InetSoft Product Information: Visual Analytics Tools

Looking for visual analytics tools? InetSoft offers free and commercial options. The commercial app is a web-based BI platform that can mashup disparate data sources. The free one is also web-based and you can simply upload your data file or spreadsheet and begin visualizing it. Read articles below about InetSoft's BI and analytics solution.

Good IT Analytics Tool - With a good IT analytics tool, you can drill down. You can do all sorts of things, but the important thing is, #1, looking at it from the business perspective, in terms of really how you are delivering that service, because that’s why you exist in a sense. And then, what’s the history of doing it, and how you are going to do that in the future, and then, it becomes a vehicle for conversation between the business and IT and the data warehousing people to understand where you are going and to understand your options. We can do it this way or that way, and here’s the cost involvement. We can look at all of those details, and we can figure out what’s the best way to move forward. Think about how this would impact the relationship between IT and businesses. Getting back to changes the culture between them because in the beginning, that’s “Oh, my God, this is broken, we will fix it as soon as we can.” This tends to be not a great relationship with the business side because the business side tends to look at IT, “The system is down, or it’s going down for a day.” Whereas when you arm them with this type of information, they become proactive, and now, they are having a business conversation with the other side of the company saying, “Look, here is what we are doing, here is how we are serving you, which is great, but here is what we need to plan for this.” That’s a completely different culture and conversation...

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Healthcare Analytics in the Cloud - Now Jim, there are also sensitive data and privacy issues that impact healthcare analytics in the cloud. There are regulations and potential audits involved. How do you manage to protect the data even as you have to go through a lot of these cleansing and joining steps across different formats types and even sources of data? Jim: So there's actually lots of encryption involved at various places and along the way in a pipeline, and so we do keep the data in our archives in an encrypted fashion. When we move data along from one part of the pipeline to another we keep control of the environment by having really good controls on each of the stages. This is where Vertica actually helps us out quite a bit because we have the ability to nicely go in and assign roles to go there and put in some protections, and that was one of the things that we were looking for in a data store which is to have some ability to have controls over the data. So as it moves along the pipeline we keep these controls in place, and as things move along everywhere where we have an attack surface. We have to keep the data either protected by network access or by encryption, and the infrastructure that we build has to deal with that...

Healthcare Analytics and Data Science - So Jim what's been the common thread from cyber security to healthcare in terms of analytics and data science? What are the technical and other hurdles that are common between them that allowed you to make that leap? Jim: So the ability to detect various kinds of events in your data streams, and I'm going to use that term somewhat loosely, detecting events and data streams and then combining them together into patterns is something that happens in cyber security as well as fraud, waste and abuse. So whether you do that by looking at a series of bad claims or a series of services that shouldn't have occurred in sequence is a simple idea. We can't pull a tooth from a baby. That's a great example of a very simple idea, that sequence of events shouldn't be happening in healthcare, and you can see in a series of network activity the same kinds of events in principle would occur as a series of patterns and as you get good at detecting those kinds of patterns you can apply the same skill and learning about how to deal with the 3Vs of big data, the variety and the velocity and whatnot to really harness this in very high speed fashion. Abhishek: Right, now not that the cyber security issues aren't important, but are the stakes higher for healthcare, or is the market larger? What's at stake when you point your technology and expertise at the healthcare sector...

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Healthcare Data Science Platform - Okay, so we have a sense of your healthcare data science platform. The challenges, let's talk a little bit about how you now take this and apply it to the problem that your healthcare sector clients have. What are you doing in terms of being able to create recommendations to make analysis available, and the speed, how does that factor. So I guess I'm trying to get to what are the requirements that people have that exploit the technology that you put in place when it comes at being actionable. Jim: Sure, so let's start from the user's perspective, and then I'll work a little bit backwards more towards the technology. From a user perspective, people involved in, any an intelligence environment where you're trying to figure out whether some behavior was good or bad. They're just inundated with lots of little questions, and the longer that those little questions take to answer the harder their job is to get it done. So what we provide by leveraging the column store technology in Vertica is the ability to rapidly cycle through data and do measurements in an interactive fashion and the column stores provide very, very fast answers, and so we can let people filter data down and get new metric calculations on the fly, and this allows them to essentially self serve by getting answers to questions. So we allow the users to filter data, and as they filter their data they get new measurements, and new metrics coming back on a very, very rapid fashion. That allows them to answer questions very, very fast, and they don't lose the context...

