Is there a future for business intelligence? Understand the key trends, technologies and techniques!
So in the beginning, back least the beginning for me was the early 90s there was business intelligence as we just talked about, and I would say within a decade it became synonymous with reporting and more specifically with dashboarding. That's what people kind of think of it now when they use that term, correct me if I'm wrong. And I wrote a book on dashboards in 2005 and a second edition in 2010, so I was kind of in the middle of that whole phenomenon of business intelligence when I worked at the Data Warehousing Institute, but more recently BI has come under assault I would say a little bit. Last year ThoughtSpot started to market this campaign that dashboards were dead, provoked a lot of interesting commentary and I of course didn't quite agree with that but I might be biased.
But over the years, people have been quick to point out business intelligence limitations if we think of BI not as the the global umbrella for things that use data to make your business more intelligent, but more as the reporting side of that whole equation, people have pointed out that BI is too reactive, it's only looking at past results, it's too generalised you're only getting the top level summary of information and the devils and the details it's too manual to put together as well as to interpret and it's too descriptive in other words it's not analytical enough it's not predictive enough, it's too inflexible you know someone has to define upfront what the metrics are that you're going to measure, what are the thresholds that that colour code performance, too hard to use especially for what I would call casual users or regular business users, people who aren't hired to crunch data on a day-to-day basis, and probably that the biggest complaint that I've heard is that it doesn't turn insights into action. So, people have been saying that BI has a last mile problem, and as a result they're questioning what is the value that we're really getting out of these reporting tools, we're spending a lot of money on them still today but are we getting a lot of value are we getting not just insights but are we getting actions based on those insights, and can we measure those actions, and are they moving the needle for our organisation. So I think that's the big question and I think a lot of BI vendors out there, wrestle with this if they're not power BI or Tableau, they face this situation a lot. What value do we bring to our customers how can we help them justify the expenditures on these tools, so that they continue to do business with us, or make room for us in their BI portfolio and as a result we're seeing this is jumping ahead a little bit and just from a product perspective that vendors who are not Tableau or Power BI are kind of taking one of two approaches.
They're either going very broad and focusing about something called this decision intelligence, which is kind of putting everything in the kitchen sink, into their platform from reporting, to dashboarding, to everything up to predictive analytics, and also some AI to help close that gap between data and actions, or they're drilling down into analyst specific tools to really move the needle on self-service, and making our power users, especially our data analysts much more productive and collaborative, so we'll talk about those two trends. So, Eckerson group TDWI or I used to work in BARC which is a big partner of ours in Germany, we've all done these surveys to try to track the adoption of BI in Enterprises and we always come up with the same results, that on average the BI tools are used by about 20 to 25% of employees and that figure is not budged in the last 10, 15 years really. And people have always scratched their heads, BI vendors are scratching their heads, even BI managers sometimes you know, they bring tools in and expect immediate adoption especially as the tools get easier to use and they're all self-service, but still you know there's still a lot of shelf wear, so what does this mean? And I kind of ask a contrary question which is, is this really a problem? Because to me the BI market is not just one market, it's actually four markets and I've had this framework for who are the users of BI tools or analytics tools, for many many years that's why it kind of looks a little bit dated here, the graphics, but I kind of see that there's two major categories of users within those categories there's two subsets and these really define the market for BI and why I think some vendors have difficulty addressing it. As you can see here, that data consumers and
those are folks are just essentially viewing dashboards and reports those are executives frontline workers frankly as well as customers and suppliers to some extent, and they're the vast majority of the consumers of BI and a lot of times those folks aren't counted in surveys that we do and in fact a lot of times those folks don't even know they're using BI because they're looking at an embedded visualisation inside of another application a portal or ERP or a CRM application and they don't to them that really doesn't count as BI. They're also data explorers and these are data consumers who actually want to do more with the data they want to dig into it a little bit more they want to open up a dashboard and modify it, maybe even create their own dashboard or add their own Excel data to it, so they want to enrich reports, that's about 30 percent of your employees and that's actually the fastest growing group as people graduating from college get more data literate, and tool literate, analytics literate, I think we'll see the explorers poach more and more of the consumers. Now these folks, don't do cell service in my opinion they're doing silver service which means they really want data served up to them on a silver platter, in the form that they want and can use immediately they really don't want to do a lot of hard work because frankly they're not getting paid to be analysts right, so they're not getting paid and not being hired to be power users like the data analysts and data scientists below, those are the folks who in my estimation really need self-service tools and by that I mean the ability to create stuff from scratch. you know go out and find data go out and pull it together, prepare it, modify clean it, visualise it analyse it, share it and publish it and data scientists two percent of your employees they obviously are much more academically trained, they're the ones who are using ML and AI to create predictive models, and discover patterns in large volumes of data. So when I look at the market we're seeing a lot of tools and functionality that are geared to these different types of users or should be geared to these different types of users, but vendors kind of have a blunt instrument when they market, and they tend to provide tools for everybody because they don't want to short shift their revenue stream right, but in reality a lot of times they're pitching power user tools to business users which is what causes a lot of the shelf wear that I talked about earlier. So to me, what do data consumers really need? Well they need those interactive dashboards that contain just the metrics that they're responsible for, and the ability to drill down and drill across and see performance of themselves or their unit, especially if they're frontline workers or even customers and suppliers they want embedded analytics, and that market from a vendor perspective this exploded, vendors see that as a whole new outlet for their products and tools, and a number of vendors have converted from selling you know retail BI, to embedded BI, Logi analytics was one, and more recently PsySense has focused 100% now on embedded analytics.
