Welcome to Digital Asset News, the top stories in cryptocurrency and digital assets, and break them down to bite-sized pieces. So today, let's talk about what's going on with this dip in the market, and we'll take a look at what has happened in the past, what is happening right now, and just to realize that it's really just more par for the course. On top of that, we'll take a look at a new public earnings report, which is put out by Coinbase ahead of their April 14th IPO. And then we'll finish up with a couple of great stories. One is from StormX, and we had Simon Yu on the channel over there at Alex Masioli, what he talked about, and what is happening behind the scenes. And then finally, just as a quick update, IRS is catching crypto tax sheet via summons. So that's always fantastic when you hear this great news, and we'll go over what you can do with all that. But first, let's take a look at what is exactly going on today. So right now, it is April 7th, it is almost 10 a.m. El Paso, Texas time. And hey, the market just took a little bit of a dip, huh? So we lost like 100 billion market cap, but not a big deal. And I say that with the most amount of respect and assurance. I know if you're new to this space, you're like, what the heck just happened? Well, crypto just happened. That's just how it is. So yeah, we won over 2 trillion, then we dropped down to 1.8, 1.86 billion or something like that. 1.86 trillion. And these things happen. So don't be alarmed. That's just what it is. And we'll take a look in a bit. So let's just break it down to actually what is happening. Let me blow this up so you can see it with Bitcoin in general. So Bitcoin topped out around $61,000, now we're at $56,000. Ethereum has dipped below the $2,000, Binance Coin through $76,000. Even though Binance Coin is up in seven days, 22%. XRP up 68%. But over the last 24 hours, everything is pretty much down. There's some things that are up. But I mean, for the most part, that's what it is. Theta is up 7%. That's crazy. Good for them. 18% for Solana and so on and so forth. So yeah, this just happens. And that's just pretty much how it is.  You can take a look below for a link to use the sentiment analysis. So look at digital note, Harmony, StormEx, Raiden Network, Bau Finance, Linear, and Solana as over the next hour or so. You could look at 13%, 5%, 4%, 5%. Gains based on sentiment analysis. Anyhow, that's what's going on the market. Let's just jump into exactly what we have as far as the history. So the history, and I like to always bring this up because I know when people are especially new or even if you've been around for a while, you're probably looking at these things like, man, I don't really think this is right. Should this really drop this much? Yes, it should. And here's a quick little snippet that I've been saving about from the last bull run. So just remember in 2016, 2017, everything goes in four-year cycles for the last eight years. Now, past performance does not equal out to present day what could happen. But over the last eight years, this is what's been going on. And we've had things drop down 38%, 38%. 33, 38, 36.9. As we took that big, huge parabolic run up in 2017 when Bitcoin hit a measly $20,000, the market cap was a paltry $840 billion somewhere around there. And Alts had a little bit of a spur, which now seems almost laughable. Can you imagine right now if we went down to less than a trillion dollars, people would lose their minds? Me personally, I'd be like, well, thanks for the sale. I'm pretty happy. I appreciate that. And I'll buy some more. But that's what's going on in the past. And of course, we just had a bit of a dip again. And just like this is a nice little chart right here from early December, January 2021. And we see 20% dips, 18% dips, as we just continue to elevate. So here's the thing. Again, if you're new, this is very scary, but just calm down. This is in the traditional markets. We are expecting these things. And usually when things go like this, we say to buy the dip. That's very hard. If you went all in when it was doing this, it's very hard to do that. And that's why I say don't do that. So what I want you to do are actually, I can't give you financial advice, I'm sorry to even say that. But what I do is I dollar cost average. And I have a set amount and every day I buy a certain amount of cryptocurrency and that's pretty much it. And then over time, I've been doing this for the last since 2017. And it seems to work out pretty well over time. Now, if you're in it for a get rich quick, there are no get rich quick schemes. I mean, you can pick the right one and kind of ride that train. But it's very risky. And that is not me. But there's plenty of YouTubers you can follow. And they'll tell you all day long about which 1000 X gems are out there and go follow them. But for me, it's just slow and steady, usually works out just trying to be an investor, keep level headed. And then off I go. The big thing for me is I don't really care about today. I don't really care about these dips, because I know, or I think I know, or I think I have a good understanding of where things could potentially go. I just look at the past for your dips, having all time I dip reset this from 2012. Then again, 2016 to 2019, same thing. And 2020 is going to repeat. Now, is it going to go all the way up? I have no idea. Is the bear market going to last for two years or so? I have no idea. But I can tell you this, 2021 is going to be a fantastic year. There are so many institutions, so many people coming out. There are so many people getting into this space. This is, you can never give guarantees, but it's looking pretty good. Anyhow, let me just think about that in the comment section. Let's move on to our next piece. So next up, congratulations Coinbase. That sounds funny coming out of my mouth. I'm not a big fan of Coinbase because as you get into the market, you start to realize the fees that are pretty crazy. And for some people, they realize that. But I have to always take a step back and look at, it's not just about me, it's about everybody. And a lot of people who are coming in Coinbase is the easiest option. It is the simplest. There are no bunch of graphs and charts and market prices and set limits, everything else is just buy and go. Super simple. And everybody seems to agree that sometimes with simplicity, you have to have that price. And that's just what it is. So Coinbase is going to go public. They're having their IPO on April 14th. So they have to start to report these different metrics. And one of those is they just said this, and I'm just going to, I'm going to skip over all the text because a picture is worth 1,000 words. And this is what we've got right here. Let me blow this up so you can see this. This is potentially just it. This is all you got to really know. So in quarter one, this is in 2019. Look at this all the way to the left. 66 million in quarter one 2019. Remember, 2017 was like the big year. And look at that. 66 million. Quarter two, 211, 159, 98, 191. Quarter two, 2020, 186, then almost doubled in Q320. Q4, 2020 was monstrous. I'm sure when they went from 315 to 585, again, almost a double. So they went from 186, 315 to 585. They're like, wow, are we doing better? Look at Q1, 2021. 