, that person will try to interpret with the knowledge that he is having. Sometimes say for example, forgetting that we had this description that person will try to, you know, interpret it in his own way and try to comprehend the idea that we are trying to communicate through this photograph in a totally different way that he only understand. So this is when like it is very important for us to have a description, which is next to a kind of a photograph or maybe some, some, I mean it could be a photograph or even in our DHS to contact context, it could be any visualization like a map or table to have some description that we be informed the person who is trying to interpret this visualization. Some context like this is coming from this program, this country, right, and these may be the contributing data elements, right, so that kind of idea. This is why I'm, what I want to highlight that this is very important for us to give some background, some description, just next to the visualization so that we can communicate the idea bit. In addition, we also see something else. For example, we have this description here and we are seeing these likes and comments. So this is, this is from a social media site and what actually happens is when someone post like this one post now this lady or something expressing her joy regarding what has happened, and what people can do is they can engage, they can engage by sharing their emotions like, for example, I mean you can put a thumbs up or a heart where you express that you like it. And sometimes you can come in, like if the comments could be like sometimes they can, the person who is looking at this thing might disagree or agree or may sometimes even add to what she's trying to communicate here. So this is a nice example that we inherently have in social media about engaging with the end users. So what we actually should try to do now on a different note DHS to itself is again a site or a website or a platform where we try to share visualizations based on data that we have collected right. So what we can actually do is rather than just displaying this visualizations in analytic tools or even dashboards, if we can put this kind of a description, right that is number one, if we can put a description, what we can do is like we can give a better context about from where this data coming to the end user that is number one. And the second thing is, if we can have this kind of features that are there available in social media sites for engaging such as you know like putting likes and commenting right. It could be really engaged. So basically in DHS to be from last time in like, like two to three years we have both this I mean all of all these features engaged in the platform so we have a feature called interpretation, where we love the users who are generating a favorite item or a visualization to put a description that is there. And now we also have the ability to put likes, comments and even subscribe like you subscribe to YouTube YouTube channel. So this is where we are trying to link the social interactions into the DHS to platform for engaging better and sharing information. I hope that is clear. Right. So, let us now go back and try to apply this to a DHS to scenario. So, for example, we see here DPT three coverage by district right this is a map, right, and then we have here a graph. So basically now we have, when we are trying to explain the context, we have to explain based on several dimensions, right. Sorry. Are you having any issues seeing my presentations. No, right. Okay. I think someone is having problem. I just want to reassure. Okay, so one thing we can comment is what are we looking at. Right. So that is one question. And then that that for that we have to provide sufficient information so that the user who is not too familiar. If you can remember on the very first day, I showed you how to do a desk review of using DHS to back. So to do that sometimes we may get a consultant from outside of the country, and that person may not be too much familiar with the, maybe the data dictionary that is using the country or even the geographical distribution things like that. So for that scenario, it is very important that we provide some context about what that person is looking at. That is number one. And the next next thing is, okay, now I can interpret what we see here. So what's next. Now, DHS to is just not totally data repository. Right. So we collect data. But now the end point is like we should be able to make use of data. So whoever who's interpreting maybe at the field level or district national level. They should know after looking at this in at this visualization, what are we supposed to do. Say for example if you are not happy with now. Now if you just focus our attention here. You're not happy with the coverage of bird district. So what is next. So what are we going to do next. So this is where the important concept of action comes to play. So, to do that, what we have to do is, we have to have a good interpretation to provide a context for the visualization, and that interpretation at the end should carry some information about the action. So this is what you see. So if based on what you see in this visualization, if it criteria is these particular parameters do this action. If it is not fulfilling the parameters do something else. So this action component also needs to happen for us to have a better engagement and data use with the end users. So to do that, like, there are so many ways of having this interpretations and designing what you are going to do with the data that that all the visualizations that you see, right. So, the thing is like what I want to highlight is in DHS to we recommend this, this framework called data to action framework, which mainly looks at five, five parameters when you are trying to create a