How Can Analytics Improve Business? - TechWise Episode 2 Transcript
Takeaway: Host Eric Kavanagh discusses the use of analytics in business with data scientists and leaders in the industry.
Eric Kavanagh: Ladies and gentlemen, hello and welcome back once again to Episode 2 of TechWise. Yes, indeed, it’s time to get wise people! I’ve got a bunch of really smart people on the line today to help us in that endeavor. My name is Eric Kavanagh, of course. I will be your host, your moderator, for this lightning-round session. We have a lot of content here, folks. We have some big names in the business, who have been analysts in our space and four of the most interesting vendors. So we’re going to have a lot of good action on the call today. And of course, you out there in the audience play a significant role in asking questions.
So once again, the show is TechWise and the topic today is "How Can Analytics Improve Business?" Obviously, it’s a hot topic where it’s going to try to understand the different kinds of analytics you can do and how that can improve your operations because that’s what’s it all about at the end of the day.
So you can see myself up there at the top, that’s yours truly. Dr. Kirk Borne, a good friend from George Mason University. He is a data scientist with a tremendous amount of experience, very deep expertise in this space and data mining and big data and all that kind of fun stuff. And, of course, we have our very own Dr. Robin Bloor, Chief Analyst here at the Bloor Group. Who trained as an actuary many, many years ago. And he’s been really focused on this whole big data space and the analytic space quite intently for the last half decade. It’s been five years almost since we launched the Bloor Group per se. So time flies when you’re having fun.
We’re also going to hear from Will Gorman, Chief Architect of Pentaho; Steve Wilkes, CCO of WebAction; Frank Sanders, Technical Director at MarkLogic; and Hannah Smalltree, Director at Treasure Data. So like I’ve said, that’s a lot of content.
So how can analytics help your business? Well, how can't it help your business, quite frankly? There are all kinds of ways that analytics can be used to do things that improve your organization.
So streamline operations. That’s one that you don’t hear as much about as you do about things like marketing or raising revenue or even identifying opportunities. But streamlining your operations is this really, really powerful thing that you can do for your organization because you can identify places where you can either outsource something or you can add data to a particular process, for example. And that can streamline it by not requiring someone to pick up the phone to call or someone to email. There’s so many different ways that you can streamline your operations. And all of that really helps bring down your cost, right? That’s the key, it brings down the cost. But it also allows you to better serve your customers.
And if you think about how impatient people have become, and I see this every single day in terms of how people interact online, even with our shows, service providers that we use. The patience that people have, the attention span, gets shorter and shorter by the day. And what that means is that you need to, as an organization, respond in faster and faster periods of time to be able to satisfy your customers.
So, for example, if someone is on your webcast site or browsing around trying to find something, if they get frustrated and they leave, well, you might have just lost a customer. And depending upon how much you charge for your product or service, and maybe that’s a big deal. So the bottom line is that streamlining operations, I think, is one of the hottest spaces for applying analytics. And you do that by looking at the numbers, by crunching the data, by figuring out, for example, "Hey, why are we losing so many people on this page of our website?" "Why are we getting some of these phone calls right now?"
And the more real time you can respond to that kind of stuff, the better chances you’re going to have of getting on top of the situation and doing something about it before it’s too late. Because there is that window of time when someone gets upset about something, they’re dissatisfied or they’re trying to find something but they’re frustrated; you got a window of opportunity there to reach out to them, to grab them, to interact with that customer. And if you do so in the proper way with the right data or nice customer picture - understanding who is this customer, what is their profitability, what are their preferences - if you can really get a handle on that, you’re going to do a great job of holding on to your customers and getting new customers. And that’s what it’s all about.
So with that, I’m going to hand it over, actually, to Kirk Borne, one of our data scientists on the call today. And they’re pretty rare these days, folks. We’ve got two of them at least on the call so that’s big deal. With that, Kirk, I’m going to hand it over to you to talk about analytics and how it helps business. Go for it.
Dr. Kirk Borne: Well, thank you very much, Eric. Can you hear me?
Eric: That’s fine, go ahead.
Dr. Kirk: Okay, good. I just want to share if I talk for five minutes, and people are waving their hands at me. So the opening remarks, Eric, that you made really tie in to this topic I’m going to talk briefly about in the next few minutes which is this use of big data and analytics for data to decisions to support, there. The comment you made about operational streamlining, to me, it sort of falls into this concept of operational analytics in which you can see just about in every application over the world whether it’s a science application, a business, a cyber security and law enforcements and government, healthcare. Any number of places where we have a stream of data and we are making some kind of response or decision in reaction to events and alerts and behaviors that we see in that data stream.
And so one of the things that I’d like to talk about today is sort of how are you extracting the knowledge and insights from big data in order to get to that point where we can actually make decisions to take actions. And frequently we talk about this in an automation context. And today I want to blend the automation with the human analyst in the loop. So by this I mean while the business analyst plays an important role here in terms of betting, qualifying, validating specific actions or machine learning rules that we extract from the data. But if we get to a point where we’re pretty much convinced the business rules we’ve extracted and the mechanisms for alerting us are valid, then we can pretty much turn this over to an automated process. We actually do that operational streamlining that Eric was talking about.
So I have a little play on words here but I hope, if it works for you, I talked about the D2D challenge. And D2D, the not just data the decisions in all contexts, we’re looking at this at the sort of the bottom of this slide hopefully you can see it, making discoveries and increasing revenue dollars from our analytics pipelines.
So in this context, I actually have this role of marketer to myself here now that I work with and that is; the first thing you want to do is characterize your data, extract the features, extract the characteristics of your customers or whatever entity it is you’re tracking in your space. Maybe it’s a patient in a health analytics environment. Maybe it’s a Web user if you’re looking at a sort of a cyber security issue. But characterize and extract characteristics and then extract some context about that individual, about that entity. And then you gather those pieces that you just created and put them into some kind of a collection from which you can then apply machine learning algorithms.
The reason I say it this way is that, let’s just say, you have a surveillance camera at an airport. The video itself is an enormous, large volume and it’s also very unstructured. But you can extract from video surveillance, facial biometrics and identify individuals in the surveillance cameras. So for example in an airport, you can identify specific individuals, you can track them through the airport by cross identifying the same individual in multiple surveillance cameras. In so as that the extracted biometric features that you’re really mining and tracking is not the actual detailed video itself. But once you have those extractions then you can apply machine learning rules and analytics to make decisions on whether you need to take an action in a particular case or something happened incorrectly or something that you have an opportunity to make an offer. If you’re, for example, if you have a store in the airport and you see that customer coming your way and you know from other information about that customer, that maybe he got really interested in buying stuff in the duty-free shop or something like that, make that offer.
So what kind of things would I mean by characterization and potentialization? By characterization I mean, again, extracting the features and characteristics in the data. And this can either be machine generated, then its algorithms can actually extract, for example, biometric signatures from video or sentiment analysis. You can extract customer’s sentiment through online reviews or social media. Some of these things may be human generated, so that the human being, the business analyst, can extract additional features which I’ll show in the next slide.
Some of these can be crowdsourced. And by crowdsourced, there’s a lot of different ways you can think about that. But very simply, for example, your users come to your website and they put in search words, keywords, and they end up on a certain page and actually spend time there on that page. That they actually, at least, understand that they’re either viewing, browsing, clicking on things in that page. What that says to you is that the keyword that they typed in at the very beginning is the descriptor of that page because it landed the customer on the page that they were anticipating. And so you can add that additional piece of information, that is customers who use this keyword actually identified this webpage within our information architecture as the place where that content matching that keyword.