1.8 billion.  They are crushing it. They are crushing it. And of course, yes, you're going to say that they are crushing it based on everybody's fees and what people are paying for. And you would be correct. But it is what it is. So when people talk about Coinbase and how good or bad it is, this is what we actually have. What's wrong? Let me jump back. So really just to finish up this article, I just talked about a couple of metrics. This was from CEO Brian Armstrong where he says, hey, on our Q1 earnings call on Tuesday, we announced 56 million verified users for Q1, 2021. So that's just the users that they have all signed up and are actually there in the system. That's not the monthly users. Coinbase says it has monthly transaction users of 6.1 million. So I know when people talk about, they reach out to Coinbase and they try to get customer service and they don't get anything. Maybe it's because they got so many people on there. And that is a lot more than I thought they actually had. But again, that is no excuse. If you're making that much money, you can afford to hire people and get them going. But I will tell you this, I've never made over a billion dollars, not yet. But if I had all that money, it's still be very difficult to get all the people in there, all the people train the right way, answer the questions correctly and get them on track to up to speed where they need to be to answer all your questions. So again, growing pains and we're seeing a lot of that, especially with Voyager. And I know there's a lot of different issues going on. And we're trying to get Steve on the show again, and hopefully he can answer what's going on there. And that is what's going on with Coinbase itself. And April 14, they say is going to be a catalyst for the cryptocurrency market. I tend to think that anything after IRS taxes really starts to set things up. That's why you always see like April, not is a pretty good month towards the end and then May. But of course, in the US, we've been pushed from April 15 tax deadline into May 15. So I think May and May June will be a pretty great year after this IPO. So I'm interested in the comments section. Let's move on to our last two pieces. So next up, StormX. So if you're on the channel, I'd like to invest into people. I mean, projects are great. You have to have good projects or you have to have a good working use case. And there's a lot of those out there. But I like to invest into people. And we had Simon Yu from StormX over on Alex Massioli's channel. I'm going to link this, the video to what we talked about. And it was great. And he had a lot of great answers and a lot of things that they're trying to do as far as like pushing things forward. And these are not a fly by night type of operation. They've been in business since 2014.  And when you go to the website, there's a section there to actually download the Google Chrome or Brave browser extension. You can also get the app on your iPhone and Android.  That's the easiest example I can take. Right. So when you go to any kind of these websites, this little thing will pop up and I'll say like up to 2% crypto cashback activate now. And then you just activate it whatever you buy, you get cashback and crypto and things really do add up after a while. And I think it's a pretty cool thing, especially since it's very global. I mean, it's global and it's going to be out there. The problem is, is that it took a big dump yesterday. I thought it was a great interview. I thought, man, maybe that interview kind of sucked.  And then I realized that nice little tweet today says, we are so sorry for me that Walmart is no longer available in StormX. Starting at 10pm today, we're going to bring them back online in the future. But so look, when you're out there, and I reason I brought this up, is because when you're out there and you're looking at all these different low-cap gems, 1000X, 100X, just make sure that the project actually does something. It has a working product and actually is functioning and can do things. And like Simon was talking about on the show, he's like, look, 90% of crypto can't do squat. You can't do anything right now. It's just a speculative market. He goes, with our token, I mean, yeah, you can stake it. And then of course, you get more cashback rewards, the more tokens that you have. He's like, we've been in business since 2014. We have over 750 and I was like, you know what, I like that. And so I talk about charity and giving back and giving more money to the users, makes sense to me. So when you're looking at these things out there, just look at, does the project actually do something? Have they been around for a little bit of time? And how does everything work as far as the project itself and everything that encompasses? So just take a look at those. And that is the thing with StormX. Let me know what you think about that in the comments section. And let's move on to our last piece. And really quickly, I will just say this. The IRS wants your money. This is in the United States. I know a lot of people are from Australia, Loassi's, UK, India, and some and a lot of parts of Southeast Asia and, and Sub-Saharan Africa. So this is just for the United States. This is what's going on. But if you don't realize, we're printing a ton of money like it's going on a style. And this is the easiest way to get that some of that money back. Is the tax live in tar out of every man, woman, even, well, every man and woman out there. So what's going on here? So the IRS is actively hunting for crypto tax cheats by demanding crypto exchanges release user information through John Doe Simmons summons. On March 30th, 2021, a John Doe summons was issued to Kraken. On April 1st, it was also issued to Circle. The John Doe summons issued to Kraken, Employee X require exchanges to release user information from 2016 to 2020 on accounts with at least $20,000 in transaction value in any of those years. Across