And now of course with the advent of Chat gbt, you have to ask is that going to replace all the tools that we already have for data consumers, and maybe even everyone else as well it's a, as you probably all looked at, it's an incredibly powerful tool, it's conversational it remembers the questions you asked prior, it's fast, it gets it right most of the time and people have told me that version 4 is going to be miles above what we're seeing today and I can't imagine what the impact is going to be so we'll talk a little bit more about Chat gbt in a second. So for these data explorers, they need to do some ad hoc stuff right so, this notion of natural language queries where you can type words into a search bar and generate sequel behind the scenes, which returns an answer and displays it as a chart or table and you can keep iterating, you know as long as someone has built a nice model behind the scenes that can work pretty well, so ThoughtSpot they came out of the the gate doing this right, made a big name for themselves being the Google of BI, we're going to simplify BI so everyone can use it well it's never as easy as anyone makes out. The number two here is something that's been around for a long time that's the ability if you see a dashboard, there should be a big button up in the top right or somewhere, that if you have the right permissions you can click that button and you can open up the model behind the dashboard and begin to to modify it right. So you can add dimensions that weren't added into the dashboard but we're in the model, that power's it. You can perhaps combine columns, you know you can you can do your own calculations and things like that, maybe even add your own spreadsheet data to. So that's something that these explorers want to do. So those are like managers, who are very data driven and want to get a little
bit more detail than what's you know available in the canned reporter dashboard. And then there's this third thing that you've probably seen, I call it assisted analytics, that's where you get a dashboard and if you put your cursor on a number or a chart behind the scenes there's an algorithm that runs against the data powering that dashboard and it will work on calculate correlations things that are hidden in the data below the level of the dashboard, that you may not have considered or thought about looking at, and those products I don't know how well used they are frankly from the conversations that I've had, not that many people use them but for a data explorer it's something that would help them, instead of them having to do an edit right of the dashboard or report, they could play around with that assisted analytics button and see what the tool automatically generates for insight and correlations that might be worth looking at. But I think where the real market is today and a lot of the innovation is, is tools for these data analysts and to me when we do consulting I always try to find out you know who are the data analysts in the organisation because they're kind of spread across all the departments and functional areas, and they're kind of the linchpin for an analytics data and analytics strategy, because they've got one foot in the business and one foot in data and technology, they're the quintessential purple people as we used to call them at TDWI, not totally in the business, not totally in IT they're a blend of both and they can really make or break your whole dating analytics strategy so it really behoves us to pay attention to them, one find out who the heck they are because a lot of companies don't even know and they have no idea how much they're spending on them which is usually millions more than they they would ever guess because these are the folks who are actually trying to make data work in the trenches right, trying to help the department heads use data to answer questions they have usually, it's ad hot questions, but they're overwhelmed because to generate a decent insight oftentimes means lots of operational work. Finding data, cleaning it, blending it you know just way too much work, they spend 80% of their time fixing data and 20% analysing it so there's a huge opportunity it's been there for decades to really empower these folks and we're starting to see some tools that really make progress, so one is business monitoring and I'll drill into tha.t It's kind of a turbocharged version of the assisted analytics that I talked about earlier. There's an analytics workbench which try to pull us in all the capabilities that a data analyst would need, to support their end-to-end workflow and collaborative intelligence is an extension of that which essentially says analyst you're all isolated, you're all reinventing the wheel, let's create one platform that you can all use together, follow each other, reuse components that you've built and workflows you've built and allow administrators who are super super users to define things once standardised where they are placed and have everyone use them right, and that alone would increase the productivity of this group of folks tremendously so I'm pretty excited about some of the products we're seeing down there. And