Healthcare Machine Learning Analytics - We're here to learn how a leading healthcare analytics solution provider and OEM partner of InetSoft's delivers actionable investigative intelligence for healthcare fraud detection using machine learning analytics. As an analytics industry professional and a social media producer I speak with a lot of consumers of technology to uncover the business value from innovative uses of the latest IT systems and processes, and among the most exciting and interesting intersections of commerce and technology today is the way that machine learning analytics identifies and quantifies risk from massive and previously inaccessible data volumes. These machine learning case studies have expanded far and wide to include many vertical industries. Healthcare is the focus of today's discussion, with trillions of dollars involved per year in the United States alone, it is no less than imperative to bring improved efficiency, productivity, quality and security to the vast healthcare ecosystem of payers, providers, patients and consumers. We're going to learn today how this company uses advanced machine learning analytics platforms and methods to identify risk across complex healthcare activities. The payoff is delivery of faster, easier and more actionable findings to among other things advance governance and oversight to often dispersed and unwieldy and even hard to track transactions. To hear how this company addresses massive data volume challenges, to identify risk in healthcare networks and deliver answers instantly to generate more revenue, save wasted costs, and improve patient outcomes, we're pleased to be welcoming to our webcast their CTO. Welcome, Jim...

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How the Analytics Software Facilitates the Discovery Process - Could you talk about how the analytics software facilitates the discovery process? You say using visual analysis software, you can facilitate that process, and I understand that, for example, you will connect a data set to an application that you purchase, and you bring in certain columns or certain fields of course and then basically can you run a preview then apply different algorithms and kind of get different visualizations? Do you look for the spikes in the trend lines, or the red areas or the green areas of a heat map? Flaherty: I think the way things used to be done with someone with tremendous experience and education in this area is he would start by doing all that work himself. I think as the visual analysis tools have gotten better, we can either bring in different types of people with less experience or make that expert much more productive. And one of the ways that we can do that is to not start off with a whole bunch of manual data transformations that could take the next two months putting together. Let the analytics software automate the data transformations. A lot of the visualization tools now have the capability to do some level of automated data transformations to get that started. So you know with the typical example would be there are always dates in a file. You can't use a date to predict anything, but you can use a time interval. So a lot of the software will automatically do that conversion...

How Important Is It to Your Organization to Have Analytics? - How important is it to your organization to have each of the following analytics and metrics related analyst capabilities. The choices were search for existing data, analytics and metrics. One of the reasons your projects might fail is if the data is not getting prepped and delivered quickly or properly. Two-thirds of the organizations surveyed spend more time prepping the data than actually analyzing it. If your customers have not tackled the data preparation process, it's going to reflect poorly on your BI solution because people are going to just say, I don’t have the information I need, because they are spending too much time in the data preparation process. Does that also include the activities of exporting data from databases and bringing into the spreadsheet because that’s where the person is going to analyze it? Waiting for data is one of them, believe it or not. Preparing the data for analysis, reviewing it for consistency and then the others are what they are actually doing once they are analyzing. But add up those pieces, waiting for data, preparing it, reviewing it for quality and consistency, that’s 69% of the organizations do most of their time...