description or interpretation So I'm not saying this is the only framework that you can use. So if your country has your own framework of doing it based on the expert opinion, or if you can adopt another better framework for based on international expertise or similar scenario, you can do that. But this is one framework that most of the countries tend to use because it covers most of the aspects of data sharing engagement and data use. So to do that, for a given visualization to design a data to action framework, we use this five parameters in a DHS to platform. So the first thing that we have to talk about is the indicator. What is this about what what is the data that we are looking. Then we talk about the visualization what exactly are you looking at. And then what is the objective of having this visualization and the data source. And then what is the related action. So we will just go through one by one. The first thing is what is the data. So basically, like this, the first parameter what it means is what is the data item that you are using to generate the visualization. So it could be like, you know, very well in whatever that is coming in the what dimension of DHS to we can use. So essentially we can use data elements or indicators. But usually, we prefer indicators, as opposed to data elements but I'm not saying data elements are not are not, you know, like, of any value. So what is the reason why we usually prefer indicators in generic dashboards. What do you think is the reason. A question for all of you. Indicator basically measures the performance. Can measure the performance, right, but even data elements someone can argue you can use a data element to us, but who have to, like, you know, like assess the performance of a particular district. I think with indicator, we can compare with each other. Exactly. Organization unit. Yeah, that's why I mentioned like so for example if you're just looking at one org unit or one area, maybe data element makes sense, or even like, say for example in in most of the COVID-19 dashboards. We look at a country level save like the cases confirmed, right that's just a raw data element. So that makes sense and it's a very important parameter. So the thing is, when we are looking at at a particular performance indicator at national level we what we actually try to do is we try to compare the different districts. So for us to do a comparison, we definitely needs to, you know, like bring everything into one common baseline. So to do that, we have to use a denominator. So when you use a denominator, it invariance become an indicator and that's why indicators are very commonly practiced in these interpretations and dashboards. But I'm not saying you can't use data elements so there are examples of using data elements as well. So that's the first parameter. The second one is the visualization. So what we mean by the visualization is like how we present our data. So for example in DHS to there are different types of visualizations that you have learned during this academic, we can use a table, we can use a chart, we can use a map, right so there are different types of visualization. So for each context, we can't say which visualization is better. So that totally depends on what you are trying to do. So if you can remember like while I was doing the charts. Session I continuously said it's all up to you, we can't say this one is the perfect because we may be able to say like this one suits better, but then you may have a compelling reason to use something else. So similarly, this is about what type of visualizations we are going to use. The next thing is about the objective of having a visualization. So the thing is now, but what usually happens in most of the countries when we talk about paper based data collection is like we become very over ambitious like people who designed this data collection forms at national level, and we try to include everything. That in fact is a wrong practice because like, if you are just trying to go in that direction, what I mean by that direction is you start with the data collection form. So first we think like what are the parameters that we need to collect from say at field level, and we, you know, like list them out and we make the data collection form. And then we send this paper forms to the field level, and we receive the completed forms from the lower level, maybe up to district or national level. And then what we are going to do, we are then thinking of how to analyze and make some use out of this state. So then we realize that sometimes most, I mean, most of the time, a significant proportion of data items or elements that we have included in the paper form. We are going to analyze. We can't in fact analyze right sometimes we have not ordered them properly so this this data is not. I mean we cannot analyze and sometimes just analyzing doesn't make sense. But like, have you ever thought about what kind of a crime we did each data item, we try to incorporate data data forms. The first thing is we are consuming printing costs right a performs are going to get longer. And somebody has to use their valuable time to collect that information sometimes they are not even available. Right. And then ultimately, we are not making going to make any use of it. So this is why you have to always think like is there any objective in trying to make this interpretation. Right, so this is why this third parameter becomes very important. So when we try to incorporate this parameter into any visualization we always think okay. I mean, do we really need to collect this data. Does it make any sense. If not, we just need don't need to collect right so simple as that. So, and then again, this even, you know, like make things more cleaner so that you know like you don't try to flood the user with multiple visualizations in a