And so the crowdsourcing is another aspect that sometimes people forget, that sort of tracking your customers’ breadcrumbs, so to speak; how do they move through their space, whether it’s an online property or a real property. And then use that sort of path they, that the customer takes as additional information about the things that we’re looking at.
So I want to say human-generated things, or machine generated, ended up having a context in sort of annotating or tagging specific data granules or entities. Whether those entities are patients in a hospital setting, customers or whatever. And so there are different types of tagging and annotations. Some of that is about the data itself. That is one of the things, what type of information, what kind of information, what are the features, the shapes, maybe the textures and patterns, anomaly, non-anomaly behaviors. And then extract some semantics, that is, how does this relate to other things that I know, or this customer is an electronics customer. This customer is a clothing customer. Or this customer likes to buy music.
So identifying some semantics about that, these customers who like music tend to like entertainment. Maybe we could offer them some other entertainment property. So understanding the semantics and also some provenance, which is basically saying: where did this come from, who provided this assertion, what time, what date, under what circumstance?
So once you have all those annotations and characterizations, add to that then the next step, which is the context, sort of the who, what, when, where and why of it. Who is the user? What was the channel they came in on? What was the source of the information? What kind of reuses have we seen in this particular piece of information or data product? And what is, it’s sort of, value in the business process? And then collect those things and manage them, and actually help create database, if you want to think of it that way. Make them searchable, reusable, by other business analysts or by an automated process that will, the next time I see these sets of features, the system can take this automatic action. And so we get to that sort of operational analytic efficiency, but the more we collect useful, comprehensive information, and then curate it for these use cases.
We get down to business. We do the data analytics. We look for interesting patterns, surprises, novelty outliers, anomalies. We look for the new classes and segments in the population. We look for associations and correlations and links among the various entities. And then we use all that to drive our discovery, decision and dollar-making process.
So there again, here we got the last data slide I have is just basically summarizing, keeping the business analyst in the loop, again, you’re not extracting that human and it’s all important to keep that human in there.
So these features, they’re all provided by machines or human analysts or even crowdsourcing. We apply that combination of things to improve our training sets for our models and end up with more accurate predictive models, fewer false positives and negatives, more efficient behavior, more efficient interventions with our customers or whoever.
So, at the end of the day, we’re really just combining machine learning and big data with this power of human cognition, which is where that sort of tagging annotation piece comes in. And that can lead through visualization and visual analytics-type tools or immersive data environments or crowdsourcing. And, at the end of the day, what this is really doing is generating our discovery, insights and D2D. And those are my comments, so thank you for listening.
Eric: Hey that sounds great and let me go ahead and hand the keys over to Dr. Robin Bloor to give his perspective as well. Yeah, I like to hear you comment about that streamlining of operations concept and you’re talking about operational analytics. I think that is a big area that needs to be explored quite thoroughly. And I guess, real quick before Robin, I’ll bring you back in, Kirk. It does require that you have some pretty significant collaboration among various players in the company, right? You have to talk to operations people; you’ve got to get your technical people. Sometimes you get your marketing people or your Web interface people. These are typically different groups. Do you have any best practices or suggestions on how to kind of get everyone put their skin in the game?
Dr. Kirk: Well, I think this comes with the business culture of collaboration. In fact, I talk about the three C’s of sort of the analytics culture. One is creativity; another is curiosity and the third is collaboration. So you want creative, serious people, but you also have to get these people to collaborate. And it really starts from the top, that sort of building that culture with people who should openly share and work together towards the common goals of the business.
Eric: It all makes sense. And you really do have to get good leadership at the top to make that happen. So let’s go ahead and hand it up to Dr. Bloor. Robin, the floor is yours.
Dr. Robin Bloor: Okay. Thank you for that intro, Eric. Okay, the way that these pan out, these shows, because we have two analysts; I get to see the analyst’s presentation that the other guys don’t. I knew what Kirk was going to say and I just go a completely different angle so that we don’t go too much overlap.
So what I’m actually talking about or intending to talk about here is the role of the data analyst versus the role of the business analyst. And the way that I’m characterizing it, well, tongue-in-cheek to a certain extent, is kind of Jekyll and Hyde thing. The difference being specifically the data scientists, in theory at least, know what they’re doing. While the business analysts are not so, okay with the way the mathematics works, what can be trusted and what cannot be trusted.
So let’s just get down to the reason that we’re doing this, the reason that data analysis has suddenly become a big deal aside from the fact that we can actually analyze very large amounts of data and pull in data from outside the organization; is it pays. The way that I look at this - and I think this is only just becoming a case but I definitely think it is a case - data analysis is really business R&D. What you’re actually doing in one way or another with data analysis is you’re looking at a business process at one sort or whether that’s the interaction with a customer, whether that’s with the way that your retail operation, the way that you deploy your stores. It doesn’t really matter what the issue is. You’re looking at a given business process and you’re trying to improve it.
The outcome of successful research and development is a change process. And you can think of manufacturing, if you want, as a usual example of this. Because in manufacturing, people gather information about everything to try and improve the manufacturing process. But I think what’s happened or what is happening at big data is all of this is now being applied to all businesses of any kind in any way that anyone can think of. So pretty much any business process is up for examination if you can gather data about it.
So that’s one thing. If you like, that’s going at the question of data analysis. What can data analytics do for the business? Well, it can change the business completely.
This particular diagram which I’m not going to describe in any depth, but this is a diagram that we came up with as the culmination of the research project that we did for the first six months of this year. This is a way of representing a big data architecture. And a number of things that are worth pointing out before I go on to the next slide. There are two data flows here. One is a real-time data stream, which goes along the top of the diagram. The other is a slower data stream that goes along the bottom of the diagram.
Look at the bottom of the diagram. We’ve got Hadoop as a data reservoir. We’ve got various databases. We’ve got a whole data there with a whole bunch of activity happening on it, most of which is analytical activity.
The point I’m making here and the only point I really want to make here is that the technology is hard. It’s not simple. It’s not easy. It’s not something that anyone who’s new to the game can actually just put together. This is fairly complex. And if you’re going to instrument a business for doing dependable analytics across all these processes, then it’s not something that’s going to happen specifically quickly. It’s going to require a lot of technology to be added to the mix.
Okay. The question what is a data scientist, I could claim to be a data scientist because I was trained actually in statistics before I was ever trained in computing. And I did an actuarial job for a period of time so I know the way that a business organizes, statistical analysis, also in order to run itself. This is not a trivial thing. And there’s an awful lot of best practice involved both on the human side and on the technology side.
So in asking the question "what is a data scientist," I’ve put the Frankenstein pic simply because it’s a combination of things that have to be knitted together. There is project management involved. There is deep understanding in statistics. There is domain business expertise, which is more of a problem of a business analyst than the data scientist, necessarily. There’s experience or the need to understand data architecture and to be able to build data architect and there’s software engineering involved. In other words, it’s probably a team. It’s probably not an individual. And that means that it’s probably a department that needs to be organized and its organization needs to be thought about fairly extensively.