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How to Apply Predictive Analytics in Business - Today’s topic is How to Apply Predictive Analytics in Business. These economic times require changes in how businesses manage, in particular, the use of critical information to optimize decision making and other business processes. But getting this information is not simple. It can’t be found in historical reports but instead requires a forward looking analytics that can help gain insight on potential outcomes from existing operations. Then how do you build predictive analytics into your management efforts and what are the issues you need to be aware of this? This podcast is designed to provide prospective on you use predictive analytics actively and as an ingredient to your success. We’re talking today about analytics, but we’re talking in the context of an entire new environment of competitive and global business pressures. How do those two relate? Well the current economic environment is not a pretty one and second of all, the pace of business is increasing quite dramatically. So as we look at those two facts, one of the things that is pretty obvious is that if we’re going to make improvements in this type of economic environment we’re going to have to have information, and we’re going to have to make decisions on a very timely basis. Both of these realizations correlate together and make it a pretty complex environment for organizations...

How to Implement Business Analytics - We are here today to talk about how to implement business analytics. You know through the course of my career here at InetSoft I have been around many analytics projects and certainly have developed some of my own opinions as to why it matters but at the end of the day my opinion probably doesn’t matter that much to you. What I think matters more are the opinions and more importantly the experiences of your peers who are using analytics effectively, in some cases, to drive double digit growth in major performance metrics like profitability and cash flow. Hopefully, today the data that I show will give you some ideas as how you might build a stronger analytical environment within in your own organizations and navigate the waters of Big Data as the title suggests and produce some real results at the end of the day. So here I’ve put together a fairly straight forward agenda for the presentation today. The world of business analytics is not surprisingly somewhat in flux. I want to start by talking through a couple of the high level data points that help show the current state of analytics. We will really highlight the value of analytics from the data side to the front end delivery of insights. And finally, I want to close things up by talking through a couple of recommendations and takeaways that will hopefully help you inform your own journey or voyage of analytics as it were...

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How Machine Learning Is Changing Business Analytics - Thank you all for joining us today for a discussion about how machine learning is changing business analytics. We have four major points that we want to discuss today. The first one being the importance of data science and data scientist and bringing machine learning into organizations. The second one being we've all heard of the V's of big data. and we know that one is velocity, and we know that there's a lot of streaming data out there now. This is going to be a big part of organizational strategies moving forward. Point three, how an organization can keep creativity with machine learning. We have all of these different tools to choose from today, all of this different data, but we deal with regulation, we deal with documentation, we deal with productionizing code. How do we keep infusing creativity into the machine learning workflow within an organization? Then, we've also heard a lot about the citizen data scientist recently and just in general more and more people in organizations wanting to get involved with analytics and machine learning, so that's point four. Okay, so we're going to start our discussion here. Is any of this really new? Is machine learning new? Is data science new? To me this is resounding no. In fact, machine learning has been studied at least since the 1950s, maybe before. Data science you could say goes back to John Tukey's 1962 Future of Data Analysis Paper. There's a great recent paper by David Donoho out of Stanford that talks about 50 years of history of data science, and I urge you to read that. We have a link to that at the end. We're seeing machine learning in organizations now. This isn't coming out of the blue. This has a long history, and so we wanted to spend a little bit of time here. One good thing to do at first is of course to define machine learning, and that's really tricky. I think for better or for worse, in a certain sense machine learning has taken on sort of a pop culture, meaning it's just the rebranding of analytics or data mining...

How Much Data To Start Predictive Analytics - Yes. The next question I have, and this is an old chestnut that we're constantly asked when talking to organizations throughout all industries. When do you know you have enough data to start your predictive analytics and what type of data should you be looking at? Again, Natalie, would you like to comment on that? Natalie: Yes, so I think it's difficult to answer that in terms of when do you know you have enough data. There's never a time when you should limit the analysis that you do because of data. You can always do something with whatever data you have. There's always a starting point, and there's always something that you can do. I think that like for me personally data is obviously what drives us here and the use of it, and one of the first things I would always do with organizations is assess their level of data. What do you have? What could we do? How could we align with your strategic goals. Yeah, so there's no point with which you should limit yourself. In fact we always start with them with small amounts of data. Jessica: Tony any comments there...

