Power BI vs Tableau: Ad Hoc Analysis

This video will do a comparison of ad hoc analysis and authoring between Power BI versus Tableau. First of all, both products are really easy to use. In the comparison, we'll focus on popular features that anyone can learn with a little bit of training. Now, very important. This video will focus on the effect efficiency of ad hoc analysis and authoring. And for efficiency, it's really important to look at the long term needs. Most BI projects at the beginning, they start simple, usually with a small number of users and maybe only one or two departments. But typically several months out or maybe a year or so out, you have a lot more users in a lot more departments involved in your BI project. So when it comes to efficiency, you have to look at the long term needs, not in the short term because that can change dramatically the outcome of your assessment. Keeping that in mind. So what you have to look is what is the expected number of hours per month that your team will be doing ad hoc analysis and authoring. If the number of hours is low, then both projects are great and any of them will work really well for your organization. Now, if the number of hours per month is high, then you have to do an assessment of which one is the best fit because that can change dramatically the outcome of your project when it comes to the time that it takes to to get the job done on an ongoing basis as well as the long term labor cost associated with your project. In order to do this analysis, what we did is we look at a handful of our clients in what they did in the last couple of years when it comes to ad hoc analysis and authoring to make an assessment between the two products. And hopefully this assessment will help you understand the differences. Hopefully it might relate to your business or at least inspire you, inspire you on how you can do your own assessment to find which one is the right fit. We'll start this comparison with an ad hoc analysis exercise to show both products side by side. The purpose of this exercise is to show that both products are really easy to use, but most importantly to also highlight some differences in the efficiency of ad hoc analysis that plays in the long run. It's a simple query. It will show sales and gross profit percentage by salesperson. It will be sorted in a descending order by sales. It will highlight which salespeople are meeting the gross profit goal of 50% and above. So let's take a look right here. I have side by side on the left, Power Bi on the right, tableau. So I will build this query first in tableau. So I want to see sales by salesperson in a descending order. And I make vertical, so it's easy to read the names. So right now I have simply sales by salesperson sorted in a descending order. I want to add gross profit percentage. So right there I have gross profit percentage. And the last thing I want to highlight which of the salespeople are meeting the gross profit percentage goal. So I'm going to use a color schema for that. And I'm going to change the schema to show only two colors. Where the middle is right here in 0.5, which is 50%. And there it is. That I just entered this question. I have all salespeople sorted in a descending order by sales. I have the gross profit percentage. And this color Is for above 50% gross profit. And this for below. So easy. We got the answer. All right, that's the same thing. Power bi. So when I see sales by salesperson and right there I already got sorted in a descending order, so it's good. Now I also want to add gross profit percentage. And when I do that, right now the gross profit percentage is in black. And because it's using the same axis, this chart is not answering my question. It's almost useless. The GPP percentage values in this chart. So I have to change the visualization. There are different ways to go about this change. But since after I add the gross profit percentage, I also have to highlight which salespeople are meeting the goal. I'll go to a strategy that will do this efficiently. So I will remove the gross profit for now, I'll go back here and I'll do something similar to what I've done in tableau and do vertically. And then I'll add a second chart. And I need to sort this chart also by sales. There it is. So now hopefully both are aligned. So I have to be sure you do things that both charts have salespeople all aligned. So they're both aligned, which is great. Now what I can do is to make it cleaner. I can remove the Y axis from this report from the gross profit. That looks much cleaner. There it is. So I have sales descending order gross profit on this chart. And now I need to add the highlighting of the profitability goal. So let's do that. I'm going to add gross profit and color saturation. Do a little trick here to make this work really well. And same thing for that one. All right, there it is. So if you look now, both in tableau and power bi, we have the same report with the same information. You know, sales by salespeople In a descending order, with gross profit in the colors, also representing above and below the gross profit percentage goal of 50%. Now, as I said, both products was easy to answer this question, but in Tableau was significantly easier. What about this? What about that? Let me show you the stats of what we just went through. So if I were to rebuild the query from scratch without making any mistakes, and the numbers I'm going to show you now in these PowerPoint slides were, after repeating the same thing over and over and over without any