then finally data scientists, they're in a whole category by themselves, they also have workbenches from Data IQ and companies like that kind of coordinate their activities across the Enterprise. These big cloud data platforms are increasingly catering to data scientists and including functions and stored procedures that can kick off machine learning functions providing zones or discovery zones inside their platform, so they don't have to download all this data to their desktop they can actually do their work and train their models on a very robust environment, processing environment, where all the data already exists right. And then finally the hardest part, we've verified this in our own consulting is actually deploying these models, creating pipelines that will distribute the data to the models in real time embedded inside applications and that whole process of productionising models and maintaining the quality and the accuracy of the output, is tremendously difficult and there's this whole I guess discipline, I'm just calling ML Ops that's involved in doing that. So Neil, if there are any questions that you think are relevant just stop me and you know insert them here because I really can't do that. There's a few coming down so "how do we categorise employee usage?" was a question I think you may have addressed that already in here or maybe not. Yeah I think for the surveys we tried to, I know the last round we did with BARC we tried to include the embedded usage but it still came out to 20% but yeah it's across all employees that's the percentage that we're tracking, how many are using BI in an active way.
That's it, I don't think there's many others I'm not sure that was from Nathan, he was talking about people who don't view reports or dashboards so I guess there are other types of consumers nowadays because how warehouses have shifted from being just a reporting centre to a data feed as well for downstream consumers as well. Right, so not much else in there at the moment, anyone else please add things, oh "will Power BI and Tableau model become passé" was a question as well, good question. Some people are saying that the whole markets become commoditised, you look at all the layoffs from salesforce, a lot of them were Tableau folks so it doesn't quite bode well. I think BI has to, it has to move beyond and add more value, as I said earlier it has to it has to close that last mile or really improve the productivity of your data analysts and data scientists to be worth the investment and from a pricing perspective you know Power BI comes almost for free so it's very easy to pick up and use so it's hard to compete with that, just from a market perspective. I think certainly agree with that, and there's another one from Chris, which I think is a good one as well which is "is it a warehouse analytic or is it integrating operational data feeds, or is it both?" Are we converging to where we have a sort of dual function of a warehouse, it's no longer just reporting, it's also part of the operational data feeds.
It's always been a data hub of sorts, I mean obviously it's designed to support analytics right, but going back decades, I always remember every company I went into you know people were taking exports of the data warehouse and even real-time feeds to populate operational applications so, it's always been a hub of sorts on its own, although I just got off the phone with Roche Pharmaceuticals and some of you guys probably know that company and they've made a very public move to the data mesh and we had a one-on-one with the head of data warehousing or I'm not sure what his title is but you know part of the data team and you know the whole mesh thing is very interesting and they're implementing the mesh using Snowflake, which is a centralised data platform but it works to support the mesh in a decentralised way um but the last question we asked him was "all right so what's happening to your data warehouse and your data lake are you keeping that? And he gave me kind of a mixed answer, he said well we're migrating our Oracle on-prem data warehouse to the cloud, but yes the goal is to have all the data domains building out our mesh to ingest their own operational data and make that available as data products, so that we don't need a data warehouse anymore, now that actually would be an interesting conversation for this group and maybe we have another webinar on that topic alone because it's very interesting. There are a lot of problems with the data mesh and it's probably going to crash and burn, that's always been my prediction and my conversation with this really nice fella there, confirmed that yet at the same time they're getting some real benefits out of it and they've got IT out of the way of what users need to do and want to do with their data so, all right so I don't even know how I got on that topic. Absolutely fine I think it's a natural consequence of what the question was I think I fully agree with you, we did actually have Paul Rankin from a couple of sessions ago to present, but if he's moved on a bit we need to drag him back in again to talk more. Well he's learning a lot that's and that's exactly what I just got off the phone with him. Oh wonderful wonderful, it's interesting I agree mesh is an interesting thing, and I hope that the industry learns from mesh takes the best bits out and moves forward because it's got a lot of good intentions in there. It certainly has implications for
the warehouse right and what you guys all do with your Data Vault model so, interesting. Okay so back to you again there's we're out of questions, others please enter more questions if you have any and we'll carry on. So, we talk about business intelligence and it's not going away, it's actually the foundation for everything that we do, as you can see from this diagram here. There are three areas of intelligence at least in my estimation and