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How Predictive Analytics Can Help Transform the Insurance Industry - Well, good morning everybody my name is Natalie Chan. I'm delighted to be here today to talk to you about how predictive analytics can help transform the insurance industry. My background is in mathematical modeling and statistics, I have over 15 years experience of working in areas where these skills are applied in a practical setting. I'm extremely passionate about using data to try to support evidence based decision making, and I've been working with organizations across a wide variety of sectors insurance, retail, banking and telcos to basically build and execute analytical strategies to ensure the success implementation of data driven and analytics. I'm here today to talk to you about how predictive analytics can improve your customer's experience, increasing customer satisfaction and reduce cost. You might wonder why I want to focus on the customer experience. Well most of the analytics employed in the insurance industry is focused on identifying or reducing fraud, estimating and managing risk, and on improving customer retention. However, reports from the insurance industry consistency highlight that the quality of customer experience remains the biggest factor driving customers to remain loyal or to switch to another insurance provider. We should focus on how to improve the quality of the customer experience rather than focusing solely on fraud...

InetSoft vs Analyzer Comparison - The InetSoft promise of easy, agile, and robust business intelligence is now backed up by a professional analysis. To create its comparison of InetSoft Style Intelligence and Analyzer, analyst firm G2 Crowd compiled reviews and ratings done by independent users of the two BI vendors, comparing the BI tools in the areas of reporting and building reports, self-service, advanced analytics, and the strength of the overall platform...

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InetSoft vs ClicData Comparison - So how does InetSoft's Style Intelligence weigh against ClicData? From the latest G2 crowd ratings, it is pretty impressive. InetSoft has received rave reviews from a majority of its users and beats ClicData in various categories across the board. Style Intelligence has been lauded for its robustness and described as a complete and powerful go-to tool for data analysis...

InetSoft's Style Intelligence vs Dundas BI -How does InetSoft's Style Intelligence compare to Dundas when the two business intelligence solutions are compared? The data from verified user reviews on analyst firm website G2 crowd rates InetSoft Style Intelligence with 4.5 /5 stars over that of Dundas BI which has 4.4 / 5 stars...> InetSoft's Style Intelligence vs Mode Analytics -A comparison between InetSoft's Style Intelligence and Mode Analytics was done based on peer reviews in 6 broad categories - Ratings, Reports, Self Service, Advanced Analytics, Building Reports and Platform. Each category was further divided under various parameters and measured in detail and the Style Intelligence was the clear winner, beating Mode Analytics on 30 out of 31 metrics...

InetSoft's Style Intelligence vs Geckoboard - The InetSoft promise of easy, agile, and robust business intelligence is now backed up by a professional analysis. To create its comparison of InetSoft Style Intelligence and Geckoboard, analyst firm G2 Crowd compiled reviews and ratings done by independent users of the two BI vendors, comparing the BI tools in the areas of reporting and building reports, self-service, advanced analytics, and the strength of the overall platform...

InetSoft's Style Intelligence vs SAP Lumira - Choosing the right solution for business intelligence and automated reporting can be daunting. InetSoft's Style Intelligence and SAP Lumira are two solutions which give robust visual analytics software for organizations to improve their operations...

InetSoft's Style Intelligence vs ThoughtSpot - Selecting Business Intelligence (BI) solutions for any organization is hard, risky, and inherently biased. It is made easy by the G2 Crowd review platform with its real-time, transparent and unbiased user reviews. This helps an organization to objectively assess what is best by leveraging the wisdom of the crowd, limiting the risk, and finding out what works. The reviews are validated by G2 Crowd thereby helping organizations make better buying decisions...

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InetSoft vs Izenda Comparison -InetSoft has been providing unrivaled business intelligence solutions since 1996. InetSoft's Style Intelligence is an agile business intelligence tool that users from all around the world recognize as one of the best visualization dashboard software tools on the market today. Recently, users on G2 Crowd roundly endorsed the superiority of Style Intelligence over competing BI tool Izenda. From the user level to the administrative, InetSoft's Style Intelligence outranked Izenda in a majority of categories...