Throwing into the mix the fact of machine learning. We couldn’t do, I mean, machine learning is not new in the sense that most of the statistical techniques that are used in machine learning have been known about for decades. There are a few new things, I mean neural networks are relatively new, I think they’re only about 20 years old, so some of it is relatively new. But the problem with machine learning was that we really didn’t actually have the computer power to do it. And what’s happened, apart from anything else, is that the computer power is now in place. And that means an awful lot of what we, say, data scientists have done before in terms of modeling situations, sampling data and then marshalling that in order to produce a deeper analysis of the data. Actually, we can just throw computer power at it in some cases. Just choose machine-learning algorithms, throw it at the data and see what comes out. And that is something that a business analyst can do, right? But the business analyst needs to understand what they’re doing. I mean, I think that’s the issue really, more than anything else.
Well, this is just to know more about business from its data than by any other means. Einstein didn’t say that, I said that. I just put his picture up for credibility. But the situation is actually starting to develop is one where the technology, if properly used, and the mathematics, if properly used, will be able to run a business as any individual. We’ve watched this with IBM. First of all, it could beat the best guys at chess, and then it could beat the best guys at Jeopardy; but eventually we’re going to be able to beat the best guys at running a company. The statistics will eventually triumph. And it’s hard to see how that won’t happen, it just hasn’t happened yet.
So what I’m saying, and this is kind of a complete message of my presentation, is these two issues of the business. The first one is, can you get the technology right? Can you make the technology work for the team that is actually going to be able to preside over it and get benefits for the business? And then secondly, can you get the people right? And both of these are issues. And they’re issues that aren’t, to this point in time, they say that is, resolved.
Okay Eric, I’ll pass it back to you. Or I should perhaps pass it to Will.
Eric: Actually, yeah. Thank you, Will Gorman. Yeah, there you go, Will. So let’s see. Let me give you the key to the WebEx. So what you got going on? Pentaho, obviously, you guys have been around for a while and open-source BI’s kind of where you started. But you got a lot more than you used to have, so let’s see what you got these days for analytics.
Will Gorman: Absolutely. Hi, everybody! My name is Will Gorman. I’m the Chief Architect at Pentaho. For those of you who haven’t heard of us, I just mentioned Pentaho is a big data integration and analytics company. We’ve been in the business for ten years. Our products have evolved side by side with the big data community, starting as an open-source platform for data integration and analytics, innovating with technology like Hadoop and NoSQL even before commercial entities formed around those tech. And now we have over 1500 commercial customers and many more production appointments as a result of our innovation around open source.
Our architecture is highly embeddable and extensible, purpose-built to be flexible as big data technology in particularly are evolving at a very rapid pace. Pentaho offers three main product areas is that work together to address big data analytics use cases.
The first product at the extent of our architecture is Pentaho Data Integration which is geared towards data technologist and data engineers. This product offers a visual, drag-and-drop experience for defining data pipelines and processes for orchestrating data within big data environments and traditional environments as well. This product is a lightweight, metadatabase, data-integration platform built on Java and can be deployed as a process within MapReduce or YARN or Storm and many other batch and real-time platforms.
Our second product area is around visual analytics. With this technology, organizations and OEMs can offer a rich drag-and-drop visualization and analytics experience for business analysts and business users by modern browsers and tablets, allowing the ad hoc creation of reports and dashboards. As well as the presentation of pixel-perfect dashboarding and reports.
Our third product area focuses on predictive analytics targeted for data scientists, machine-learning algorithms. As mentioned before, like neural networks and such, can be incorporated into a data transformation environment, allowing data scientists to go from modeling to production environment, giving access to predict, and that may impact business processes very immediately, very quickly.
All these products are tightly integrated into a single agile experience and give our enterprise customers the flexibility they need to address their business problems. We’re seeing a quickly evolving landscape of big data in traditional technologies. All we hear from some companies in the big data space that the EDW is near an end. In fact, what we see in our enterprise customers is they need to introduce big data into existing business and IT processes and not replace those processes.
This simple diagram shows the point in architecture that we see often, which is a type of EDW-deployment architecture with data integration and BI use cases. Now this diagram is similar to Robin’s slide on big data architecture, it incorporates real-time and historical data. As new data sources and real-time requirements emerge, we see big data as an additional part of the overall IT architecture. These new data sources include machine-generated data, unstructured data, the standard volume and velocity and variety of requirements that we hear about in big data; they don’t fit into traditional EDW processes. Pentaho works closely with Hadoop and NoSQL to simplify the ingestion, data processing and visualization of this data as well as blending this data with traditional sources to give customers a full view into their data environment. We do this in a governed manner so IT can offer a full analytics solution to their line of business.
In closing, I would like to highlight our philosophy around big data analytics and integration; we believe that these technologies are better together working with a single unified architecture, enabling a number of use cases that would otherwise not be possible. Our customers’ data environments are more than just big data, Hadoop and NoSQL. Any data is fair game. And big data sources need to be available and work together to impact business value.
Finally, we believe that in order to solve these business problems in enterprises very effectively through data, IT and lines of business need to work together on a governed, blended approach to big data analytics. Well thank you very much for giving us the time to talk, Eric.
Eric: You bet. No, that’s good stuff. I want to get back to that side of your architecture as we get to the Q&As. So let’s move through the rest of the presentation and thank you very much for that. You guys definitely have been moving quickly the last couple of years, I have to say that for sure.
So Steve, let me go ahead and hand it over to you. And just click there on the down arrow and go for it. So Steve, I’m giving you the keys. Steve Wilkes, just click on that farthest down arrow there on your keyboard.
Steve Wilkes: There we go.
Eric: There you go.
Steve: That’s a great intro you’ve given me, though.
Steve: So I’m Steve Wilkes. I’m the CCO at WebAction. We’ve only been around for the last couple of years and we’ve definitely been moving fast as well, since then. WebAction is a real-time big data analytics platform. Eric mentioned earlier, kind of, how important real time is and how real time your applications are getting. Our platform is designed to build real-time apps. And to enable the next generation of data-driven apps that can be built incrementally [Inaudible 00:29:36] on and to allow people to build dashboards from the data generated from those apps, but focusing on real time.
Our platform is actually a full end-to-end platform, doing everything from data acquisition, data processing, all the way through to data visualization. And enables multiple different types of people within our enterprise to work together to create true real-time apps, giving them insight into things happening in their enterprise as they happened.
And this is a little bit different from what most people have been seeing in big data, so that the traditional approach - well, traditional the last couple of years - approach with big data has been to capture it from a whole bunch of different sources and then pile it up into a big reservoir or lake or whatever you want to call it. And then process it when you need to run a query on it; to run large-scale historical analysis or even just ad hoc querying of large amounts of data. Now that works for certain use cases. But if you want to be proactive in your enterprise, if you want to actually be told what’s going on rather than finding out when something went wrong kind of towards the end of the day or the end of the week, then you really need to move to real time.
And that switches things around a little. It moves the processing to the middle. So effectively you’re taking those streams of large amounts of data that is being generated continually within the enterprise and you’re processing it as you get it. And because you’re processing it as you get it, you don’t have to store everything. You can just store the important information or the things that you need to remember that actually happened. So if you’re tracking the GPS location of vehicles moving down the road, you don’t really care where they are every second, you don’t need to store where they are every second. You just need to care about, have they left this place? Have they arrived at this place? Have they drove, or not, the freeway?
