How many people own more than one phone? Own two phones? How many people own five phones? Right. Some of these people. There's a lot of phones, right? Now imagine all those applications on your phones. Each of those applications actually collecting data. Now imagine all the applications you have across your other computers and your tablets. There's a lot of data being generated. Now imagine all the devices and all of the different like the Internet of Things. All of your home devices that are sending data. In fact, the amount of data is exploding. Here's a great chart we got from Cisco showing the exponential growth of data. And what's interesting about it is that it's not just data, the amount of data is growing, but the number of data sources is growing too. So it presents a big challenge of how do you manage this data. Or strategically, how can you actually take advantage of this data? Or rather, if your competitors take advantage of this data faster than you where does that leave you in a competitive landscape?
So when we started talking to our customers about how did they actually work with data, it turns out they all go through a common workflow. Everybody needs to be able to collect data, they need to be able to prepare it, they need to be able to do some analysis, answer some business questions. They need to then report those analyses to stakeholders and share them. But what we found is that limitations in any of these stages causes people to use different tools. And it turns out people use many different tools to go through this. And what we find that it's actually because so many people use different tools, it becomes expensive and difficult to manage. Now imagine it like you have your boss asking you for some performance numbers. How well did you perform in the last quarter? Well, how would you go about it? You'd go probably try to get some data, maybe across some different sheets, then you're probably working to put it all together, answer some basic questions about what your growth was.
Putting that into some presentations, and then sharing them around by email. Now what happens if your boss now asks you for new numbers or asks what if you could tweak something? You have to go through the whole process all over again, taking a ton of time. Now what happens if the boss actually says, hey, I'd like to see this on a daily basis. Think about how much work that is. It is actually complex, and we probably do go through this quite frequently. And the issue with that is that what we find then is that there's very limited number of people within an organization who actually have the time to actually go through that. And that leads to these analytics silos where a few people who have access to this data have the skill set and time to be able to work with the data and make actual proper decisions. And so this is really the business challenge of how do we actually empower all users within an organization to make better decisions. So with that, we have two amazing products from Google– Google Sheets, Data Studio.
Our vision with these products here is to make it really easy. Have a single suite of products that makes it easy to go between and work with data. We're trying to make it really efficient to make you more productive. And so ease of use and usability is a big key factor of how we build our products. And we also want to make them very affordable. As you know, Sheets is part of G Suite. With Data Studio, it's completely free. We just announced that it's completely free. We just launched it to over 180 countries, so we really want everybody to take advantage of it. So with that, let's just jump into a Sheets overview. I'd like to introduce Dan. DAN GUNDRUM: Thank you, Nick. As Nick mentioned, my name is Dan. I'm a product manager on the Google Sheets' team. And it looked like from the hands that a lot of you already use Sheets. So that's awesome and great. I'm really excited. Hopefully, in this section, since it is an overview, hopefully I'll still be teaching you some tips and tricks that you might not even know about Sheets today.
But before we get into that, I just wanted to take a quick look. If we can switch back to the slides actually, not the demo yet. I just see that. Great. Perfect. We actually take a quick look back to 2006. This is what Sheets looked like. If you look closely at the screen, you'll probably see that there weren't a lot of features. It was actually still a beta product at the time. It was not an enterprise grade solution in 2006. However, it was in the cloud. Even back in 2006, we were already, the data was already being stored in the cloud. Now fast forward to today, and we have added a lot to sheets over the past few years. And I'll go through some of the features, but it really is a supercharged and powerful productivity tool. Let's go through some of the features. These are just a few of the things that we added over the past few years. For example, scale. Back in 2006, you only could handle like 100 cells of data. Now, we scale to millions and millions of cells of data.