InetSoft's Style Intelligence vs Oracle Cloud Comparison - How does InetSoft's Style Intelligence compare to Oracle Cloud when the two business intelligence solutions are compared? The data from verified user reviews on analyst firm website G2 crowd rates InetSoft Style Intelligence with 4/5 stars over that of Oracle Cloud BI which has 3.8 / 5 stars...

Innovations in Discovery and Analysis Tools - What are some innovations in discovery and analysis tools? There’s a range of new charts types. We have 15. I think the important point here is that there are charts that are more report-like: data sheets, counts, text filters, and basically rows and columns. We can create a good visual display in each chart type, but you also need to take some steps in advance. If it’s customers that you are analyzing, you need customer names. If it’s products, then product names because people need that to get context. We tend to balance this more standard type of chart with more visual charts, bar charts, and pie charts. We also have a set of richer charts you’ll see in a few minutes, heat maps, paretos, and time tables which are more in that analysis category. In fact when we can do a demonstration right now of a project that uses the time table. Let me just click this. I’m opening a web visualization project, and we’re looking at event logs from a factory. This is a manufacturing operations dashboard. It’s one day of event logs. There are 880,203 of them. They are listed out here. This chart groups them by command...

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Insurance Claims Analytic Views - Now with this insurance claims data, in the context of healthcare provider and physician profiles, there's a bunch of different analytic views that we're able to show you right off the bat. Not only can we provide you detailed analytics on diagnoses and procedures and financial breakouts, but what we can also do is bring you insight and intelligence related to those prescription drug claims that might be processed and incorporated into a physician's profile. Each of these data discovery tools gives you not only the ability to get a lot of great detail on an individual provider, but also gives you the opportunity to do some powerful searching, whether that might be a particular ICD code, whether that be by a CPT code, or maybe that's a drug class or an NDC code. Really as a result what we've seen is that being able to integrate all of this information into each of their profiles gives you the sense of understanding of what's actually taking place in market today. But also having the option to go ahead and understand how trends have been impacted over time by having access to that historical information is a big plus. With this data in context of all of the other healthcare industry intelligence that you track, now I'd like to go ahead and walk you through three different use cases in which we really have seen some of our customers and clients really gain some extreme value out of by having access to the commercial claims intelligence. The first use case that we're going to go ahead and explore is really around mapping influence and being able to understand how you can think about targeting physicians...

Insurance Coverage Analysis Dashboard - The Insurance Coverage Analysis Dashboard below is an example of InetSoft's interactive BI software. Targeted towards organizations of a many scopes, InetSoft's dashboard solution is ideal for users looking for simplicity, power, and performance. A pioneer since 1996, InetSoft prides itself on combining data mashup, dashboards, and reporting solutions to help businesses enhance business performance in a range of ways. The chart below clearly demonstrates the simple nature of the dashboard. Still, there's a rich level of information and data that users can absorb from the available information chart by using the dragging sliders to filter information for effective and efficient analysis. By being able to narrow the results of different statistics, users can get a quicker and more accurate glance at the data being used. Effective business practices only enhance performance and InetSoft's feature-packed solution does just that for organizations...

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Insurance Analytics Solution Case Study - This InetSoft client requested to remain confidential. The client sought to create an end-to-end insurance BI solution which would provide insurance companies with access to and analysis of real-time data, so that insurance companies could discover trends and insights across their entire customer base. The client wanted to build their solution from insurance industry standard data models (Acord & OMG) that include line-of-business details such as worker compensation. Incorporating these data models into the solution would allow insurance companies to do consolidated operational reporting and analytics...