So it’s really important to consider that as more and more data gets generated, then the three Vs. Velocity basically determines how much data generates every day. The more data that’s generated the more you have to store. And the more you have to store, the longer it takes to process. But if you can process it as you get it, then you get a really big benefit and you can react to that. You can be told that things are happening rather than having to search for them later.
So our platform is designed to be highly scalable. It has three major pieces - the acquisition piece, the processing piece and then the delivery visualization pieces of the platform. On the acquisition side, we’re not just looking at machine-generated log data like Web logs or applications that has all the other logs that are being generated. We can also go in and do change data capture from databases. So that basically enables us to, we’ve seen the ETL side that Will presented and traditional ETL you have to run queries against the databases. We can be told when things happen in the database. We change it and we capture it and receive those events. And then there’s obviously the social feeds and live device data that’s being pumped to you over TCP or ACDP sockets.
There’s tons of different ways of getting data. And talking of volume and velocity, we’re seeing volumes that are billions of events per day, right? So it’s large, large amounts of data that is coming in and needs to be processed.
That is processed by a cluster of our servers. The servers all have the same architecture and are all capable of doing the same things. But you can configure them to, sort of, do different things. And within the servers we have a high-speed query processing layer that enables you to do some real-time analytics on the data, to do enrichments of the data, to do event correlation, to track things happening within time windows, to do predictive analytics based on patterns that are being seen in the data. And that data can then be stored in a variety places - the traditional RDBMS, enterprise data warehouse, Hadoop, big data infrastructure.
And the same live data can also be used to power real-time data-driven apps. Those apps can have a real-time view of what’s going on and people can also be alerted when important things happen. So rather than having to go in at the end of the day and find out that something bad really happened earlier on the day, you could be alerted about it the second we spot it and it goes straight to the page draw down to find out what’s going on.
So it changes the paradigm completely from having to analyze data after the fact to being told when interesting things are happening. And our platform can then be used to build data-driven applications. And this is really where we’re focusing, is building out these applications. For customers, with customers, with a variety of different partners to show true value in real-time data analysis. So that allows people that, or companies that do site applications, for example, to be able track customer usage over time and ensure that the quality of service is being met, to spot real-time fraud or money laundering, to spot multiple logins or hack attempts and those [Inaudible 00:36:11] kind of security events, to manage things like set-top boxes or other devices, ATM machines to monitor them in real time for faults, failures that have happened, could happen, will happen in the future based on predictive analysis. And that goes back to the point of streamlining operations that Eric mentioned earlier, to be able to spot when something’s going to happen and organize your business to fix those things rather than having to call someone out to actually do something after the fact, which is a lot more expensive.
Consumer analytics is another piece to be able to know when a customer is doing something while they’re still there in your store. Data sent to management to be able to in real time monitor resource usage and change where things are running and to be able to know about when things are going to fail in a much more timely fashion.
So that’s our products in a nutshell and I’m sure we’ll come back to some of these things in the Q&A session. Thank you.
Eric: Yes, indeed. Great job. Okay good. And now next stop in our lightning round, we’ve got Frank Sanders calling in from MarkLogic. I’ve known about these guys for a number of years, a very, very interesting database technology. So Frank, I’m turning it over to you. Just click anywhere in that. Use the down arrow on your keyboard and you’re off to the races. There you go.
Frank Sanders: Thank you very much, Eric. So as Eric mentioned, I’m with a company called MarkLogic. And what MarkLogic does is we provide an enterprise NoSQL database. And perhaps, the most important capability that we bring to the table with regards to that is the ability to actually bring all of these disparate sources of information together in order to analyze, search and utilize that information in a system similar to what you’re used to with traditional relational systems, right?
And some of the key features that we bring to the table in that regard are all of the enterprise features that you’d expect from a traditional database management system, your security, your HA, your DR, your backup are in store, your asset transactions. As well as the design that allows you to scale out either on the cloud or in the commodity hardware so that you can handle the volume and the velocity of the information that you’re going to have to handle in order to build and analyze this sort of information.
And perhaps, the most important capability is that fact that we’re scheme agnostic. What that means, practically, is that you don’t have to decide what your data is going to look like when you start building your applications or when you start pulling those informations together. But over time, you can incorporate new data sources, pull additional information in and then use leverage and query and analyze that information just as you would with anything that was there from the time that you started the design. Okay?
So how do we do that? How do we actually enable you to load different sorts of information, whether it be text, RDF triples, geospatial data, temporal data, structured data and values, or binaries. And the answer is that we’ve actually built our server from the ground up to incorporate search technology which allows you to put information in and that information self describes and it allows you to query, retrieve and search that information regardless of its source or format.
And what that means practically is that - and why this is important when you’re doing analysis - is that analytics and information is most important ones when it’s properly contextualized and targeted, right? So a very important key part of any sort of analytics is search, and the key part is search analytics. You can't really have one without the other and successfully achieve what you set out to achieve. Right?
And I’m going to talk briefly about three and a half different use cases of customers that we have at production that are using MarkLogic to power this sort of analytics. Okay. So the first such customer is Fairfax County. And Fairfax County has actually built two separate applications. One is based around permitting and property management. And the other, which is probably a bit more interesting, is the Fairfax County police events application. What the police events application actually does is it pulls information together like police reports, citizen reports and complaints, Tweets, other information they have such as sex offenders and whatever other information that they have access to from other agencies and sources. Then they allow them to visualize that and present this to the citizens so they can do searches and look at various crime activity, police activity, all through one unified geospatial index, right? So you can ask questions like, "what is the crime rate within five miles" or "what crimes occurred within five miles of my location?" Okay.
Another user that we’ve got, another customer that we have is OECD. Why OECD is important to this conversation is because in addition to everything that we’ve enabled for Fairfax County in terms of pulling together information, right; all the information that you would get from all various countries that are members of the OECD that they report on from an economic perspective. We actually laid a target drill into that, right. So you can see on the left-hand side we’re taking the view of Denmark specifically and you can kind of see a flower petal above it that rates it on different axes. Right? And that’s all well and good. But what the OECD has done is they’ve gone a step further.
In addition to these beautiful visualizations and pulling all these information together, they’re actually allowing you in real time to create your own better life index, right, which you can see on the right-hand side. So what you have there is you have a set of sliders that actually allow you to do things like rank how important housing is to you or income, jobs, community, education, environment, civic engagement, health, life satisfaction, safety and your work/life balance. And dynamically based on how you are actually inputting that information and weighting those things, MarkLogic’s using its real-time indexing capability and query capability to actually then change how each and every one of these countries is ranked to give you an idea of how well your country or your lifestyle maps through a given country. Okay?
And the final example that I’m going to share is MarkMail. And what MarkMail really tries to demonstrate is that we can provide these capabilities and you can do the sort of analysis not only on structured information or information that’s coming in that’s numerical but actually on more loosely structured, unstructured information, right? Things like emails. And what we’ve seen here is we’re actually pulling information like geolocation, sender, company, stacks and concepts like Hadoop being mentioned within the context of an email and then visualizing it on the map as well as looking at who those individuals and what list across that, a sent and a date. This where you’re looking at things that are traditionally not structured, that may be loosely structured, but are still able to derive some structured analysis from that information without having to go to a great length to actually try and structure it or process it at a time. And that’s it.