the IRS commissioner, Chuck Reddick, the John Doe summons is a step to enable the IRS to uncover those who are failing to properly report their virtual currency transactions. We will enforce the law where we find systemic non-compliance or fraud. And just so you know, tools like John Doe summons authorized today send the clear message to taxpayers that the IRS is working to ensure they are fully compliant in their use of virtual currencies. And last thing, taxpayers who have properly filed their crypto taxes during the years under investigation 2016 to 2020 should not be concerned. So let me just say this. If I read that and your heart started to skip a beat because maybe you didn't do something. Maybe you didn't report whatever it is. It's okay. It's going to be all right. So let's get you on track. I know there's always going to be some of the comments that say taxation is theft and you don't have to pay taxes and da-da-da. Sure. I'm not saying you don't have to pay taxes. I mean, yeah, I am. I'm saying you have to pay taxes.  So I mean, if you don't want to pay taxes in the US, there's a lot of options, I guess, for you. But if you want to go through an audit like I did, have fun with that. It's pretty awful. So what I did is I reached out to David, a camera. He is the co-founder over there at CryptoTrader.tax just to see what's going on and how he can help us. So just so you know, I use CryptoTrader.tax personally. I've used it the last two years. I trust them thoroughly. I, from the time I started it up to actually got done, took me 30 minutes except for Voyager.  But everything else was an API integration, very smooth, sent it over, everything was good, done. If you want to go through a lot of different documents and something like that, go right ahead. And if you don't have just a few transactions, you don't probably need CryptoTrader.tax. This is for somebody who's got like 50, 100, 200 transactions. They don't want to deal with all the things. So let's talk to David real quick and he can fill us in about what's going on with taxes. Everybody, I want to welcome David Kemmer. He is one of the co-founders of CryptoTrader.tax. David, thanks for coming on and talking to us about these issues. Like the article we just talked about, it seems like people are, it concerns me greatly as the IRS starts to really pull their resources together to really focus in on just a little guy, you know, just a little guy investing. So just talk to us about what you've seen over there, over there CryptoTrader and what do you think is coming down the pipe? Yeah. Yeah. It's not a surprise, right? We've seen crypto like explode in value over the last 12 months, which is great, right? And it fuels the space and companies like ours and just everyone in the space is doing well. And the IRS sees that and sees, hey, there's not much 1099 information reporting that's happening in terms of, you know, the Coinbase is in the crackings of the world, reporting on customer information, which is very common in the equities world, which you could parallel to kind of how crypto is taxed. So the IRS sees that as this huge tax gap, right? They see that there's millions and millions of people in the U.S. who are investing in likely making significant amount of money on crypto. And then they see on tax returns that, you know, a very small percentage of people are reporting that they had any type of crypto income on their tax return. So there's that gap. And Chuck Redding is actively trying to close that gap and he's becoming very aggressive about it. Of course, we see that again with the subpoenas now to Circle and Rumored to Crackin. Exactly how they went after Coinbase in 2015, I believe is how they're going after the new folks. So it'll get sorted out in the courts and it's likely that Circle and Crackin will have to turn over some amount of customer records to the IRS just like Coinbase had to. But yeah, they're getting serious and they have been for a while, right? It's not a secret. Yeah, see, because like I remember just a couple of years ago, oh, maybe it wasn't even last year, where they were sending out, no, I think it was the last two years, they were sending out letters and going, hey, Rob, we think you may have unreported some things. So, you know, wink, wink, nudge, nudge, because you just give us an information. And it was like no big deal. But now it's more like subpoenas going out, going, well, I mean, subpoenas are the exchanges. But I think what's going to happen coming on the pipe is that, look, we're doing a ton of quantitative easing. There's a lot of money being pushed out and printed. And there's really one big way to get that revenue back. And that is just a tax to live in tar out of people like you and me. So this is why I always talk about unportedism in your taxes. I know nobody wants to, there's only one assurance, no debt and taxes. But like I said before, I did this last year, I did this again, the time that I actually popped open the website and put all my information in with the API, took me about 30 minutes, sent over to my account, everything was done. The only one I had to wait for was Voyager, but that was because they needed to do that CSV file. So yeah, you guys can't really help with that. But I've already introduced you to Steve, so maybe something like that will come on the pipe. Yeah, it's actually already better now. So I've connected with Steve, he's a great guy. And so that's more streamlined now. But yeah, you're right. It's not something that we want to do, anyone wants to do, but I do think it's smart to cover your bases. Perfect. And then real quick, I get this question a lot, which is about staking and how that works out with taxes. Because first of all, do we have to pay tax on the staking? And second of all, how does crypto trader do that? Can they do that? Can it be reported or we have to do it manually, which would suck? Right, exactly. So it very much depends a little bit on how the staking actually happens for some pools and incentive programs. It happens differently where the staking reward is a crew, but they're not actually available in your wallet. But let's say in a traditional example where I'm staking assets and I'm getting dripped staking rewards into my wallet. Every time that you receive staking rewards, that's just treated as income at the fair market value if you're in the US in US dollars at the time you receive it. So if I'm staking, let's say Ethereum on the new 2.0, and I receive a staking reward of one Ethereum today, which wouldn't happen, but I would recognize around $2,000 of income today. And then