Integrate, Report, and Analyze Data Any Way You Want - Well let’s create a globally consistent way to integrate, report and analyze data and then just open up lots of local franchises throughout the company in either the sales, service, marketing, HR, finance or some of the different lines of business units. And then by bringing it to the local market it’s going to be more responsive. It’s going to have more domain expertise, but there is still a sort of global consistency. So that might be more right for a more centralized self-service BI approach. Now I have seen other folks, and a lot of manufacturers come to mind who tend to be very decentralized, base their BI strategy upon lines of business units. They wanted to be even more decentralized, and they almost had what I would almost view as a bottoms-up approach where essentially the different departments get to do whatever they want. They can integrate, report and analyze data any way they want, and they had full autonomy and full control. And the job of the centralized team is really just to kind of watch what the decentralized teams are doing, and when they are doing something really useful, identify that and promote that, and disseminate that out to the other departments and say hey, this team over here is doing something really cool, let’s make this more widely available to other aspects of our enterprise...

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Interact More with the Data in Terms of Analytics - Interact more with the data in terms of analytics. As more and more users in the organization start thinking about building predictive models and trying to work with the data and look at different levels of the data, particularly, if they're looking at a dashboard and want to dive in and understand some of the metrics that they're taking to look at. In early stages of embedded data analytics, that really wasn't possible. That was something where you had to go back to IT and try to develop something that they can work with. Now we're starting to see more of that kind of ad hoc querying capability moving into the embedded reporting systems which is definitely a good trend. Think again about the nontechnical user being able to self-serve their reporting, how they can receive analytic insights. How is that done? How do they do it if they're not actually building predictive models or working with analytics themselves? How can they receive those insights so that they're in context? As I mentioned before, that they have to be understandable so they're actionable. If they're built off of a predictive model what does this mean for their particular area of interest, their responsibility, their business process? The ability to perform self-service analytics is through that query and search capabilities, so forth. That's obviously very broad idea of what it is all about...

Key Ingredient for a Successful Business Analyst - The second key ingredient for a successful business analyst is having good business skills. One of the things I hear an awful lot from the business analyst community out there is the challenge that they have to sit at the conference table with senior executive leaders and quantify strategies about the next fiscal year. Where are we going? What are we doing? What sort of insights do the senior BAs have? How can we guide the future? I think the profession still needs to grow, I think the acknowledgement still needs to be there, but I also think one of the missing pieces is what I would call ownership accountability. We need to get in synch with the phases our executive management team is using, return on investment and the creation of efficiencies. You should be able to talk about strategic plans and be able to articulate it. Be able to present that information which is relevant and important to a senior executive audience. I am not suggesting that we don’t do this already, but I’m suggesting that we need to really fine tune this ability. Things like critical thinking and problem solving, change management, things like integrity, things like setting goals and objectives, these are all the soft skills that are important...

Key Performance Indicators Analysis Tool - Are you looking for the best key performance indicators analysis tools? Since 1996 InetSoft has been making BI software that is easy to deploy and easy to use. Build self-service oriented dashboards and visual analyses quickly. InetSoft's data mashup engine solves the data access and transformation challenges that other tools cannot. View a demo and download one of our applications for free...

Liberating Analysts and Managing Output - Now, obviously on the BI side what we try to do is to get ahead of these spreadmarts by encapsulating as many of them as possible into various data marts and data warehouses. And this is a never ending process and one that we need to take on and never get defeated in pursuing, but it is kind of like playing whack a-mole because it seems as soon as we consolidate one spreadmart into a data mart or data warehouse, two or three pop up to take its place. Nonetheless through a very persistent and consistent creation of new subject areas within a warehouse and proper application of BI governance, we can get ahead of the spreadmart dilemma that afflicts many of us. But, since we are talking about analytics and business analysts in particular, we have to ask why do these analysts create these spreadmarts? So I have got here the true deepest confessions of a real honest to goodness business analyst. And she’ll remain anonymous for the time being. She said, “I have been one of those so called “spreadsheet jockeys,” and I have an affinity for Excel, but out of necessity. Often the BI team generates a report that gives me 90 percent of what I need...