Eric: Hey, okay good. And we got one more. We’ve got Hannah Smalltree from Treasure Data, a very interesting company. And this is a lot of great content, folks. Thank you so much for all of you for bringing such good slides and such good detail. So Hannah, I just gave the keys to you, click anywhere and use the down arrow on your keyboard. You got it. Take it away.
Hannah Smalltree: Thank you so much, Eric. This is Hannah Smalltree from Treasure Data. I’m a director with Treasure Data but I have a past as a tech journalist, which means that I appreciate two things. First of all, these can be long to sit through a lot of different descriptions of technology, and it can all sound like it runs together so I really want to focus on our differentiator. And the real-world applications are really important so I appreciate that all of my peers have been great about providing those.
Treasure Data is a new kind of big data service. We’re delivered entirely on the cloud in a software as a service or managed-service model. So to Dr. Bloor’s point earlier, this technology can be really hard and it can be very time consuming to get up and running. With Treasure Data, you can get all of these kinds of capabilities that you might get in a Hadoop environment or a complicated on-premise environment in the cloud very quickly, which is really helpful for these new big data initiatives.
Now we talk about our service in a few different phases. We offer some very unique collection capabilities for collecting streaming data so particularly event data, other kinds of real-time data. We’ll talk a little bit more about those data types. That is a big differentiator for our service. As you get into big data or if you are already in it then you know that collecting this data is not trivial. When you think about a car with 100 sensors sending data every minute, even those 100 sensors sending data every ten minutes, that adds up really quickly as you start to multiply the amount of products that you have out there with sensors and it quickly becomes very difficult to manage. So we are talking with customers who have millions, we have customers who have billions of rows of data a day that they’re sending us. And they’re doing that as an alternative to try and to manage that themselves in a complicated Amazon infrastructure or even try to bring it into their own environment.
We have our own cloud storage environment. We manage it. We monitor it. We have a team of people that’s doing all that tuning for you. And so the data flows in, it goes into our managed storage environment.
Then we have embedded query engines so that your analyst can go in and run queries and do some initial data discovery and exploration against the data. We have a couple of different query engines for it actually now. You can use SQL syntax, which your analysts probably know and love, to do some basic data discovery, to do some more complex analytics that are user-defined functions or even to do things as simple as aggregate that data and make it smaller so that you can bring it into your existing data warehouse environment.
You can also connect your existing BI tools, your Tableau, is a big partner of ours; but really most BIs, visualization or analytics tools can connect via our industry standard JDBC and ODBC drivers. So it gives you this complete set of big data capabilities. You’re allowed to export your queries results or data sets anytime for free, so you can easily integrate that data. Treat this as a data refinery. I like to think of it more as a refinery than a lake because you can actually do stuff with it. You can go through, find the valuable information and then bring it into your enterprise processes.
The next slide, we talk about the three Vs of big data - some people say four or five. Our customers tend to struggle with the volume and velocity of the data coming at them. And so to get specific about the data types - Clickstream, Web access logs, mobile data is a big area for us, mobile application logs, application logs from custom Web apps or other applications, event logs. And increasingly, we have a lot of customers dealing with sensor data, so from wearable devices, from products, from automotive, and other types of machine data. So when I say big data, that’s the type of big data that I’m talking about.
Now, a few use cases in perspective for you - we work with a retailer, a large retailer. They are very well known in Asia. They’re expanding here in the US. You’ll start to see stores; they’re often called Asian IKEA, so, simple design. They have a loyalty app and a website. And in fact, using Treasure Data, they were able to deploy that loyalty app very quickly. Our customers get up and running within days or weeks because of our software and our service architecture and because we have all of the people doing all of that hard work behind the scenes to give you all of those capabilities as a service.
So they use our service for mobile application analytics looking at the behavior, what people are clicking on in their mobile loyalty application. They look at the website clicks and they combine that with our e-commerce and POS data to design more efficient promotions. They actually wanted to drive people into stores because they found that people, when they go into stores spend more money and I’m like that; to pick up things, you spend more money.
Another use case that we’re seeing in digital video games, incredible agility. They want to see exactly what is happening in their game, and make changes to that game even within hours of its release. So for them, that real-time view is incredibly important. We just released a game but we noticed in the first hour that everyone is dropping off at Level 2; how are we going to change that? They might change that within the same day. So real time is very important. They’re sending us billions of event logs per day. But that could be any kind of mobile application where you want some kind of real-time view into how somebody’s using that.
And finally, a big area for us is our product behavior and sensor analytics. So with sensor data that’s in cars, that’s in other kinds of machines, utilities, that’s another area for us, in wearable devices. We have research and development teams that want to quickly know what the impact of a change to a product is or people interested in the behavior of how people are interacting with the product. And we have a lot more use cases which, of course, we’re happy to share with you.
And then finally, just show you how this can fit into your environment, we offer again the capability to collect that data. We have very unique collection technology. So again, if real-time collection is something that you’re struggling with or you anticipate struggling with, please come look at the Treasure Data service. We have really made capabilities for collecting streaming data. You can also bulk load your data, store it, analyze it with our embedded query engines and then, as I mentioned, you can export it right to your data warehouse. I think Will mentioned the need to introduce big data into your existing processes. So not go around or create a new silo, but how do you make that data smaller and then move it into your data warehouse and you can connect to your BI, visualization and advanced analytics tools.
But perhaps, the key points I want to leave you with are that we are managed service, that’s software as a service; it’s very cost effective. A monthly subscription service starting at a few thousand dollars a month and we’ll get you up and running in a matter of days or weeks. So compare that with the cost of months and months of building your own infrastructure and hiring those people and finding it and spending all that time on infrastructure. If you’re experimenting or if you need something yesterday, you can get up and running really quickly with Treasure Data.
And I’m just pointing you to our website and to our starter service. If you’re a hands-on person who likes to play, please check out our starter service. You can get on, no credit card required, just name and email, and you can play with our sample data, load up your own data and really get a sense of what we’re talking about. So thanks so much. Also, check our website. We were named the Gartner Cool Vendor in Big Data this year, very proud of that. And you can also get a copy of that report for free on our website as well as many other analyst white papers. So thanks so much.
Eric: Okay, thank you very much. We’ve got some time for questions here, folks. We’ll go a little bit long too because we’ve got a bunch of folks still on the line here. And I know I’ve got some questions myself, so let me go ahead and take back control and then I’m going to ask a couple of questions. Robin and Kirk, feel free to dive in as you see fit.
So let me go ahead and jump right to one of these first slides that I checked out from Pentaho. So here, I love this evolving big data architecture, can you kind of talk about how it is that this kind of fits together at a company? Because obviously, you go into some fairly large organization, even a mid-size company, and you’re going to have some people who already have some of this stuff; how do you piece this all together? Like what does the application look like that helps you stitch all this stuff together and then what does the interface look like?
Will: Great question. The interfaces are a variety depending on the personas involved. But as an example, we like to tell the story of - one of the panelists mentioned the data refinery use case - we see that a lot in customers.
One of our customer examples that we talk about is Paytronix, where they have that traditional EDW data mart environment. They are also introducing Hadoop, Cloudera in particular, and with various user experiences in that. So first there’s an engineering experience, so how do you wire all these things up together? How do you create the glue between the Hadoop environment and EDW?