the business intelligence was the IT driven era that started in 1990 and went roughly to 2005, and then the self-service intelligence era began and that's gone for 15 years, and now we're into the model driven era where we're using ML and AI extensively, but none of these go away, none of them supplant the other, they all actually build on each other and as you move up this food chain here, the benefits start increasing as you can see from down below, we go you know the focus moves from past, to present, to future, reports are moving from IT generated, to business generated, to machine generated, analysis goes from intuition driven, hypothesis driven, to model driven, actions from reactive, to proactive, to automated and the impact is neutral to positive to absolutely game changing. There are things you need to master at each of these levels, now every organisation goes through this orbit in different ways, in different paces but from my perspective these are the keys that you need to master in each of these errors or stages of maturity, in your data and analytics journey. So in the business intelligence we really are trying to figure out how to consolidate our data, integrate it, ensure it's clean and trustworthy, we've got standard reference data and master data and most importantly from a business perspective we've got standard KPI's and dashboards that display them. Every company that we consult with, still has problems with those. There's still a lot of work to do there and the self-service era, people think that's a slam dunk, easy, everyone wants to do their own data, but I think we've come to realise that unless you govern your self-service deployments, they become absolute chaotic messes and you actually can make things worse than before you started with self-service so you need strong data and report governance, you need data literacy, you need this workbench that's basically self-service tools, for your power users and you need a data refinery, that's basically a warehouse or a lake or a lake house or whatever centralised repository you use to store your data or repositories where the zones are places where you give users access based on their role and what they're trying to do. So you have you know your basic landing zone that no one accesses really, it's just the raw data, it's permanently stored, your refined zone, your trusted zone, your formatted zone, your discovery zone, your integration zone, those are just some of the zones that come on top of my mind that we use in a recent client engagement, and they were all important. And then, in this last era it's all about
machine learning and AI, creating those robust pipelines and deployment vehicles, which I said was it was pretty hard. The interesting thing about AI is that we're not only using it to do analysis and prediction for the company, AI is being built into all of our data and analytics tools itself, making those tools much more productive and automated than they were 5, 10 years ago and that's pretty impressive and that's what's fuelling a lot of these new tools, for data analysts and data scientists and even for casual users as well. Kind of a slightly different take on this, as you said the evolution of intelligence we've gone from Human Intelligence where actually we're pretty good computers ourselves, we collect data using our brains, we detect patterns and make decisions based on those people call that intuition right? When we make decisions based on our gut feel, but there's actually a lot of data and pattern detection going on there and sometimes our intuition is extremely accurate, but other times it can be completely way off the mark and we just have to be aware of our internal biases that might affect the quality of our decisions. So with BI now, we've introduced
machines and they're doing some of the heavy lifting for us, they're collecting all this data and organising it and then summarising it based on what we think we need, and we look at those summarised data sets or dashboards and make judgments and business decisions so that's great! Augmented Intelligence goes to the next step and uses machines and AI to not only collect the data, but also rummage through it with these very powerful algorithms, to understand the patterns, the correlations, the trends, the anomalies, and then suggest possible actions. Then humans look at those suggestions and either adopt them or override them, and make a decision. In my opinion this is the most powerful way that our organisations can make use of AI, is by using the algorithms to augment what we do, and if they're good and in a lot of cases they are much better, more accurate, more consistent than humans, we begin to trust them quite a bit, that doesn't mean you should just let them necessarily go on unattended, because bad things can happen like we saw the financial crisis of 2008, where people start trusting these models implicitly and didn't see the Black Swan right in front of them, they didn't know the limitations of those models. So obviously the end goal, and for some people is completely automated actions and that that can happen in our space, we're certainly trying to do that with autonomous vehicles we're seeing that with chat bots and Chat gbt is an exemplary of that for sure.