We also have every function you need in order to perform analysis. I listed just a few up there on the screen, but there are hundreds and hundreds. And additionally, there are some really interesting unique functions that are only found in Sheets, and I'll get to those a little bit later. There's also things like publishing, macros, the ability to write custom functions, advance charting. The list goes on. Now, I'd actually call a lot of these things actually table stake features. A lot of the things that a lot of you in the room would probably expect out of an enterprise grade productivity solution. However, Sheets actually has quite a few industry leading unique features in order to do data analysis and help you create these dashboards and reports as well. I'll jump through a few of them. First of all, as I was hinting to before, real time collaboration. Even back in 2006, Sheets was born on the web. We get this really nice. We're all in the cloud. You can collaborate in real time with other people.
You're working on the same file. There's no need to send attachments back and forth or use email. Regardless of the device you're working on as well, you're all just working on the same thing. Because we're in the cloud and because there's real time collaboration, it makes working together much more efficient. However, we know as well that you're not always collaborating. Sometimes you're doing the bulk of your work by yourself. Sheets also has some really interesting and unique features for that as well. For example, add-ons. Add-ons if you're not familiar are secure third-party features that run inside of Sheets that can extend its functionality. They can do things like automate workflows to make your business run more efficiently. Or you can pull in data from other sources. In the screenshot, you actually see on the left an add-on called Supermetrics that can pull from Facebook and Twitter and Google Analytics and a variety of sources. And because it's the cloud, it always stays up to date.
If you're working with financial data, we have the Google Finance function. That also updates regularly. There's a whole class of functions, and I'll show one a little bit more later as well. There's a whole class of functions in this bucket that can pull data from other sources in your sheet and keep it up to date. And then if you want to analyze the data or see the trends, we have other functions like the Sparkline function, which can put a miniature in-cell cell chart of data, so you can see the direction, see trends, see how it's going. So you've either brought now your data in through an add-on, you've imported it, you've used one of these unique functions. It's now time to analyze it, prepare it, going through the steps that Nick was mentioning earlier. And that's where a feature called Explore comes in. Over the last year in Sheets, we added a feature called Explore that brings machine intelligence into the product. So what do I mean by machine intelligence?
I'm basically referring to Google's smart algorithms that extract trends and find insights in your data for you to help you out. For example, Explore will suggest charts that help make sense of the data you just pulled in. Or they'll show you little key snippets and insights to help extract outliers or interesting information. Or just recently announced a few months ago, you can just ask Explore a question in plain English. You don't have to write a formula or a function or do code. Just ask. We really think this makes data analysis much easier, simpler, and faster. This brings analysis to all users inside of a company. It's directly in a spreadsheet. This is what people, as you all were saying before, you're all using spreadsheets. And so this is bringing sophisticated analysis directly to you in the place you're already using. There's no other spreadsheet tool that has features like this. There are products. You might have to use separate complex, expensive tools to get features like this.
But we include it all together in G Suite. So with that, now we'll switch over to the computer. I'll give you a quick live demo. I've mentioned a lot, so I'll basically now show you everything I was mentioning. We'll start out with one of the formulas. Like I said, there's a whole class of import formulas. I'll start with– as I type in, we'll see auto complete of some of them. I'm going to choose the import range function. This allows me to import data from another spreadsheet. For example, if information was shared with you in another spreadsheet, you can pull it in. So I'll just switch over to this other tab, grab the URL. This is the data I want to pull. I'll put that in there. And then use just specify the sheet name as well. So it's on Sheet 1. And it'll come in in just a second. Perfect. So now the data that was in the other spreadsheet is now pulled into this spreadsheet. And as the data in the other spreadsheet changes, this one also stays up to sync, because it's a formula.
It's linked. I could have just copied and pasted, but then it wouldn't have stayed up to date. So this keeps the data fresh and up to date from another source in a spreadsheet. All right. So now the data is in. Let's see what Google can help me with. I could go through and do an analysis. But as I was mentioning before, in the bottom corner, we have Explore. Let me just open that up and see what it tells me that I can do. First of all, one of the first things I see is that it's suggesting some formatting suggestions to me. This will make it easier to read. So let me just click that button, and in one click, I'm able to more easily read the data in this spreadsheet. It automatically found the header row. And it automatically found the bounds of the table. It automatically cut it off, so I didn't have to select the data even. Explore just knew where to go with that. All right. So now I have some formatting. Now I want to start to understand this data little better.