And then you have the business user experience which we talked about, a number of BI tools out there, right? Pentaho has a more embeddable OEM BI tool but there are great ones out there like Tableau and Excel, for instance, where folks want to explore the data. But usually, we want to make sure that the data is governed, right? One of the questions in the discussions, what about single-version experience, how do you manage that, and without the technology like Pentaho data integration to blend that data together not on the glass but in the IT environments. So it really protects and governs the data and allows for a single experience for the business analyst and business users.
Eric: Okay, good. That’s a good answer to a difficult question, quite frankly. And let me just ask the question to each of the presenters and then maybe Robin and Kirk if you guys want to jump in too. So I’d like to go ahead and push this slide for WebAction which I do think is really a very interesting company. Actually, I know Sami Akbay who is one of the co-founders, as well. I remember talking to him a couple years ago and saying, "Hey man, what are you doing? What are you up to? I know you’ve got to be working on something." And of course, he was. He was working on WebAction, under the covers here.
A question came in for you, Steve, so I’ll throw it over to you, of data cleansing, right? Can you talk about these components of this real-time capability? How do you deal with issues like data cleansing or data quality or how does that even work?
Steve: So it really depends on where you’re getting your feeds from. Typically, if you’re getting your feeds from a database as you change data capture then, again, it depends there on how the data was entered. Data cleansing really becomes a problem when you’re getting your data from multiple sources or people are entering it manually or you kind of have arbitrary texts that you have to try and pull things out of. And that could certainly be part of the process, although that type simply doesn’t lend itself to true, kind of, high-speed real-time processing. Data cleansing, typically, is an expensive process.
So it may well be that that could be done after the fact in the store site. But the other thing that the platform is really, really good at is correlation, so in correlation and enrichment of data. You can, in real time, correlate the incoming data and check to see whether it matches a certain pattern or it matches data that’s being retrieved from a database or Hadoop or some other store. So you can correlate it with historical data, is one thing you could do.
The other thing that you can do is basically do analysis on that data and see whether it kind of matches certain required patterns. And that’s something that you can also do in real time. But the traditional kind of data cleansing, where you’re correcting company names or you’re correcting addresses and all those types of things, those should probably be done in the source or kind of after the fact, which is very expensive and you pray that they won’t do those in real time.
Eric: Yeah. And you guys are really trying to address the, of course, the real-time nature of things but also get the people in time. And we talked about, right, I mentioned at the top of the hour, this whole window of opportunity and you’re really targeting specific applications at companies where you can pull together data not going the usual route, going this alternate route and do so in such a low latency that you can keep customers. For example, you can keep people satisfied and it’s interesting, when I talked to Sami at length about what you guys are doing, he made a really good point. He said, if you look at a lot of the new Web-based applications; let’s look at things like Twitter, Bitly or some of these other apps; they’re very different than the old applications that we looked at from, say, Microsoft like Microsoft Word.
I often use Microsoft as sort of a whipping boy and specifically Word to talk about the evolution of software. Because Microsoft Word started out as, of course, a word processing program. I’m one of those people who remember Word Perfect. I loved being able to do the reveal keys or the reveal code, basically, which is where you could see the actual code in there. You could clean something up if your bulleted list was wrong, you can clean it up. Well, Word doesn’t let you do that. And I can tell you that Word embeds a mountain of code inside every page that you do. If anyone doesn’t believe me, then go to Microsoft Word, type "Hello World" and then do "Export as" or "Save as" .html. Then open that document in a text editor and that will be about four pages long of codes just for two words.
So you guys, I thought it was very interesting and it’s time we talked about that. And that’s where you guys focus on, right, is identifying what you might call cross-platform or cross-enterprise or cross-domain opportunities to pull data together in such quick time that you can change the game, right?
Steve: Yeah, absolutely. And one of the keys that, I think, you did elude to, anyway, is you really want to know about things happening before your customers do or before they really, really become a problem. As an example are the set-top boxes. Cable boxes, they emit telemetry all the time, loads and loads of telemetry. And not just kind of the health of the box but it’s what you’re watching and all that kind of stuff, right? The typical pattern is you wait till the box fails and then you call your cable provider and they’ll say, "Well, we will get to you sometime between the hours of 6am and 11pm in the entire month of November." That isn’t a really good customer experience.
But if they could analyze that telemetry in real time then they could start to do things like that we know these boxes are likely to fail in the next week based historical patterns. Therefore we’ll schedule our cable repair guy to turn up at this person’s house prior to it failing. And we’ll do that in a way that suits us rather than having to send him from Santa Cruz up to Sunnyvale. We’ll schedule everything in a nice order, traveling salesman pattern, etc., so that we can optimize our business. And so the customer is happy because they don’t have a failing cable box. And the cable provider is happy because they have just streamlined things and they don’t have to send people all over the place. That’s just a very quick example. But there are tons and tons of examples where knowing about things as they happen, before they happen, can save companies a fortune and really, really improve their customer relations.
Eric: Yeah, right. No doubt about it. Let’s go ahead and move right on to MarkLogic. As I mentioned before, I’ve known about these guys for quite some time and so I’ll bring you into this, Frank. You guys were far ahead of the whole big data movement in terms of building out your application, it’s really database. But building it out and you talked about the importance of search.
So a lot of people who followed the space know that a lot of the NoSQL tools out there are now bolting on search capabilities whether through third parties or they try to do their own. But to have that search already embedded in that, baked-in so to speak, really is a big deal. Because if you think about it, if you don’t have SQL, well then how do you go in and search the data? How do you pull from that data resource? And the answer is to typically use search to get to the data that you’re looking for, right?
So I think that’s one of the key differentiators for you guys aside being able to pull data from all these different sources and store that data and really facilitate this sort of hybrid environment. I’m thinking that search capability is a big deal for you, right?
Frank: Yeah, absolutely. In fact, that’s the only way to solve the problem consistently when you don’t know what all the data is going to look like, right? If you cannot possibly imagine all the possibilities then the only way to make sure that you can locate all the information that you want, that you can locate it consistently and you can locate it regardless of how you evolve your data model and your data sets is to make sure you give people generic tools that allow them to interrogate that data. And the easiest, most intuitive way to do that is through a search paradigm, right? And through the same approach in search takes where we created an inverted index. You have entries where you can actually look into those and then find records and documents and rows that actually contain the information you’re looking for to then return it to the customer and allow them to process it as they see fit.
Eric: Yeah and we talked about this a lot, but you’re giving me a really good opportunity to kind of dig into it - the whole search and discovery side of this equation. But first of all, it’s a lot of fun. For anyone who likes that stuff, this is the fun part, right? But the other side of the equation or the other side of the coin, I should say, is that it really is an iterative process. And you got to be able to - here I’ll be using some of the marketing language - have that conversation with the data, right? In other words, you need to be able to test the hypothesis, play around with it and see how that works. Maybe that’s not there, test something else and constantly change things and iterate and search and research and just think about stuff. And that’s a process. And if you have big hurdles, meaning long latencies or a difficult user interface or you got to go ask IT; that just kills the whole analytical experience, right?
So it’s important to have this kind of flexibility and to be able to use searches. And I like the way that you depicted it here because if we’re looking at searching around different, sort of, concepts or keys, if you will, key values and they’re different dimensions. You want to be able to mix and match that stuff in order to enable your analyst to find useful stuff, right?
Frank: Yeah, absolutely. I mean, hierarchy is an important thing as well, right? So that when you include something like a title, right, or a specific term or value, that you can actually point to the correct one. So if you’re looking for a title of an article, you’re not getting titles of books, right? Or you’re not getting titles of blog posts. The ability to distinguish between those and through the hierarchy of the information is important as well.