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Machine Learning Analytics Company - As a machine learning analytics company, we tend to focus on automation, time savings, and something that we are going to talk about in more detail later, graphical interfaces that can bring people who know more about the business closer to the data. Proprietary solutions also tend to focus on the deployment of models. We were talking about one sort of workflow which when the data scientist develops the model and then hands it off to a programmer to deploy the model. Well, we see InetSoft and other proprietary solutions working towards a one click sort of deploy button, and what a time savings that can be for an organization. What we want to look for and things that we've done is how do we feed the creativity of data scientists, and we think that allowing for bi-directional integration with open source products is one way. I can be in InetSoft, and make calls out to Open Source. Then we're talking about things like using the open standard of a PMML. I think it's really silly, and I do see less and less of this, thank God, people debating is R better than Python better than InetSoft? These are not productive discussions, I don't think. I mean I think it's good to know which tool to use for what. I certainly think that's good. I teach a data mining class and where I expose this to my students. Know which tool saves you the most time at what point of the process. I think that's more important...

Machine Learning and Predictive Analytics Create Data to Analyze - So we talk about machine learning and analytics, predictive analytics, the platform itself actually becomes a data creation platform, and that also adds to the different varieties of data. So it actually ends up compounding the drive to work towards the variety of the platform itself. The opportunities that it creates for using the data that comes in will undoubtedly be unlocked. It will keep growing. The varieties are never going to go in other direction. It's all is going to be increasing, and that's a great opportunity. It means different data types, script data and non SQL data will continue to increase a little. Coming back to how you actually get that data coming the other direction with SQL becoming more and more important, but in the underlying data, the variety is only going to increase. Abhishek: Larry, any thoughts you may want to add in here...

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Machine Learning Utilized For Predictive Analytics - Now let's move on to machine learning which is a subset of artificial intelligence. It provides computers with the ability to learn without being explicitly programmed and utilize predictive analytics to forecast outcomes and also to assess the probability of future predictive events. Machine learning has the ability to identify risk and to identify opportunities for businesses by using cognitive computing techniques, and it supports much greater efficiency. It can understand and can respond to human sentiments and emotions as well. Machine learning has gotten beyond the capabilities of predictive analytics and beyond the capabilities of big data analytics. It also surpasses in some ways human capabilities by thinking independently and making its own evaluations and its own conclusions. If we look to the machine learning development, we see that it's accelerating very quickly, and we will discuss now the cause of this rapid advancement. One such cause is the recent explosion of big data. Data is everywhere, and it is expanding from a number of sources. You can think about the sources for text, for images, for digitized documents and for internet devices. Everything is connected right now...

Making a Dashboard Solution Plug into Predictive Analytics - Now let’s talk in terms of making a dashboard solution plug into predictive analytics. There is something called PMML. That’s industry standard language, it’s called Predictive Modeling Markup. And PMML is a way that you can exchange predictive models to run against standard databases. So a BI solution should support PMML. Analytic solutions like SAS and SPSS can output PMML structures, and you just need to have a way to import them. There are a couple of issues there but delivery formats that are appropriate for organizations, so dashboards, alerts, mobile capabilities, time limits, we talked about. There are lots of problems with spreadsheets. One of the things we see that distinguishes the innovative firms is they are delivering information in a much more timely fashion. You can see a huge difference between the innovative firms and the tactical ones. Does timely mean day prior information...

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Making Data Usable For Broad Analytics - Yeah, there is a question that one of the attendees asked, is Alteryx a data shaper, and the answer is absolutely. In addition to you mentioned Trifacta and Paxata. Those are two technologies that that were kind of born with Hadoop, and that's where you were seeing the largest variety of data. In that variety, you have to find a way of making the data really usable for broad analytics use cases. It depends on the shape of the data, whether it's a nested files or something else. And so you saw technology as Trifacta and Paxata that are really born around leveraging the Hadoop platform to do that data shaping and processing right on there. Now it has expanded to other technologies so it's not just dependent on Hadoop, but Alteryx has just gone the other way where they started with being able to shape data and prepare data off of a number of different sources whether they are now actually leveraging the processing of Spark or Hadoop to be able to do some of the transformations in memory...

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