Make sense of it. Let's ask Sheets questions. Like I said before, you don't need to write formulas in order to do analysis. You see some examples here that can get you started. I'll just type in a few of my own. For example, let's just actually type, what's the average profit margin? And as you see, autocomplete can help me make sure I'm not making typos and mistakes and things like that. Makes that a little faster to enter. And right there, without having to write a formula or anything, I see that the average profit margin in this sales data is about 22%. So I just did that really quickly. I didn't need to know any special spreadsheet formulas. If I'm not an expert or anything, I was able to do that seamlessly. Let's try something a little bit harder though. Like that was an average. Maybe most of you in this room, if you're familiar with Sheets, know an average. Let me ask something like what was the top selling item on Black Friday? They were talking about Black Friday earlier in the keynote, so I thought that was interesting.
And right there, I press Enter, and I get the answer returned to me is a jacket. We sold 20 of them on Black Friday in my small business. Now, let me see, let me show you this formula. That's what you would have had to have known in order to get that data from this spreadsheet. And if you also notice in our little answer here, we saw that Black Friday was November 25 of 2016. I didn't have to go to Google. I didn't have to look this up. I just asked Sheets, and using Google's knowledge, we just knew that date. We found that in the spreadsheet, and we provided the answer to you. It was seamless, easy, efficient, and I did it in basically five seconds. Not having to write that formula, probably would have taken me quite a bit of time even as a Sheets expert. All right. So now I'm starting to understand this data little bit better. I'll show you some of the other things Explore offers. First of all, I'll just pull in some charts. For example, I'm seeing that Explore's recommending this chart that has an interesting pattern to it.
The cost of the goods versus profit. And I can just simply drag that in if I wanted to start developing a dashboard or a report directly in Sheets. And it's showing this nice trend as the costs go up, my profit goes up as well. The dollar amounts. And you can see some of the other insights and a bunch of other charts. For example, these are the types of items I'm selling. I can pull that in as well. And I can continue to go down as well. For example, the manufacturers. Explore is finding a lot of these insights for me. And so I'm just inserted several different charts using different data ranges in my data, and I just drag them in. Explore found these interesting things, and I've been basically setting up this dashboard or report in a matter of seconds. All right. So now we can switch back to the slides. Basically, to summarize, I just went through the first half of this flow. I collected data from another spreadsheet. I started analyzing and preparing it. I could have inserted some formulas.
I started inserting some charts and stuff like that. Nick will come up in a few seconds and also take you through the second half of this of how Data Studio, you can start using this data that we used in Sheets, we started analyzing a little bit more, and then generate some of these reports and share them out. That was a quick, like 10 to 15 minute overview of Sheets. However, if you do want more information on Sheets, we have a whole other session later today at 4:00 PM. Check that out. We're going to go even deeper into Sheets. It's going to be about an hour long session. Highly recommend that. And if there's not enough time for questions at the end, we also have demo booths and Q&A sections that's in Moscone Center. That's running all week, so we can answer any questions you have about Data Studio and Sheets. And with that, I'll hand it off back to Nick who will go over Data Studio. NICK MIHAILOVSKI: There it is. Thanks a lot, Dan. So let's talk a little bit about Data Studio.
Dan just talked you through about how we can collect data, how we can prepare, how we can analyze it. Let's talk about how we can actually do some reporting and dashboarding on it. How we can collaborate with our peers. So for people who haven't known, Google Data Studio is actually a relatively new product. We launched less than a year ago. May last year. Since our launch, we made the product 100% free. And just yesterday, we announced our global availability, so now the product's available in 180 countries. So everybody can use it right away. Go ahead and use it. I'll be doing a demo. You can follow along in the demo as well. We also just launched a new data import feature where you can import data for two gigabytes completely for free for everybody. We actually store the data on Google Cloud Storage, so it's a really nice feature that we just added. Now, I really want to get into a demo. But before that, I want to highlight some of our unique capabilities of the product in case you don't know them.