that's added to your taxable income. Now that income becomes your cost basis moving forward. So now for that one Ethereum that I received, that's I have a $2,000 cost basis. So let's say I eat skyrockets in price and goes to $4,000. If I sell that Ethereum, I have a $2,000 basis, I sell it for $4,000. So there's also a $2,000 capital gain on the sale of the property. So that's kind of how staking works. And with Crypto TraderBot Tax, there's so much development activity, new protocols, new governance models launching really every day, which is great for the space. Makes it gives us a tough job for my team, but we're scaling up fast now. So we're actively hiring if anyone in your audience wants to check out our career page. But in terms of just deeper integrations, you and I offline, we're talking about Carvedano, the staking rewards there. So we don't support that right now, but all of these things are on our roadmap, and it just takes time. So if you're receiving staking rewards from things like directly on Coinbase, you can now crack into, we pull that in no problem right now. If you're on Uniswap doing liquidical stuff, we rolled out Uniswap a couple months ago. So we're just going to keep doing that. We're going to keep doing what we do, rolling out more and more integrations so that there is no manual work. And you can just click a few buttons and we'll spit out your necessary stuff. So that's our job. And the next nine months, there's a lot to do. But it'll be good. Yeah, it'll be good. Yeah, thanks for not making me do more work. I mean, no one likes my work, so thanks. We appreciate it. And then just real quick, I know we talked about how like if they're staking on the exchanges, you guys pretty much cover that as far as the whole spectrum, right? Yeah, exactly. Because that's just a little bit easier. We already consume all the exchanges, APIs. So they just made that available. We can pull in staking reward, classify that under your account in our system. It's not as difficult as like going out to the blockchain data, like Cardano, right? Ethereum's a little easier. But this is technical jargon. Yes, for the exchanges, no problem. That's, we've got that covered. Perfect. And then the last thing I want to talk about is what's coming down the pipe with you guys? Yeah. What's happening that can really like propel crypto trader like the next level? So the first answer is more the same, right? We have a model that's working. And so we just want to do more of that. So hire more people to build more integrations, to build better features that are just going to do exactly what you and I have said. No manual work, super simple, you know, tax reporting for any use case in crypto. Outside of that, we do want to move into a lot more portfolio tracking type capabilities. So, you know, real time P&L across all of your platforms. And that's a little bit more on the future, but it's going to be awesome once we launch that. And that will be like completely free to use for fun, like, you know, downloading your tax reports. So there's a lot of ways we can go with it, but those are the two big things, right? It's just more and more and more integrations, DeFi margin, right? All this stuff. And then moving into portfolio tracking, real time tracking. Perfect. Well, David, thanks for coming on the show. We all appreciate it because no one likes to do taxes, so there's just a couple of things. If you look in the description below, there's going to be a link to crypto trader.tax.  On top of that, there's a video where I show you exactly how to use crypto trader.tax. It's very simple, API integration, pulls all your information in, very slick. Unless you got Kucoin, that was my only one that I had a problem with because they had like three transactions, but it doesn't really matter. So on top of that, there's also a nice little thing going on with crypto trader. They are doing a weekly giveaway for a tax or an unlimited tax report, $300 value. Just put your first name and email, enter to win, David always draws it, and then emails those people.  I will also link what David was talking about as far as the information to work with crypto trader, the careers page, link that in the description as well. And that is it. David, any last words for us? No, I love your channel, Rob, and it's a pleasure just jumping on and chatting with you. So thanks for having me on. Great, thanks for coming by. All right, so I'm glad David got my message to wear his classic tee. I'm just kidding, he just wore a tee shirt. And that is it for today. So look, if you found a value in this video, please give it a thumbs up. That helps tremendously. Also consider subscribing because that always is great. A lot of things we talk about are time sensitive, and I will link everything below in the description, pin a couple things at the top so you can find easily. And that's it for today. So thanks for watching, I appreciate it, and I'll see you on the next one.


A warm welcome please for Srivenas Gopinath. Welcome. Thank you. Thank you. So, thanks for that introduction, that good piece of introduction. It felt very nice. And, well, that philosophy bit in my introduction actually, it came because I didn't know what to put, you know, when I signed up to speak here I was asked to give some personal tidbit. And that's the thing that came to my mind. But what we are going to talk about today is anything but philosophy. Let me be very clear about it. Okay, so it's a deep dive session into data analytics, which is the big data analytics system. We have developed for government of Andhra Pradesh as part of ePragati. So, without further delays, let me get straight into it. So, we have heard about the data analytics, the big data analytics, Hadoop, and the need to have an enterprise-wide analytics system in corporate sectors. But trying to do that in a government sector and trying to do a whole-of-state analytics system is a different ball game altogether. So, you know, we had to first build a strong business case and then conceptualize it first of all. You know, why? Why we need an entire statewide analytics system? And then next question would be how and then what? So, those are the three phases, which I'm going to describe broadly today. So, this is the concept. So, the concept is data-driven governance. So, what is data-driven governance? It's an approach that values decision backed up with data that can be verified, usually with the help of statistical and business intelligence methodologies. This is becoming more and more prominent these days, particularly with the innovation in the field of