given dash we only have the ones which are very necessary for someone to make some proper interpretation. So that's why we have this one called the objective of the visualization why it is important to have this particular visualization. Then something very important to mention what is the data source. So for example, the data source could be multiple. Sometimes, in a case of indicator, the data source may be coming from multiple data sets or data and reforms. Right, you numerator may be coming from one form and the denominator from another. So this is when if you don't mention that one in the visualization. What usually happens is, people are lazy right so you have to keep that in mind whoever who's seen this visualization, they can assume that they are lazy and they are not going to you know, drill down, maybe open a main and the other problem is sometimes they may not have access to the maintenance module in the DHS to them to see what is the data source. So that way, we can make their life very difficult by not sharing the data source so it really makes sense for someone to get a complete idea of the interpretation that we are trying to give by having the data source list. So here in an example of indicator you can mention the now here the numerator the BCG doses given and the denominator number of live birds. And finally, the most important thing, what is the action. And remember this framework is about data to action. So we have collected data, we have structured them nicely so that someone maker can make a very good interpretation of what we are seeing, but what do you have to do, based on the data. So here, if you try to concentrate on what is mentioned. So for example, we tell them what to do for each of the out. So we know, for example, most of these visualizations. So if they are charged so maps, they tend to have legends, right so they give an idea about if this is red color, what does it highlight. So here it says if the BCG cover is is coverages purple, which is like, equal or more than 125% then it says, please review the numerator. Are there any interest errors, or are there any outlines. And also it could be due to a problem in denominator, right, because the value can go up based on an issue with the numerator or the denominator. So if it is the denominator is a target population possible. Maybe the target population is too less. That is why we are seeing 125%. So always check whether this is something that could happen. And then, if it is green, like, more than or equal to 90%, then we ask them sustain the effort okay you're doing good, but keep it up right I mean sometimes what happens is most of the time. If things are fine we don't come in right, but it really matters, you know like even for the end users to motivate them, if you are good, you just have to you know like, accept that they are doing good and appreciate what they are doing. So basically you we have a criteria here, it is over 95 90% give it up. Then, if it is less than 90%, what could happen and what they have to do something very generic so it all depends on like your country context and the target audience that you are looking at say for example here some, but they have mentioned is, we can check whether there is under under voting, why it is less than 90% or whether the staff have conducted education awareness program in the community, right, for them to receive the vaccines, or whether there are adverse effects following immunization in the community. So, but again we have to mention like sometimes we don't tend to when we are presenting we we go with these abbreviations like DCG, AFI, in case if you are not familiar with these abbreviations. So, always feel free to ask in Slack word chat, we can definitely answer, but like we just to go with because like we assume that most of the participants here are somewhat familiar with the health related terms that we are using but but at any any point of time if you are not familiar please let us know, we will definitely explain what these, what these terms mean. And whether the call chain is working fine so call chain is something to do with vaccines. I'm not going into going to mention what it is, and whether significant periods of stockouts have in there. So now you understand right, I mean just looking at this D2A, the data to action framework, if you are just an IT person, without having any any health knowledge, you may be not able to, you know, like design this data action frame. And also if you are just a public health physician or a public health person who doesn't know how this parameters have been configured in DHS to then also you might struggle. So this is where you have to understand. Sometimes configuring is data to action framework is always going to be a group exercise it inputs from me. Okay. So, so what we do finally after you know creating this framework is to put it in DHS to so it is very simple now you have, you all know how to save a favorite item right so after designing the favorite item each art or a map. You click on save as right so that that is where you have to give a name and description details right. So what you can do is in details tab, you have to make mention the data to action framework like this. Okay, so it's basically is a summary of these five parameters that we discussed so so you initially mentioned about indicator visualization is anywhere they are the objective data source and action that follows only four of those right you don't mention the visualization because it's already already displayed here. And also keep in mind when you are typing this visualization in DHS to DHS to support rich text. For you to make this text appear in bold and it is so I mean I'm doing the demonstration I will show you an example on how to type this so that you can format it. Okay. Right. Okay, so the thing is. Now, what