So again, you can't trust these things implicitly I've always said that I would never be the first person, I didn't know if I would ever take a an autonomous vehicle and let it drive me around Boston because it I just don't think it would be capable of handling all the different variations. We have really bad traffic patterns here in Boston, and then if you add snow, sleet, hail, I think even it's too much for a machine to handle, but there's going to be instances where it works perfectly fine. All right so that brings us to ChatGBT-3, I'm sure all of you guys have looked at this right? And gals and it begs these questions, will it become the interface for analytics? Will it replace analytical tools? Will it replace data analysts? I mean, what I've seen so far is quite impressive, but you have to remember it's a language model right now that deals with text right, so there are some considerations there. I actually just did a podcast with the CEO of Sisense and they're building to ChatGPT's API, so, in this extension, allows you to convert queries or questions into ChatGPT questions and converts the answers into tabular format which it displays in Sisense, I'm not sure how useful that is at the moment, but you have to believe that, every analytical tool, every data tool, is going to figure out how to use and benefit from ChatGPT but there are limitations, I'd love to hear what you guys think too. So as I said, it only works with
text right now, not taggler, data doesn't have DBMS connectors, so you can't really point it at your own data and and let it rummage, although I've heard someone said that they could do that, so wouldn't that be interesting if you could let ChatGPT loose on your own databases, and then enable people to ask questions, so maybe they're just more tactical questions. They don't require the joining of data, but then again ChatGPT can pull data from multiple sources and come up with a coherent response so, we actually don't know the full extent of the power of this tool for our spatia, I think we're still early on and obviously it doesn't always get things right, because there's a lot of crud out in the internet and there's a lot of crud in our own databases too. It doesn't visually display anything right now and I read somewhere that requires a lot of data to achieve or approximate a reasonable degree of accuracy let me stop there, I'd love to see if anyone has some comments on this ChatGPT.
One question about visual prompts for ChatGPT is that "are there any visual prompts yet?" don't remember seeing anything there. I haven't does anybody else, has anyone seen that I don't think so. I'm worried about the integrity of source information if ChatGPT generates stuff which it then feeds off itself for learning from, there'll be no one creating original content and it'll end up being self-referential. That was my initial reaction to the whole thing, that it would just been breathing its own exhaust, I was told recently that actually that makes it smarter, because the first pass it gives a reasonable response to your question right? And then if it gets another try, it will take what it already did and make it much better I'm like huh that's interesting, it's like I make my research analysts do multiple drafts of reports right and each time it gets better and maybe the same is true with ChatGPT, I mean this thing is starting to feel like it's got human blood, so.
We have a lot of questions coming in, not all relevant necessary to the point we're talking about here but the challenges of getting governance right, because that's probably the hardest part of BI, the technology is the easy bit to solve it's the governance and political aspects that kill the projects. I couldn't agree more. "What's the alternative to Power BI and client-side ETL in the future as you see it?" Could be this. Client-side ETL, well there's a lot of these data prep tools, and there's a big movement to create citizen data integrators right, so you don't need IT people to build your pipelines anymore you can have your regular run-of-the-mill data analysts. There's a few people are wondering "are we building Hal from 2001?". We're building what? Hal, the computer that took over the spaceship. Yeah, ChatGPT is yeah. You heard it first here, Wayne said we're building Hal. Okay I think we've got a few more questions towards the you've got some more slides, do you want to cover yeah. So how much time do we have here? We've got a 10 minutes or so and
then we can take questions at the end and if necessary we can take things offline at that point. All right um. But we can't overrun this, there's no real hard limit so, if you want to spend an extra few minutes there's no trouble. Yeah so, one of those tools for power use, I call business monitoring that, some people call it augmented analytics, there's a lot of different names for it, but it's like these IT monitoring or application monitoring tools we use to manage our infrastructure but it's actually for business metrics. It's just releasing these very sophisticated
algorithms on your business data and allowing them to rummage through them on a regular basis, and then surface only the things that are really relevant for you as an individual, and I think this holds a lot of promise to improve the productivity of data analysts. So, a dashboard I always said, if you had a thousand metrics that was a good size dashboard right? 10 metrics, 10 Dimensions, 10 hierarchies and a business monitoring system it might be evaluating a million or more metrics yeah, 100 metrics 100 Dimensions, 10 hierarchies at the minimum right but maybe even much much more and because it's a machine it doesn't ever get tired and you just let it loose on your business data and it is going to spot and surface the relevant trends correlations and anomalies and basically it's a modern AI driven alerting system or surveillance system, that is going to be very useful for data analysts to point them in the right direction it's not going to necessarily do the work for them, although it might in some cases spot things that they should have spotted, but in many cases it's going to help them figure out where to do their analysis and really save them a bunch of time. And the way these things work is, you just give it your data and it starts processing it and it learns, it creates a baseline for every metric and combination of metrics right so you may have a million combinations of metrics and dimensions, and time and it's going to create a baseline for each of those metrics right, by a day, by a season and gradually it will refine that baseline, so that if there's an anomaly, it will detect it very quickly.