computing. So, we have this distributed computing, massively parallel processing technologies and all that. So, in the older days, when we had these large processors and supercomputers which were very expensive, people tried to avoid these kind of big initiatives. But with the technology we have today, it's very viable and feasible to do this. So, on top of that, we have the availability of data, external data that is, and the technology to process them. So, some of the facts and figures are given here. You can see that by 2012, White House had already invested more than 200 million dollars in big data analytics system. And then you have UK government which was able to locate almost 7 billion pound additional revenue due to analytics, by leveraging analytics. So, data-driven governance in brief is to leverage the data and the technology to process data to improve the functioning of government and enhance the services provided to the citizens. So, value proposition. So, some of them are listed here. These are pretty common value propositions that you will find with any data-driven governance initiative across the world. Fraud and pilferage, prevention of fraud and pilferage of funds, better engagement with citizens, effective usage of funds, and managing public perception of government. This is very interesting because you will see that these days with the advent of social media, citizens have started engaging themselves in government and it becomes imperative for governments to hear what citizens have to say and to work closely with them. So, that's about the public perception of government. So, the government need not wait until the general elections to understand what people think about them. So, it's there, it's available live. So, something happens today, some significant event in government today. The government will come to know about public perception the next day within 24 hours. So, that's the strength of social media in governments. So, that's an important angle to consider. We'll come back to it later, but these are the value propositions. Typically, data-driven governance had a traditional approach, right? If you see, governments had already started doing some kind of analytics a few years ago. And these were mostly at the department level. So, for example, the tax department or revenue department might have had a small analytics engine to find out who is paying tax and who is not paying tax and those kind of things. But what we are going towards is a whole of government level initiative and enterprise-wide analytic system. Again, this gels in nicely with the concept of enterprise architecture and JSR and Dr. Saha already mentioned this many times that it's very important to think as a unit, as one government or rather than thinking as separate departments. So, who knows? The department available in... sorry, the data available in one department may be very useful for some other department. So, you may have the revenue department which would want data from, let us say, power and other departments to find out the energy consumption pattern and then see if an entity is paying tax the way it should be paying or not. So, that exchange of data and getting the maximum value out of the data is most important from a government analytics perspective. So, again, leadership is also important. As you can see, the last point talks about enterprise strategy. You have a leader at the top who will drive all these initiatives rather than having leaders in the department level or sub-department levels and not trying to drive large initiatives. Moving on. So, as I said, governments have already started with the concept of data-driven governance. In AP, we have a core dashboard and some amount of analytics and reporting available to the chief minister at his fingertips. And also, of course, AP is one of the first states in India to recognize the importance of data and getting the value out of data to run a government. But there are areas for improvement and a couple of them are mentioned below, the predictive and prescriptive analytic capabilities. So, that is where governments would want to look at. So, if you are able to support your decision or if you are able to predict something and then decide and then if somebody asks, why did you take this specific decision? You could easily give some kind of reasoning. You could support your argument with some data, facts. Well, that will also avoid a lot of controversies. So, it becomes much easier for people to explain why they have done what they have done and reduced controversies. So, predictive and prescriptive analytics and then, of course, data collection and governance strategies. Two very important critical success factors for any program involving data and IT. So, these are the two areas of focus which we have identified, but there are many not going to details of those aspects. So, we have this animal called Datalytics, which is big data analytics and it has a vision and then, you know, it has a definition and it is there because of a reason. So, the vision is there, of course, to support the sunrise AP 2022 vision and how by providing an integrated whole-of-government business intelligence and big data analytics platform. And the definition is, of course, business intelligence and data analytics system, conventional and unconventional, which is big data. Big data, of course, includes all kind of data. It's wrong to categorize big data as only unstructured data, but big data is all data. So, in the context of the ePregative program, the Datalytics fits into the support layer. You can see there dotted circle, along with systems such as CLGS, Certificate Less Governance System, Payment Gateway, et cetera. Datalytics is an important supporting application. The reason is that you really do not give any direct, you do not really get any direct G2C services out of Datalytics. So, it's not that citizens directly use this analytics engine. I mean, some entities might use the system, but ultimately, it is meant to support the departments and the government to better provide G2C services. So, it is, therefore, a supporting process. Well, it could also be seen as an application or a system that can enhance productivity, but ultimately, fundamentally, it's a support process, right? So, that's why it's in that layer. Well, any program has to be supported with strong business cases or use cases. So, unless you come up with some business reasoning and use the scenarios, nobody would buy your idea. So, this is one of the most important things that we did in the beginning when we started with this concept called