you're seeing here is a theoretical framework for data driven decision making, which has been presented at another major forum. Right. So what we try to highlight here is inherently DHS to is a data repository, right where we are collecting data. So that is there. No problem about it. And in DHS to rather than collecting data we try to make the data meaningful by you know like presenting and combining it with another denominator so that you can make indicator so you can get some information out of it. But to turn data into information and then the most important thing is to turn it into knowledge and action. So this knowledge and action that is where some insight has to come from from the context and the public will the knowledge that you have. So that's what we are trying to achieve by having this data to action framework. So when we try to do that. Sometimes you may ask like, at which level we are targeted. Sometimes for that would be like, just to be all the levels. Sometimes you can decide. Like, you can have some certain visualizations, which may be only applicable to like provincial level, right, because the, I mean, you may not really be able to make much sense at the level, but always remember you see it is compulsory and it is really important for you to have some dashboards for the lowest level users as well, because like we don't just, you know, expect to have data use applied only at provincial or district. So it has to be applied at all levels, starting from the lowest most level. So please keep that in mind when you're creating this visualizations about the end users. So that final objective is to not only to collect data, but to make it turn it into useful information and knowledge, which can be used for public health action. So that's the objective of what we are trying to do. So when can we take this approach at any time, right, it doesn't have to be like only after, you know, like, say like after one year of implementation, no, not like that at any time. And every time when you are creating a DHS to visualization. So, again, we have because we are lazy we just try to put a name and save the visualization. And actually, at least when you are trying to do it in the production instance if we can try to put some description detail based on the data direction framework, it really makes sense. And every time you try to comment the DHS to visualization. Right, who uploaded. I mean, I created this visualization, please upload the data direction framework. If it is already not there in the visualization he made. And maybe like because it's a, like, people might feel it's a bit difficult at first but if you really get used to it you will you will see like how few visualizations you actually need to have in your visualizations. If you are not sure what you are trying to show then only you will try to put too many stuff into the dashboard so that it may not make proper sense. All right, so that's it about data direction framework and next I will just try to demonstrate how to do this in DHS to instance any questions up to this point. Right, if there are no questions. So my recommendation is, please provide case study in real practice on the data visualize and take action how to take it and how to translate that visualize to make the action. Definitely. So, in fact, like that's what we are trying to do next, rather than me trying to find a just this presenter case and try to apply to the direction framework, but we will try to do it is within group context, so that so that there will be inputs from multiple people and then we will discuss one by one so that we can have a much better engagement will do like that so that we can also save time. That's fine. Thank you. Okay. So, yeah, how many. So we have, you know, 30 participants. Yeah, I would like to know if there is any standard indicator to say that, like, for instance, here we said, this is the coverage is good if it is like 100 more like if it is 100% is there any standard format or an indicator checklist. Okay, good question. I guess even if we have participants who are representing a development partners they can also come in. So it's like this. So there are some like now is used across multiple programs. There are some standard guidelines based on international recommendations from WTO UNICEF and similar organizations on what would be the standard values values, but in most cases, countries go by their own indicators right because we can just say 90% is a good one, because usually depending on the national program in the Ministry of Health, say for example national immunization program, because the thing is this right now you are comparing across countries so like some countries are developing countries who are really having a lot of communicable diseases and I mean, which you try to address through vaccination programs. So if that is a scenario then they may like think okay the good values must be values which are much much less right. So that way it is, it is totally up to the countries but for some there are some generally accepted indicators. There are others who want to comment or any other participant who's like a public health expert on this but this is what I generally understand. Alright. Okay, so let's now focus on the demonstration. Let me share my screen again. Right. So, now I have logged into the DHS to instance as a national user. Right, and I'm able to see many dashboards. So I'm going to focus on this particular dashboard here called HIV national right. So, in this HIV national program I see multiple items displayed, right. And I'm going to specifically focus on this particular visualization here this map, which is on HIV ART retention last 12 months. So if I just display the legend here we can see like if it is more than 90% that is when they consider as satisfactory. Anything below that