You pointed out earlier the development, absolutely, right? The ability for our customers to actually pull in new data sources in a matter of hours, start to work with them, evaluate whether or not they’re useful and then either continue to integrate them or leave them by the wayside is extremely valuable. When you compare it to a more traditional application development approach where what you end up doing is you have to figure out what data you want to ingest, source the data, figure out how you’re going to fit it in your existing data model or model that in, change that data model to incorporate it and then actually begin the development, right? Where we kind of turn that on our head and say just bring it to us, allow you to start doing the development with it and then decide later whether or not you want to keep it or almost immediately whether or not it’s of value.
Eric: Yeah, it’s a really good point. That’s a good point. So let me go ahead and bring in our fourth presenter here, Treasure Data. I love these guys. I didn’t know much about them so I’m kind of kicking myself. And then Hannah came to us and told us what they were doing. And Hannah mentioned, she was a media person and she went over to the dark side.
Hannah: I did, I defected.
Eric: That’s okay, though, because you know what we like in the media world. So it’s always nice when a media person goes over to the vendor side because you understand, hey, this stuff is not that easy to articulate and it can be difficult to ascertain from a website exactly what this product does versus what that product does. And what you guys are talking about is really quite interesting. Now, you are a cloud-managed service. So any data that someone wants to use they upload to your cloud, is that right? And then you will ETL or CDC, additional data up to the cloud, is that how that works?
Hannah: Well, yeah. So let me make an important distinction. Most of the data, the big data, that our customers are sending us is already outside the firewall - mobile data, sensor data that’s in products. And so we’re often used as an interim staging area. So data is not often coming from somebody’s enterprise into our service so much as it’s flowing from a website, a mobile application, a product with lots of sensors in it - into our cloud environment.
Now if you’d like to enrich that big data in our environment, you can definitely bulk upload some application data or some customer data to enrich that and do more of the analytics directly in the cloud. But a lot of our value is around collecting that data that’s already outside the firewall, bringing together into one place. So even if you do intend to bring this up sort of behind your firewall and do more of your advanced analytics or bring it into your existing BI or analytics environment, it’s a really good staging point. Because you don’t want to bring a billion rows of day into your data warehouse, it’s not cost effective. It’s even difficult if you’re planning to store that somewhere and then batch upload.
So we’re often the first point where data is getting collected that’s already outside firewall.
Eric: Yeah, that’s a really good point, too. Because a lot of companies are going to be nervous about taking their proprietary customer data, putting it up in the cloud and to manage the whole process.
Eric: And what you’re talking about is really getting people a resource for crunching those heavy duty numbers of, as you suggest, data that’s third party like mobile data and the social data and all that kind of fun stuff. That’s pretty interesting.
Hannah: Yeah, absolutely. And probably they are nervous about the products because the data are already outside. And so yeah, before bringing it in, and I really like that refinery term, as I mentioned, versus the lake. So can you do some basic refinery? Get the good stuff out and then bring it behind the firewall into your other systems and processes for deeper analysis. So it’s really all data scientists can do, real-time data exploration of this new big data that’s flowing in.
Eric: Yeah, that’s right. Well, let me go ahead and bring in our analysts and we’ll kind of go back in reverse order. I’ll start with you, Robin, with respect to Treasure Data and then we’ll go to Kirk for some of the others. And then back to Robin and back to Kirk just to kind of get some more assessment of this.
And you know the data refinery, Robin, that Hannah is talking about here. I love that concept. I’ve heard only a few people talking about it that way but I do think that you certainly mentioned that before. And it really does speak to what is actually happening to your data. Because, of course, a refinery, it basically distills stuff down to its root level, if you think about oil refineries. I actually studied this for a while and it’s pretty basic, but the engineering that goes into it needs to be exactly correct or you don’t get the stuff that you want. So I think it’s a great analogy. What do you think about this whole concept of the Treasure Data Cloud Service helping you tackle some of those very specific analytical needs without having to bring stuff in-house?
Robin: Well, I mean, obviously depending on the circumstances to how convenient that is. But anybody that’s actually got already made process is already going to put you ahead of the game if you haven’t got one yourself. This is the first takeaway for something like that. If somebody assembled something, they’ve done it, it’s proven in the marketplace and therefore there’s some kind of value in effect, well, the work is already gone into it. And there’s also the very general fact that refining of data is going to be a much bigger issue than it ever was before. I mean, it is not talked about, in my opinion anyway, it’s not talked about as much as it should be. Simply apart from the fact that size of the data has grown and the number of sources and the variety of those sources has grown quite considerably. And the reliability of the data in terms of whether it’s clean, they need to disambiguate the data, all sorts of issues that rise just in terms of the governance of the data.
So before you actually get around to being able to do reliable analysis on it, you know, if your data’s dirty, then your results will be skewed in some way or another. So that is something that has to be addressed, that has to be known about. And the triangulator[?] [01:09:43] of providing, as far as I can see, a very viable service to assist in that.
Eric: Yes, indeed. Well, let me go ahead and bring Kirk back into the equation here just real quickly. I wanted to take a look at one of these other slides and just kind of get your impression of things, Kirk. So maybe let’s go back to this MarkLogic slide. And by the way, Kirk provided the link, if you didn’t see it folks, to some of his class discovery slides because that’s a very interesting concept. And I think this is kind of brewing at the back of my mind, Kirk, as I was talking about this a moment ago. This whole question that one of the attendees posed about how do you go about finding new classes. I love this topic because it really does speak to the sort of, the difficult side of categorizing things because I’ve always had a hard time categorizing stuff. I’m like, "Oh, god, I can fit in five categories, where do I put it?" So I just don’t want to categorize anything, right?
And that’s why I love search, because you don’t have to categorize it, you don’t have to put it in the folder. Just search for it and you’ll find it if you know how to search. But if you’re in that process of trying to segment, because that’s basically what categorization is, it’s segmenting; finding new classes, that’s kind of an interesting thing. Can you kind of speak to the power of search and semantics and hierarchies, for example, as Frank was talking about with respect to MarkLogic and the role that plays in finding new classes, what do you think about that?
Kirk: Well, first of all, I’d say you are reading my mind. Because that was what I was thinking of a question even before you were talking, this whole semantic piece here that MarkLogic presented. And if you come back to my slide, you don’t have to do this, but back on the slide five on what I presented this afternoon; I talked about this semantics that the data needs to be captured.
So this whole idea of search, there you go. I firmly believe in that and I’ve always believed in that with big data, sort of take the analogy of Internet, I mean, just the Web, I mean having the world knowledge and information and data on a Web browser is one thing. But to have it searchable and retrievable efficiently as one of the big search engine companies provide for us, then that’s where the real power of discovery is. Because connecting the search terms, sort of the user interests areas to the particular data granule, the particular webpage, if you want to think the Web example or the particular document if you’re talking about document library. Or a particular customer type of segment if that’s your space.
And semantics gives you that sort of knowledge layering on top of just a word search. If you’re searching for a particular type of thing, understanding that a member of a class of such things can have a certain relationship to other things. Even include that sort of relationship information and that’s a class hierarchy information to find things that are similar to what you’re looking for. Or sometimes even the exact opposite of what you’re looking for, because that in a way gives you sort of additional core of understanding. Well, probably something that’s opposite of this.