The core aspect of Data Studio is really to communicate information. And to do that, we want to make you a hero when you use our tools, and we really strived hard to make it easy to build beautiful reports. We have a very easy WYSIWYG editor to help build out these reports. Tons of styles. Tons of charting. We can make them completely interactive so consumers or viewers of these reports can actually explore them themselves. And you can actually make these multiple pages. You can actually curate a story around your data. Getting data into the product is really easy. We've built many different connectors to popular Google products. We also have connectors to on premise data, including MySQL and Postgres. And again, we just launched our CSV import for two gigs where you can import data directly to Google and start visualizing your data. Once you get your data into the product, we also have some ad hoc transformations that we allow you to do. You can do quick aggregations across your data.
I'll show you some of how that works. You can do ad hoc groupings and also calculations. So it becomes quite powerful as a BI solution. And finally, the last part is we've actually really love how G Suite, how simple it is to share. And so we've brought that sharing technology to your data through Data Studio. And so with Google Data Studio, you can share your reports with individual users, you can share them with Google Groups, you can make them completely public. All these files actually show up on Google Drive as a first class experience. And you can collaborate in real time, which is really cool. And I'll show you a demo how to do that. So enough talking. Let me jump right into the demo. If we can go over to the computer, please. Cool. So let's just jump right in. I'm going to go to datastudio.google.com. If you guys have a computer, you can jump along as well. So here's all the reports I have access to. I'm going to go ahead and create a new report.
I'll go ahead and click the plus button here. And the first thing I need to do is to create a new data source. So I'll click Create a New Data Source. Now, here's all the different connectors that we offer, again, to a lot of Google products, but we're adding a lot of connectors as well. For this demo, I'm going to connect to Google Sheets. And what I'm going to do is connect to that Black Friday report. Whoops. Cool. And I'll connect to the first and connect it. OK. So what we've quickly done is we've connected to the sheet. We actually pool all the different columns as what we call a field, and then we associate that with some conjectural information. For example, these types. We've automatically inferred what the data is here, and then we've given some types to some of these other columns. So if some of these here, for example, the unit price is a number. I can go ahead and model this out so that way it's a currency. So let me put that in to price is currency.
Margin here. Make that currency as well. Cost of goods. We can put that also as a currency. And you can see that we've had some aggregations here happen out of the box. So for example, unit price will be summing by default, and profit margin will be summing as well. So I'll use that. I'll go ahead and add this to my report. And now we're back into the WYSIWYG editor. So to start visualizing data, all I need to do is come in here to this tool panel at the top. I'll use a bar chart. I'll select it, and I'll just draw it right here on the canvas. And just like that we're visualizing data. It was that simple. It was less than a minute. Less than two minutes. And what we're doing here is we're showing the metric here, serial number. What we can do is now go into our unit price or let's say our profit margin. That was really interesting. And what we'll do is we're aggregating. We're pulling out all the different pants and we're summing what the margin was across all the different pants.
And we're summarizing that here right in this chart. So this is really nice. I can quickly come in here and start visualizing data. Everything here is live data. So if I Control C, Control V, I'll actually make a copy of this, and I could bring it down right here. So here we're looking at the profit margin at the top. I could actually change my metric to look at the number of units sold. We could see, of course, the number pants corresponds to the number of units sold as well. If I looked at unit price, we could see the jackets are a lot more expensive. It has the highest cost whereas the pants have a much lower cost. So again, we've just provided a very simple way of allowing people to start exploring visually how the data works. OK. So let me just go here to unit price. The other thing we could do with this report is we can actually add a nice little title on here. So let's just say, let's just make this a little bit bigger. 32. Black Friday Sales. And let's make this interactive.