are not good. And if it is on red color then that that is not good at all. So the thing is like if I just have a look at the existing data we can see the suite and desert district are the ones that are having satisfactory retention HIV ART retention rates, but all others are having some issues. So, what I try to do next is I just try to click on this button here and it has something called show interpretation and details. Okay, now what I can see is the name is here for this visualization, but I don't see any description on what to do or how to interpret this day, which is not good. Right, so what I can do is I can open this visualization and try to put a data to action frame. Okay, so let me open this by clicking on open in maps application. And here what we can do is we see this. Yeah, now here what we see actually is the the general maps visualization. Okay, and we need to add a interpretation. Okay, so what we do is we click on file and rename. Okay, here we are seeing the name of the visualization but the description is not there. So, to save time, I have already prepared a description which I'm going to copy paste here. Okay, or before doing that let me show show it in notepad so that you'll be able to understand how we do this. So you can see the visualization. I mean the interpretation that I'm going to create. So we have this called now here if you can remember, we have four major topics, one is the indicator, then the objective data source and action to follow. So what we talk about, for example, they are the retention rate after 12 months, that's the indicator. And then the objective, we can specify like to track the percentage of patients, patients retaining on ARP after 12 months of initiation of treatment. And then we are mentioning about the data source from where this indicator, the data to this indicator is coming from. Right. So here we can mention, we have mentioned the numerator denominator. And also if you can, if you think mentioning the data set is better, please do that. You can also mention which data set this indicator is contributed from. So that's, that's about the data source, and then the action to follow. So in this one, you can like, for example, what is different from this one and the actions that I mentioned you in the previous example is here we only comment on what to do if it is more than 90% and what to do if it is less. But if it really makes sense if you really want you can put what to do if it is more than 100 and say, for example, if it is more than 100% what to do that also has to be that you can include. Okay. Right. And then another thing I wanted to mention is the characters that we have the asterisks and the underscores. So, basically, we are using something called Markdown or rich text in DHS to visualizations. So when you're using rich text or what you can do is you can Google something called Markdown, right. Now, this is what. So this is what we use when we are writing the descriptions in DHS to so what do we mean by Markdown is like, now this is a plain text that we are seeing right we don't see any, we don't see, but like if you want to make the text bold or italics, right, when it appears in the DHS to individualize in the interpretation, we can use characters like this. So I'm not going into too much of information on how to use this you can always Google. So, for all purposes for this. During this session, you can remember that if you use this at asterisk mark at the beginning and end. This is the opening a strict and this is a closing one. The text that is between these two asterisks marks is going to get turned into bold, right. Similarly, if it is between the underscores, this is going to turn into italics. Okay, so that's how it works. And here we have again bold. And this is Italy's. Okay, so to see whether this works, what I'm going to do is, I'm going to put it in the description. Right, so I just copy paste it here. Right. It is all saved. And then I click on rename. Right. Fine. I hope it saved. Let me click on interpretations now. And now you can see the text that we saved just now is is nicely displayed here under the interpretation step. Okay, so we have the map details the indicators they are objective data source and action that follows in bold, and then the subsections, those are there in italics. Okay, right. So hope you can interpret it that any questions up to this point so what we what we have done is like now I of course I edited the existing item, favorite item, but what you can actually do when you are like creating a new visualization is when you are going to save it. Okay, at that point, you can mention this data to action framework in the description. And when you do that in the visualization here when you click on the interpretation step, it appears right here. Okay, hope that is clear. Right. So, let me do something. What I can do is, I can try to log in from another user's account. Okay, just give me a second sharing again. Right. So this is another user, a separate user for the academy. So I just try to load refresh the dashboard, the same dashboard HIV national dashboard and I scroll all the way down. Right. And here, I'm seeing the HIV RT retention rate. So I try to click on show interpretations and details. And here now I'm seeing that the interpretations has properly got the the description has got updated properly and it is available to all the users. Okay, right. So here you see a few few things first thing that you see here is you can make a comment to the interpretation here. And also you see a bell icon here, which is a subscribe button. So let me try to click on subscribe button here. Right. And now I'm subscribed. What I will try to do is, I will go back and login again with the previous user here. And then I will go to the dashboard again. Right, so that I see whether I also