So let's see here one, learn metric behaviour, two, detective significant deviations, personalise the alerts so here's the thing, if every deviation you were alerted to it you would just ignore it right and that that's what happened to the BI tools back in the early 2000s that started to offer alerts everyone just ignored them, because they were dumb right. Now we've got smart alerts, and the next step for these things is to correlate deviations to actually suggest root causes and ideally in the future remediation. So this is going to help us close that castle right, the last mile BI between insights and action, these tools will help do that.
So here's an example from Outlier, which I really like this product and this tool, unfortunately it's a funny story, they had a deal to be acquired by Twitter and the deal is going to close unfortunately the day before Elon Musk decided to buy Twitter and then the whole thing fell apart, the company ran out of money and I don't even know where the IP is, I asked because I thought this was a really good tool, you can see here how they the spot, detect anomalies but they do a lot more than just anomalies, they do correlations and what causes, which are essentially very, there are correlations that there's a high degree of confidence in, that's really what they determine as a root cause. So you can see here at the bottom here they've got three potential root causes. So, the cool thing about this tool, is that it uses algorithms to personalise the alerts so that you weren't overwhelmed and it gave you each day a daily briefing book with three interesting pieces of data that you probably need to know based on you and things you were interested in.
So it's like hiring an army of analysts because it's doing a lot of the heavy lifting. Makes them 10 times, 100 times more productive once in the right direction, separates the signal from the noise which is what an analyst does, call it a data surveillance engine. Well I guess we got a couple more minutes, so another thing we're seeing is tools that kind of pull together all the different capabilities that a data analyst might need to do their job well and there's a lot of different capabilities as I'm building out this slide here, and we're seeing some tools pull all these together in a coherent way and one tool I really like is Promethium it's kind of a data catalogue that was its heritage, and it uses AI to do natural language certs and underneath the covers there's a data virtualisation capability that federates data across the Enterprise. And then once you find your data, you can profile and evaluate it, then it lets you prepare the data through joining the data, building pipelines and visualising the data.
So, it's a complete workbench, very powerful and it also has got some interesting workflows built in which I'll talk about in the next slide, which is the collaborative intelligence capability. So you see the workflow in the middle here, and if you put this platform and make it available to all of your data analysts throughout the Enterprise, you create a fabric that stitches them all together right so now they can start seeing what everyone else is doing and start to perhaps reuse work instead of reinventing the wheel so, the first thing they need is this catalogue and ideally it's got metrics and queries in there that they can reuse. It allows them to follow each other and rate workflows, share of workflows that they build, it allows administrators or authorised super data analysts to build guard rails into the platform so that maybe they're all joining data the same way or handling slowly changing dimensions the same way. And that the cool thing is ideally they've got workflow that enables business users who have hit the limits of their dashboard or maybe they're even you know junior data analysts right, and they need data to do their analysis and how's that happen today? Well they find a friendly data analyst or a data engineer and they send an email or create a ticket right, but it's not a very iterative process it's not a very transparent process, so some of the new tools like promethium does a really good job of building a workflow between business users and data analysts and data engineers so that requests one, if they've already been asked they're answered immediately with existing data sets. If they're not asked people can then start to have a
chat and collaborate more closely with the data analyst to refine the query or the question, until they get a satisfactory answer. So I think that's very heartening. All right we kind of talked about this. If you're not Microsoft or Power BI what do you do you? You either go broad and try to be all things to all people or you go deep and try and build one of these analyst tools we're just talking about. This is the go broad version, it's got everything in the kitchen sink in it.
For any type of user, they're not really differentiating between power users and casual users. Some vendors are marketing or messaging this decision intelligence Pyramid Domo Tellius and Diwo, but most BI tools are heading this way. They're adding all the capabilities, even data science built into a BI tool, data prep built into a BI tool, simulation built into a BI tool. You got to wonder if a bi tool is really capable of handling all of that, and whether people would really want to go to a BI tool to do all that!