So let's add a little filter control on here. So we can go ahead and add filter control. And with the filter control, there's a bunch of different things we can do. For this one, let's just make this expandable. And on the data, instead of using the item let's use the manufacturer. Cool. So now if I go into View mode, what you'll notice is that everything here is interactive. And when I click on the manufacturer, I can see all the different manufacturers and I could actually now interactively start exploring through this curated experience I just built. Everything is super easy to use. It's all drag and drop. It's all web based. Super easy. So I want to make this report look a little bit prettier. But I don't have a lot of time, so what I'm going to do is I want to collaborate with my colleague, Dave. So the first thing I want to do is share this report. So as I mentioned, we've integrated directly with Drive here. So what I'll do here is add Dave.
And hopefully if the internet works, I can send him an invite request. So what that did is it actually sent an email over to Dave who's sitting in the audience. It should come in his inbox, and it should be a link. And so Dave, if you can click the link. Hopefully, you got it. Did you get it? All right. He got it. Here he is. There he is. There's Dave. So Dave's now into this collaborative report. And if I go now into Edit mode, I can actually see Dave here in the report. So Dave, why don't you go ahead and style this. Maybe I'll. So here, look. Hands free. I'm building a report, and what we've done here is integrate with real time collaboration. And the nice aspect of this is next time when you're in a meeting, and you're working with your boss has a question or you want to update your slides, you can now actually update these reports live in real time, and it's all live data. So here, Dave, maybe let's change a dimension from manufacturer to something else in the drop down.
Cool. So now it's on Item. Now, if I go into View mode, notice how everything is wired up. And now it's all dynamic, and I can actually do on the fly filtering, what Jay just updated in real time. So again, super easy feature. Super powerful feature. And very easy to start building these interactive experiences that you can share. We know starting with a blank report is very difficult for many people. So we've built a bunch of custom templates, and we're adding a lot of work to help make it easy get started. What I'm going to do is show you a few of them just to show you what's possible that you can create. So this one was a report we created for a health care company. We branded it. We added a logo. We styled it blue, because health care is blue. And you can notice how pretty it is. Here we have showing off some of the visualizations, showing what you can do in terms of customizing this experience. And again, everything here is interactive. If I change it to the last 10 days, then hit Apply.
Notice how everything is updating here live in real time. So again, powerful tool to make these beautiful reports. Here's another one that we did. We themed this one black. If you are running advertising, you can use this report to look at your own data. And there's a bunch of other templates. Cool. So that's a quick overview of Data Studio. Just the tip of the iceberg. Definitely invite you to go and try it out yourself. As you saw how easy it was for me to create a report, should be easy for you to create a report as well. If we could jump back to the slides. So the big thing here that we really want you to take away is that we have a lot of powerful tools. But for you guys, we really want to make them easy so you can be more productive, and we're offerings these tools to you at a very competitive price point, which is free. And through G Suite as well if your company is buying it as well. So definitely, next time you're thinking about how do I work with data, definitely look at the solutions that are available and consider Sheets and Data Studio for your next project.
So I've just talked a lot about why we think our tools are great. But what's even better is to have our partners come on stage and talk about how they are able to actually use them within their own business. So I'd like to introduce Jeremy up stage to talk about how he's using it at Analytics Pros. JEREMY ECHOLS: Cool. So I'm Jeremy Echols. I'm the manager of analytics, growth, and development at Analytics Pros. And we're a digital analytics consultancy. And as such, we get the privilege of working with a lot of really great clients and a lot of really great companies. And at the end of the day, what we're seeking to do is actually solving real problems that they're running into day to day with their data. One example of that we're going to walk through here is with Food Lion. And Food Lion's a grocery store chain within the southeast United States. And with that, as we've talked about today with data, we run into lots of issues of lots of individuals are working with data.
There's lots of systems that that data is housed in, and that creates a lot of complications. And then with Food Lion, we actually run into even bigger complications in the fact that we're not just dealing with individuals and systems, but we're also dealing with lots of different agencies, organizations, and vendors. And so I want to talk through how we've actually sought to simplify a lot of that using the solutions of Google Sheets as well as Data Studio. So as we started working with Food Lion, there were a few things that we ran into that we knew we had areas of disconnect in how we worked with their data. The first area is with social data. With social data, with Food Lion, like many different companies, they're on many different social platforms. And so that was the first thing that we had. We had different data sets, different user interfaces for their social data. On top of that, they used different organizations and different vendors for their CPC campaigns as well as on top of that for their social communication in general.