data analytics. So, it said, where is that we want to use analytics? You know, there were arguments both for and against having an analytics system. You know, some people said that why do you need analytics in government? Government can run without all this big technology stuff. But yes, government can definitely run, but there is some value out of coming out of these systems that might be very, very useful for governments. So, we went about building these use cases. We spoke to many departments, tried to explain them what an analytics system is, and then, you know, tried to solicit that information from them, data from them, and not try to build these scenarios. Broadly, you can categorize these use cases into two. One is supplementing existing business processes or department processes, and then creating new business processes or department processes. Now, this is one use case in primary sector where we have a service identified already, pest control and prevention of these uses. Now, the question is, what value can data analytics bring here? So, we were dependent on experts to go to do the field visits to see what's happening in the field and then, you know, get some data from satellite and weather department and then put them all together and come up with some analysis and then, you know, say, okay, most likely, this is what is going to happen this season. You might have specific type of pest attack because the conditions are favorable, and these are mostly expert opinions based on their own experiences and etc. But what can data analytics bring now? So, we have the capability to analyze data from multiple sources, right? So, it could be something happening in the neighboring state, for example, Karnataka or Tamil Nadu or Telangana. Something might have happened there which might be very useful for us to know, right? And then, you may have a lot of articles on the web which talks about specific kind of disease or pest attack. And then, for you to gather this information, this bits of pieces of information from various sources and then put them all together and understand it's manually impossible. So, now you have a system which can do that for you and can actually give you some kind of a prediction of what might happen, right? And then, it can be presented in a way that is easily understandable by people across departments at all levels. So, you have the field level officers, you have the department HODs. So, each one needs information, specific piece of information in a specific format. So, you have that capability to deliver now. So, that's the value addition here. So, you have using internal as well as external data and advanced analytics to predict disease and pest attacks well in advance and help farmers and departments take remedial actions, right? So, this is one case. Opportunities for creating new services. Again, this is another scenario. A typical scenario, gaining insights into relationship between student enrollment, attendance and dropout rates and condition of schools, availability of basic facilities, toilets and etc. And then, you do an analysis and then you find out, okay, that probably most likely the high dropout rates or the low past percentages are because of these reasons. People are not able to commute from their homes to the school or it's too hot. The weather might be very hot for people to just walk. They don't have commutation facilities. So, then you bring the classroom to the people, right? So, you have mobile classrooms as a concept. So, you can think about delivering educational services through mobile classrooms. And there are a few more use cases. These are typical standard use cases. You have the commercial tax department trying to identify the anomalous and dealer behavior. The department of energy trying to do some load analysis, PR department doing sentiment analysis and all that. These are typical and you can find them in any literature relating to analytics and government. Of course, some of these also come from our interaction with the department. So, it's more customized to AP. Okay, so now you have the concept of data-driven governance. So, people have bought it, bought the idea. So, now how to go about doing it? How to about realizing this concept and coming up with a system that can be used to drive the governance, to drive data-driven governance. The normal way of coming up with ideas is brainstorming. You sit in a conference room or in an enclosed place and then with a whiteboard and then you scribble something on the board and then you pick brains of people around you and also from outside, et cetera. So, that's typical brainstorming strategy. But to give it some kind of a structure, we have something called as TOWS, Tau's Analysis, which is basically SWAT plus strategy alternatives. So, you identify the SWAT strengths, weakness, opportunities and threats. You list them down as a project team and then you come up with alternatives for each combination of the strength, weakness and opportunities and threats. So, you'll see the X-axis here, the green boxes are strengths and weaknesses. The red boxes are opportunities and threats and the blue boxes are where they intersect. So, you have some strategic alternatives in each of the blue boxes. Some key strategic alternatives I have picked. If you see the first one, it says develop strategy to use data from sensors, social media and internet for analytics. So, it becomes very clear that you need to have a big data analytics system and you have a use case here, a business use case. And similarly, if you see point number 5, project leadership. So, we all know now how important leadership is in an enterprise architecture initiative. So, how can that be leveraged to get the laws, rules or processes changed? You know, sometimes these things become bottlenecks for information exchange. So, you know, they need to be fixed before you can now go ahead with the initiatives. And of course, there is leadership skills to motivate and bring in more volunteers and technology experts. And all these things have happened. I've seen it happening. We have seen it happening in our team. So, now we have strategic alternatives. So, we know the data-driven governance is important and then we know what alternatives we have. Now, we have to crystallize them into a package and bring a system, an application system out of it. So, that is the process of building architecture. We have the enterprise architecture considerations, the principles, the processes, the standards, etc. And then we have the need to produce value quickly and incrementally. Right? So, that's also an