see the same visualization I should be able to see. And here I can mention, I can either as you can remember, I showed you, I can type it at mark so that it will list out all the users who are there. Right. So if you if I want to specifically address to one one one user I can use it like I can type it like that say for example if I want the attention of admin user, I can type something like you see this, for example. Or else, what I can do is I can make a generic commit. Say for example, all district managers is ensure that you have review ART retention rates. And I will click on save interpretation. Okay, so that's what I did. And now let me try to log back in using the previous user. Hope I'm and I try to refresh the screen. Right. And look what happened. So here I am seeing now previously this interpretation this this button here, it was blank. And now I'm seeing it shows one notification. So that means there is one notification available for this user. So let me try to click on it and see what happens. All right, so here I'm seeing all the notifications which are available for this user. And you see why I got this notification because I have subscribed to this particular favorite item and because the other the one person made some comment, which was the district managers please ensure that you have review the retention rates. I'm getting this alert, so that I don't miss this. So every time when there's an activity or engagement to already subscribed item, there is an alert that is coming the interpretation spot so that I don't miss it. So similarly now what I can do is I can comment on here or else. I can go here. And even from the dashboard. I can comment on this interpretation. So what I can do is I can reply, right so that it goes under the main thread noted. And then click on save. So it is nicely displayed here. As a threat. Is that clear. Right. So I hope that makes sense. And again, let me try to open this in the maps application this visualization, right, and try to show you how this interpretation or the description and the comments that the person has made appears in this interpretations panel. So I click a click here. Some reason here it has not got updated. I will have to check on that but what I wanted to highlight here is, here you see the subscribe button. Right, so because I'm already subscribed here, I can click on this button to unsubscribe from this interpretation, right, and then I should be ideally seeing all the comments made under this interpretation. I can see the description as a threat below. And in this, I'm able to see few engagement buttons, right, I can see like, I can see reply, I can see share, edit, and delete. If I have the proper access controls to do this particular activity. Now here I'm seeing the view button and look what happened, right. I mean, my other comment is there now. So look what happens when I click on this exit and view, right, when I click on view button, you will see the same visualization and it will mention weaving interpretation from 26 May. Right. What actually happens here is what basically like the problem that we can encounter is now when we, you know, like because so many people will be commenting and you know, interpreting what they see. And maybe a comment I have made here three days back, it may not properly reflect what was actually there in the visualization three days back. So that's the, that's the thing that we are trying to address here, because now this is a kind of a living dashboard and data can get, you know, populated and based on that the visualization can get updated. And if you like, try to see a comment that is made by someone about three, four days back, maybe the visualization that loads by default may not make proper sense. So that's why you can click on this view button here so that it will try to highlight what was there in the visualization at the point of time that person tried to make that comment. Is that clear. So that's what this view button does so that you go back in time and try to see what context this person has made this comment. Okay. Right. So, I guess that is all I have to cover mainly in the interpretations. So what I actually did in this demonstration is I showed you how to open existing favorite item, right, you can just open it here. And if that favorite item is lacking interpretation what you can do is you can click on this rename button because we try to modify existing one. Okay, and we want the same one to appear in the dashboard as well orders you can click on save as but then what happens is it will create a new favorite item, and you may have to add that item again into the dashboard because it's a new one. Change the existing one, but we'll do is we will save it here. Okay, and we put a description based on the data to action framework and I also showed you like you can use this markdown characters to make it little X and both. Right. And then I also showed you what is this interpretation panel what it has so it has the description, and it has the subscribe button, and it will also have the comments made by the end users. And I mentioned to you what are these each of this ones are doing, and also specifically about this view button, which shows that which will highlight which will bring bring up the visualization that was shown at the time the person made the comment. And also I highlighted to you that there is this the interpretation button, in which we will get notifications every time somebody makes an engagement. And also, alright, yeah, I can also show this now for example this is my email account of this particular user. Like, every time when someone makes a count businesses, makes a comment to a item that I have subscribed, I will see some, I will be getting notification alert from me to email last week that of course you will have to configure. Right, so