The next area that I'll talk about is going to be in-store coupon activity. This is a third party vendor that they use for customers that need to activate coupons for their loyalty card as well as redeem them in-store. And all of that data is actually hosted in that third party vendor system with limited access. And then finally, I'm going to talk about connecting online to in-store behavior. And what that really is that Food Lion has a website that doesn't actually sell anything. And so what really matters at the end of the day are are their customers actually going to in-store and how does the website activity influence that. So we needed to join data from multiple systems. So touching the first one of social data. Really what we sought to do here was actually moving all of their data from multiple systems into one system. So you see here that their primary areas are looking at Facebook, Twitter, and Pinterest. And so with that there's a lot of different datasets and a lot of different interfaces that we're working with.
And so we were able to utilize the previously mentioned add-on Supermetrics in order to take the powerful feature that Sheets has and plug-in an add-on to connect to the APIs for Facebook, the APIs for Twitter, as well as Pinterest. And from that we were able to actually put all of that data into an automatically updated sheet within Google Sheets to have all raw data into one place. So now all of these teams weren't having to actually go get the original data from each of those interfaces. And then beyond that we then were able to clean it up, put it into a schema that Data Studio could have read as a data source in order to have beautiful visual reports that actually matched the branding for Food Lion instead of just using the interface of, say, Facebook Insights. And so how we were able to do that is we were able to move just from having the sheets and the raw data, which is super helpful to have in one place, but move from that by having the powerful features of the formulas that exist inside of Sheets.
And so as you see there on the left, we had a very complex formula that would automatically update based on what day it was to always look back into a clean dataset looking at last month's social data. And from this, we were able to combine a lot of datasets that actually weren't possible within the Facebook Insights interface. And so here we have the example of how we were able to show them the Facebook post engagement– likes, comments, and shares that were taking place– broken out by day of week as well as by post type. And so what this allowed was actually a much faster way to get insights for their data that actually led to action. They were able to take that information, know what day of week their audience was active on Facebook, as well as what type of posts would actually get the most amount of response. The next area is going to be the in-store coupon activity area. So the way that this works since this was a third party vendor working with their data and just sending it back out because there was limited access to that raw data, they were actually just emailing a spreadsheet file every month to the main point of contact at Food Lion.
And then from that throughout the month as people needed access to that data for coupon activations and redemptions, they were actually just emailing it to the team throughout the month. This creates a lot of complexity, and a lot of problems that can come about from this. Because by the end of the month, you might even have adjustments that were made to the file and people are looking at different sets of data as a result. So what we were able to do was to work with that vendor along with Food Lion and use Google Sheets as a central point. So no longer we're having the inefficiencies of emailing files around in order to get that type of information collaboratively. And then from there, we were actually able to take them from people having different versions of a spreadsheet file that had been emailed to them, and actually put it into Data Studio in order to have now interactive reports. Instead of just having a flat spreadsheet that has multiple different forms throughout the month, we were actually able to show this.
And here's the example we used of a scatterplot inside of Data Studio in order to show the relationship of coupon redemptions compared to coupon activations. So a shopper puts a coupon onto their loyalty card, and does it actually result for that product category, does it result in an in-store purchase? Because at the end of the day, what they need to know is what type of product categories are actually driving users to not just be interested in having the coupon on their card, but actually redeeming it in-store and causing the in-store store behavior. And then the third area is connecting online to in-store. So the way that we did this when we first started working with Food Lion, we created a schema within their dataset for Google Analytics that whenever a user logs in online, we actually collected that user's MVP loyalty identification number. And we put that into a custom dimension into Google Analytics. And what that allowed was for us to actually, every time we needed to extract that data to know what users were logged in and were active on Food Lion's website, we were able to export that out to Sheets for Food Lion's business intelligence team to work with, and to know what type of data they were working with, and what type of users they had.