important consideration, like Dr. Saha mentioned. It's important to think like an implementer rather than just like somebody, you know, who is doing some consulting and lip service. And then, of course, the governance is a key aspect. It's one of the critical success factors. And we have the governance body to see that, to see through the implementation of data analytics system and ensure that it meets its desired objectives. So, now we have come to the phase where we have to decide what are the analytical capabilities that we need that the government needs. All right? Broadly speaking, it's easier for us to imagine a scenario where you need all the capabilities that's available. But that's not viable or not even feasible. So, we have to carefully plan and then prioritize and then select the top ones and implement them. Although your architecture should be flexible enough to undergo gradual improvements over a period of time. But the way you implement, you know, should be based on priority and, you know, the need of the R. And there are important considerations around creating an analytical system, the collection, data collection and enrichment strategy. Right? That's the point number four. As you can see, it's a very important aspect. And the way we do it, the way we plan the analytical capability, so we have the user cases which is our stories which is given to us by the departments. And then we know what analytical requirements the departments need. We now go and identify the data points or the data sets that we require in order to fulfill the analytical requirement. And then we go in search of the data sources. So, once we build this matrix, it becomes easier for us to come up with a data collection strategy. So, that's why it's bolded there, you can see. And capability development planning is also an important aspect. That is, you know, it's fine to develop a system and give it to the government, but the government should be able to manage the system and take it forward. Right? So, the government should have some capability on its own to maintain and upgrade the system. So, all these aspects like training, skill sets, hiring, et cetera comes into the picture there. Data governance is an important aspect too. As I mentioned earlier, it's a critical success factor. And I have a slide then to discuss about the governance, aspects of governance later on. Yeah, here it is. So, data governance. So, define, approve, communicate data strategies, policies, standards, architecture, procedures and metrics. Enforce policies, standards and procedures and make sure that all the applications, including data analytics system, adheres to those. And of course, the oversight at overseeing the delivery of data analytics projects. That's also an important function of the government. Reducing bottleneck issues, identifying the department processes, rules or laws that hinder data exchange, all these become part of the governance team. Last but not the least, you have the value, promotion aspect, which is understanding and promoting the value of an analytical system across the departments among all the stakeholders. And that's also a key function of the governance team. So, finally we have the inputs for data analytics application. You can see it's the architecture considerations, analytical capability requirements and strategy alternatives. So, we have the blueprint. And first step, of course, is to come up with a reference architecture or find a reference architecture if available. And this is one of the biggest challenges we faced. You know, we did a lot of research and we found that nowhere in the world any government had tried anything like this in terms of developing an analytic system. So, you had a lot of use cases where department level initiatives were done and we had some architectures for those. But whole of government level data analytics, big data analytics system is something which not many people. In fact, at the government level, nobody had even tried. So, this is one of the first I can safely say, and in fact I've got the confirmation also from some of my colleagues elsewhere, that this is one of the first of its kind being developed in the world. You know, if you find something like this, it's still under development or the scope is not as large. But this is one of the first. So, we had to put in a lot of effort in coming up with a reference architecture. And finally we did, and this is what it looks like. You'll see that it contains all aspects of an analytic system. Excuse me. So, you have the sources which are structured and unstructured, the bottom. And then you have the delivery which is multiple deliveries, alerts, portals, collaboration, mobile office apps, et cetera. And then on the right hand, on the left hand side, the dark blue boxes show you how some of the common aspects like metadata, security, life cycle management have been included in this model. We came up with a more detailed architecture pattern, which is this. This is very specific to government of Andhra Pradesh data analytics system. And you will see, I mean, it's not legible. Pardon me for that, but this is our logical architecture. Again, it's the same thing. And the key aspect here is the integration layer, which is the central enterprise service bus kind of a product, e-highway, which will help us in delivering and also extracting data from some of the sources. So I'm not going to go into the details of the architecture. Just a few bullets on the key features. So we have predictive, prescriptive, descriptive and causal analysis capability, multiple delivery mechanism, KPA analysis, which is basically very, very important for departments because that's the way they are measured. And then you have all the big data analytics system, textual sentiment, pattern matching, topic detection, log stream, et cetera. Textical and mathematical analysis, reports visualization. And one of the, you know, we have this concept of real-time streaming log analysis and sensor analytics, which can easily gel with smart city initiative. So tomorrow if you have a lot of sensors coming, sensor data coming from, let us say, smart city initiative, then this platform will be in a position to capture and analyze those data. Now the next important aspect is the team building. So we have conceived an idea. We have an idea of forming a central analytics team which will cater to the needs of all the departments. And we have some rough idea on how the team is going to look like. The composition include, I mean the team might include data analysts, engineers, data scientists, statisticians, software