you have to configure your, your phs to instance, with the mail, you know, with the mail server credentials number one, and you have to have the email account set up for that particular user. And you have to also number three is you have to enable the permission or the from the user side to receive notifications through email. So I assume that's all what we have to discuss about the interaction framework and interpretations. So what we can do next is a group activity. So basically seeing it in the, in the edX, what you have to do. So, what we expect to do in simple terms, is to produce a one slide presentation PowerPoint presentation, which have those five columns, which are highlighted in the data direction So, let me share. So, basically, what we have to do is something like this. Okay, so I just want you to do something like this. Okay. Create something like share again. Okay, the activity is this and mind you this will be a group activity so you, we will open zoom breakout rooms. So once we do that, how many participants. So how are we going to do this server. We have 35 so how many we, how about we, we will have maybe six groups, so that, otherwise it will be too many, because we need to spend time on discussing as well so we will make six groups so that you will be randomly allocated to one of the groups breakout rooms. So in that room what you have to do is you have to discuss amongst participants. So please listen carefully what you have to do. So, say supposing like because we have around 35 people we may have around five to six people per group. So what you have to do is go through your DHS to instance, that is number one, and find a visualization existing to which you can create this data to action frame. Right, so it could be any, anything right so for example it could be don't use the ones the examples that we have already discussed such as the BCG coverage and the ART retention so other than BCG coverage and ART retention select any visualization. But the only issue is we, we don't want all of you to discuss about the same visualization which is there right so please let us know in advance in Slack. We will make the groups as 123456. So each group please let us know in advance what is the particular visualization like what is the indicator name that you are selecting right so that we can I mean so it's so it's totally first come first serve basis like if you select it first and announce it in the in the Slack, then another group will not be able to take it. Okay, so let us know what is the indicator or the visualization that you select that is number one. Right, so in within the group, decide what to include for this slide. Right, I mean for each of the column what you are going to include so for example, here the name is kind of straightforward, the visualization. Right, so in fact, even though we just asked you to use existing visualization, you can comment, this is the visualization which is there at the moment but we would prefer to have. Not this, maybe something else you can even maybe even create like say for example you think map is not ideal but it should be a chart, you can put a chart here saying this is what we have here, but we can put something else. And then write the objective the data source and the data the related actions right. So this in fact like especially completed these three columns you will have to discuss amongst a group and do it, and what we ideally want. So we will give you around 20 minutes is that enough sorrow but do you think 20 minutes. It's a polite that that's also how we can find it and how to yeah it's there and the exercises in edX. Let me get back to that shortly, but like I just wanted to explain the procedure so what we want to do is like once you make this slide in PowerPoint. We want one person of each group to make a like a brief two minutes so maybe maximum three minutes take three minutes and explain to entire audience to all of us. How you interpreted this what is there in each of the columns and just make a brief interpretation and then everyone can come in. Right, so going back to your question. It should be the exercise should be available in the edX. So, let me put it up. So, in the data to action framework. Today's exercise, there is an option called exercise download right. So when you click on exercise download. It should download a presentation, a blank presentation, which is something like, let me share my screen, something like this should be there. So, what you have to do is, you can refer this one. I mean this is just for your reference, right, and you can use the slide number three to complete this slide based on the visualization that you see it. Okay, and what you actually have to do is to present this slide number three. Is it clear. Any questions. Right, so if there are no questions, so by you ready for to create the breakout rooms mentioned to the participants before any instructions. I'm just adding all to the course. Right okay. So how many groups we have six right. Yes, yes. Yeah, yeah. So once out of opens the the six rooms the breakout rooms you all will end up in the breakout rooms right. And then what you have to do is within the group, please select one visualization. And as soon as you select it, please announce us what you selected in which channel. You can announce it in assignments channel right in the assignments channel in slack. Please let us know. I'm group number one we selected this right so that way everyone can see the topic the visualizations we have already selected, and then in the groups discuss how to complete this related to action framework and complete that slide, and maybe get ready in like 20 minutes so that we will discuss one by one. So, when sort of is ready we can start the breakout rooms and then you will discuss. Okay, right. So, see you in 20 minutes.