Kirk: So actually understand this. I can see something that’s opposite of this. And so the semantic layer is a valuable component that’s frequently missing and it’s interesting now that this would come up here in this context. Because I’ve taught a graduate course in database, data mining, learning from data, data science, whatever you want to call it for over a decade; and one of my units in this semester-long course is on semantics and ontology. And frequently my students would look at me like, what does this have to do with what we’re talking about? And of course at the end, I think we do understand that putting that data in some kind of a knowledge framework. So that, just for example, I’m looking for information about a particular customer behavior, understanding that that behavior occurs, that’s what the people buy at a sporting event. What kind of products do I offer to my customers when I notice on their social media - on Twitter or Facebook - that they say they’re going to a sporting event like football, baseball, hockey, World Cup, whatever it might be.
Okay, so sporting event. So they say they’re going to, let’s say, a baseball game. Okay, I understand that baseball is a sporting event. I understand that’s usually a social and you go with people. I understand that it’s usually in an outdoor space. I mean, understanding all those contextual features, it enables sort of, more powerful, sort of, segmentation of the customer involved and your sort of personalization of the experience that you’re giving them when, for example, they’re interacting with your space through a mobile app while they’re sitting in a stadium.
So all that kind of stuff just brings so much more power and discovery potential to the data in that sort of indexing idea of indexing data granules by their semantic place and the knowledge space is really pretty significant. And I was really impressed that came out today. I think it’s sort of a fundamental thing to talk.
Eric: Yeah, it sure is. It’s very important in the discovery process, it’s very important in the classification process. And if you think about it, Java works in classes. It’s an object oriented, I guess, more or less, you could say form of programming and Java works in classes. So if you’re actually designing software, this whole concept of trying to find new classes is actually pretty important stuff in terms of the functionality you’re trying to deliver. Because especially in this new wild, wooly world of big data where you have so much Java out there running so many of these different applications, you know there are 87,000 ways or more to get anything done with a computer, to get any kind of bit of functionality done.
One of my running jokes when people say, "Oh, you can build a data warehouse using NoSQL." I’m like, "well, you could, yeah, that’s true. You could also build a data warehouse using Microsoft Word." It’s not the best idea, it’s not going to perform very well but you can actually do it. So the key is you have to find the best way to do something.
Kirk: Let me just respond to that. It’s interesting you mentioned the Java class example which didn’t come into my mind until you said it. One of the aspects of Java and classes and that sort of object orientation is that there are methods that bind to specific classes. And this is really the sort of a message that I was trying to send in my presentation and that once you understand some of these data granules - these knowledge nuggets, these tags, these annotations and these semantic labels - then you can bind a method to that. They basically have this reaction or this response and have your system provide this sort of automated, proactive response to this thing the next time that we see it in the data stream.
So that concept of binding actions and methods to specific class is really one of the powers of automated real-time analytics. And I think that you sort of hit on something.
Eric: Good, good, good. Well, this is good stuff. So let’s see, Will, I want to hand it back to you and actually throw a question to you from the audience. We got a few of those in here too. And folks, we’re going long because we want to get some of these great concepts in these good questions.
So let me throw a question over to you from one of the audience numbers who’s saying, "I’m not really seeing how business intelligence is distinguishing cause and effect." In other words, as the systems are making decisions based on observable information, how do they develop new models to learn more about the world? It’s an interesting point so I’m hearing a cause-and-effect correlation here, root cause analysis, and that’s some of that sort of higher-end stuff in the analytics that you guys talk about as opposed to traditional BI, which is really just kind of reporting and kind of understanding what happened. And of course, your whole direction, just looking at your slide here, is moving toward that predictive capability toward making those decisions or at least making those recommendations, right? So the idea is that you guys are trying to service the whole range of what’s going on and you’re understanding that the key, the real magic, is in the analytical goal component there on the right.
Will: Absolutely. I think that question is somewhat peering into the future, in the sense that data science, as I mentioned before, we saw the slide with the requirements of the data scientist; it’s a pretty challenging role for someone to be in. They have to have that rich knowledge of statistics and science. You need to have the domain knowledge to apply your mathematical knowledge to the domains. So what we’re seeing today is there aren’t these out-of-the-box predictive tools that a business user, like, could pull up in Excel and automatically predict their future, right?
It does require that advanced knowledge in technology at this stage. Now someday in the future, it may be that some of these systems, these scale-out systems become sentient and start doing some wild stuff. But I would say at this stage, you still have to have a data scientist in the middle to continue to build models, not these models. These predictive models around data mining and such are highly tuned in and built by the data scientist. They’re not generated on their own, if you know what I mean.
Eric: Yeah, exactly. That’s exactly right. And one of my lines is "Machines don’t lie, at least not yet."
Will: Not yet, exactly.
Eric: I did read an article - I have to write something about this - about some experiment that was done at a university where they said that these computer programs learned to lie, but I got to tell you, I don’t really believe it. We’ll do some research on that, folks.
And for the last comment, so Robin I’ll bring you back in to take a look at this WebAction platform, because this is very interesting. This is what I love about a whole space is that you get such different perspectives and different angles taken by the various vendors to serve very specific needs. And I love this format for our show because we got four really interesting vendors that are, frankly, not really stepping on each others' toes at all. Because we’re all doing different bits and pieces of the same overall need which is to use analytics, to get stuff done.
But I just want to get your perspective on this specific platform and their architecture. How they’re going about doing things. I find it pretty compelling. What do you think?
Robin: Well, I mean, it’s pointed at extremely fast results from streaming data and as search, you have to architect for that. I mean, you’re not going to get away with doing anything, amateurish, as we got any of that stuff. I hear this is extremely interesting and I think that one of the things that we witnessed over the past; I mean I think you and I, our jaw has been dropping more and more over the past couple of years as we saw more and more stuff emerge that was just like extraordinarily fast, extraordinarily smart and pretty much unprecedented.
This is obviously, WebAction, this isn’t its first rodeo, so to speak. It’s actually it’s been out there taking names to a certain extent. So I don’t see but supposed we should be surprised that the architecture is fairly switched but it surely is.
Eric: Well, I’ll tell you what, folks. We burned through a solid 82 minutes here. I mean, thank you to all those folks who have been listening the whole time. If you have any questions that were not answered, don’t be shy, send an email to yours truly. We should have an email from me lying around somewhere. And a big, big thank you to both our presenters today, to Dr. Kirk Borne and to Dr. Robin Bloor.
Kirk, I’d like to further explore some of that semantic stuff with you, perhaps in a future webcast. Because I do think that we’re at the beginning of a very new and interesting stage now. What we’re going to be able to leverage a lot of the ideas that the people have and make them happen much more easily because, guess what, the software is getting less expensive, I should say. It’s getting more usable and we’re just getting all this data from all these different sources. And I think it’s going to be a very interesting and fascinating journey over the next few years as we really dig into what this stuff can do and how can it improve our businesses.
So big thank you to Techopedia as well and, of course, to our sponsors - Pentaho, WebAction, MarkLogic and Treasure Data. And folks, wow, with that we’re going to conclude, but thank you so much for your time and attention. We’ll catch you in about a month and a half for the next show. And of course, the briefing room keeps on going; radio keeps on going; all our other webcast series keep on rocking and rolling, folks. Thank you so much. We’ll catch you next time. Bye-bye.
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