engineers, DBAs, and OEM specialists. And we wanted the system to be easily configurable and maintainable. And like, you know, the corporate systems which are technology heavy because you have a lot of support system there. And the core team, the technology team is huge and they can, you know, develop systems on their own. Whereas in government to minimize cost, it is better to have a configurable system which can be easily maintained by the existing support team. Maybe with a little bit of training, but mostly it should be manageable by the existing team members. So that actually brings in the COTS angle, right, so, and then the OEM specialists. And then, of course, collaboration with academicians. It's important to work with various universities, particularly when you talk about statistics. So we have advanced statistics institutes in the state which can contribute and also leverage the system for the R&D purposes. The challenges, of course, it's always there. But before that, the critical success factors, availability of data. So these are some of the questions which we need to ask ourselves as a team to ensure that we are on the right track and we have the right support system. Do we have all the data points to perform accurate data analysis? What is the quality of data? Do we have enough historical data to perform analysis? And coverage of data, data governance, training and talent development plan, of course, and then team composition. Just one quick bit. I know data might be available, but we may not have enough historical data. And then you use whatever is available with you and then you get something wrong out of it. That's a dangerous situation to be in. You want to have analytical capability, but you want it to produce accurate reports, accurate predictions. You don't want inaccuracies or huge inaccuracies to creep in which will defeat the purpose of having an analytical system in the first place. So one example, there's some study which was done some time ago which predicted that onion prices would peak in February 2016 and this was done a few months ago. Five or six months ago. And it predicted that onion prices in India would be at its peak in this month. But that has not happened so far. So imagine what would have happened if this information had gone to the government and the government procures or comes up with a strategy to procure onions in advance and then they find out that, wow, it's a waste of money. So you need to have good data and enough data to be able to do advanced analysis. So that's a very important factor. Key challenges and, of course, the strategy is to overcome those challenges around data, so the data availability. I think Pallab already mentioned about that. And how do you overcome the challenge of availability? So you have to be proactive, start collecting the data. At least, if not now, you will have some data one or two years down the line which can be useful. So you come up with, you can see the idea now. Don't wait till your idea is materialized but start collecting the data in some format. So that's one strategy. If you don't have enough historical data, well, if you don't have it, you don't have it. You can't do much about it. At least start building so that it will be useful in the years to come. But if you don't do it, then you will not have data even after two years. So that's very important. Quality of data, and this is very important specifically again from statistical analysis point of view, you need to have quality unbiased data and it's very difficult to control the quality of data in the open world. So the systems and the sensors and et cetera applications, they produce data and there is legacy data. And you have to accept them as they are because you can't go back in the time and then correct the fixed issues. So the first step would be to come up with some parameters which can measure the quality of data which you are interested in and then come up with some solutions to fix and improve the quality of data. So that's again a very elaborate and thoughtful process and data sharing, again, it might become a bottleneck particularly because now you have all the departments giving their pieces of data and it's going to be consolidated in one location and departments might not want to do it and you have seen that some of the technical support teams resist to share data. So that bottleneck has to be removed and again that comes from leadership as I mentioned earlier. So I have seven more minutes and I think I'm on track. So finally what's that we are looking at? So we have the system, so now we have the concept, we have the idea, we have the system, we know the challenges and we are already now on the road, we have started our journey towards building the analytic system. So what's that we have learned and what's in it for other governments and other states, right? So this analytics framework is unique as I mentioned earlier and it can become a framework in itself for other governments and states to follow. It is something which is conceived, created here in Andhra Pradesh but it can be useful for other governments in other states and nations too. But of course we have our learnings and we present our learning as guidelines, right? So understand your data, assess data as a capability. Now data as a capability is an important concept, we are bringing in this concept here because data itself has a lot of value in it and if you have good amount of data, quality data it itself becomes a capability which of course you can tap into. Governance framework is important. Prioritization of analytical capabilities is important. Don't go after all the fancy analytics that's available in the market. Just have it in mind. Build an architecture that can support all those fancy stuff but you prioritize based on the cost and the time, etc. and what exactly you need, where you have your data capability, etc. And then the strategies for acquisition, creation, profiling, cleansing and enrichment of data that's also an important thing. And bottlenecks for data exchange. Identify and eliminate them. And finally, and I think it's one of the most important considerations although it generally gets left out at the enterprise architecture level the implementation strategy which is whether to go on cloud or on premise or hybrid, etc. And again, you have to decide a good implementation strategy one that will work for you. Don't go by what corporates do or other people do. Based on the data you have, the size, the volume, etc. you come up with a proper implementation strategy. So these are the guidelines and that's it.