mistakes, just focusing on the mechanical part of building each of these reports from scratch. So by answering the question, sales by salesperson, both products was super easy. You can do like in four seconds. And actually a lot of the reports and dashboards that you can build in Tableau or Power BI are similar. It's going to take about the same time. Sometimes one is a little bit faster than the other, but overall you can do a lot and both will do great. So that's awesome. It's great news. Now, depending the things that you need, and this example shows where we needed to do sales and gross profit percentage by salesperson in the same chart for Tableau, it was only 5 seconds, was only an extra click, and the answer was right there. With Power bi, I had to go through a lot more clicks and know where to go to answer the same question. So we took, you know, pretty much three times as long to answer the question. Still, we're talking about seconds. So super fast, but a lot slower comparing to Tableau, when we added who's meeting the 50% gross profit goal, then you can build from scratch in Tableau in 16 seconds, while in Power BI, you can do it in 43 seconds. So these benchmarks is what we just went through in our exercise. Let's take a look at some a little bit more elaborate comparisons. This is a what we consider a regular dashboard. It has four different chart types. It has grid, a few measures, a few filters, and both look great on both products. So how long does it take to build from scratch? Just mechanically, the process right there you have the timing to build from scratch. What if you add who's meeting the 50% gross profit goal on both dashboards? How long does it take to build them? Right here you have the benchmark. Now let's do a summary of all of these benchmarks. So the first part of the exercise, the sales by rep, both products were really fast. And actually there's a lot of things that one can do with both products that are really easy and really fast in about the same time. So don't take me wrong. There's a lot of things both products do really well, really efficiently, sometimes one a little bit better than the other. But there's also a lot of things where tableau becomes much more efficient. There's a lot of things in the second step, tableau was about 70% faster. When we add the gross profit goal, it became 62% faster. For the regular dashboard, it was 25% faster. When we added the gross profit goal, it became 39% faster. This is just conveying the point. There is a lot of things in tableau that will become more efficient, and there's actually no place can play a big role in the long run. We actually took a handful of our clients, and we look at what they did in the last couple of years when it comes to ad hoc analysis and authoring, and we look at several different criteria or visualization technique criteria, and we assessed them and we classified them into reports where both products are about the same. Reports where Tableau is about 12% more efficient, 25% more efficient, and 40% more efficient. And we just did a crunch of all the numbers. We extrapolated, looking at, let's say, what if a person is working full time a whole year doing this? And we came up with a figure that says that on average, for these clients, Tableau is about 25% more efficient than Power BI when it comes to ad hoc analysis and authoring. Now, for clients, organizations that are similar to these clients that we assess, what does that mean in the long run? Well, for the people who have to get the job done in about a week, if they're working with tableau, they deliver 40 hours worth of, you know, analytics. If they're using Power BI instead, you're going to be about 10 hours behind because Tableau is 25% faster. So people working, they'll have to work extra hours maybe over the weekend or be behind schedule in the course of a year. For the person with tableau that will work for 12 months worth of analytics, the person doing the same work with Power BI will be 25% behind, which is pretty much three months behind, or working a lot of extra hours to keep up with the job. So that's the perspective from the person who has to do the job. Now, for the person who have to pay for the job using tableau, let's say for every $100,000 worth of investment in labor to get the job done, you get $100,000 worth of analytics. With Power BI, you have to invest $125,000 to get the same amount of work done. So even though in this example or this analysis, Power BI is more affordable from a license standpoint, you might end up investing a lot in labor and sometimes way more than the savings that you're doing in the license cost. Finally, there's the opportunity cost, which is harder to put a number on. And the point is, if your decision makers can make can can have faster answers to their questions, how much they can move the needle and you know, if they don't have to wait until they get to an answer, if they don't have to wing it by not having an answer in the timeless fashion, how much more they can help the business move forward. And if this tool is easier, faster that these decision makers can do the job, how much that can help and with Tableau overall is 25% faster and significantly easier and more intuitive to do the job based on the assessment that we do here. So here we just saw the long term impact from a time to get the job done cost to get the job done and the opportunity cost of having quicker, faster, easy answers in the hands of decision makers. I hope you found this section of the video informative. Thank you.