And so what Food Lion's business intelligence team was able to do in the collaborative setting like Google Sheets is they were able to actually take all of those user IDs to know who they were and how many sessions each of those users had. And then from there, actually understand what categories do those users fall into. And they could compare and contrast all of the data that was coming in from their transaction database on the back end compared to their Google Analytics interface. And so with that, they were able to see users that are existing to the website, meaning they've actually been logged in, and been using the website. Users that were new to the website that had just started using it this month. As well as users who were not actually active on the website at all. And immediately this caused an impact inside of Food Lion's ecosystem of data to be able to say users who were actually engaged with the website and had previously been engaged with the website are actually spending more money in-store per month, which is a huge benefit to get insight into that because as a website that doesn't actually sell anything what it relates and what it connects to in-store is actually what matters.
So finally, to summarize, the impact that we've seen of combining Google Sheets and Data Studio together has actually been just simpler, faster reporting. We're able to get insights much quicker. We're able to move in a much more agile fashion. We have a faster, more efficient reporting workflow. We've actually heard back from Food Lion that they're saving about 25 hours every month that they were spending creating these reports. They've reduced that workflow time, so that actually helps their internal process quite a bit. We've moved from multiple platforms into one. We see this especially with their team of social that we have logging into Facebook Insights, logging into Twitter Analytics, and being able to pull that information down. We've actually moved this into Sheets and Data Studio for them to get all that information. We moved from multiple reports into one. We're no longer sending spreadsheets around. We're no longer having these different formats.
We have one multi-page report inside of Data Studio that actually is giving their executive team a high level view of everything going on in their company. We have viewers that can interact with data like they couldn't before. Previously, especially when we talk about spreadsheet files, you had to give the raw dataset every time that you worked with people. But because of the data governance inside of Data Studio, we could have viewers of that report that actually could get insights from that report without needing access to the data source itself, which was very powerful for their situation. And then finally, insights can be found faster. At the end of the day, if we're not actually finding insights in our data and taking action on them then we're kind of just spinning our wheels. And so what we want to see at the end of the day is for users to come in, get insights, understand what social posts are working, understand what coupon product categories are actually causing user behaviors that we want to see in stores.
And so we're seeing that happen with a combination of Google Sheets and Data Studio. So next, I'll have Andy from Whirlpool come up and talk about how they're using these solutions. ANDY LEWIS: All right. Hi folks. Wow, that is loud. My name's Andy Lewis. I'm from Whirlpool corporation. I lead a team at Whirlpool called the Winning Workplace. And what that team is is it was a purpose built team. In fact, it was purpose built because we were the Google Go Live team in the beginning. But it was purpose built specifically to drive the adoption and transformation of the Google tools at Whirlpool. We wanted to make sure that we were getting the most out of what we were doing, and collaboration was really the most important part of this. The reason that we went with Google in the first place was actually because we wanted to drive a culture change at Whirlpool. Now, when I talk about culture I'm really not talking about our heritage. We've got a very strong heritage.
We're extremely proud of our heritage at Whirlpool. We've been around for 100 or so years. Iconic brands like KitchenAid and Maytag. What I'm really talking about is our behaviors, and how are we collaborating with folks, and how fast are we to decision making. Who do we involve in the different discussions that we're having? How siloed are how, and easy is it to get to information? And so with that, the only tool that we felt that we could accomplish this with was just G Suite and Google. With that, we also knew that it wasn't going to be a quick flip of the switch, and it was just going to happen, that we needed to kind of shepherd that through. So a number of things that we did. The first one was actually we looked at our environment. And it was our physical space, where we were sitting. Were we sitting next to the folks we needed to sit to. We have an open office environment, and we felt that we could do better with that. And so Winning Workplace started out with our real estate team first and foremost and actually cleaned up that environment.
We're refreshing all of our buildings. We have a beautiful new North American headquarters, and we got people into a physical space that actually helped with collaboration. On top of that, when we looked at our virtual space, we've got employees all over the globe. 100,000 employees in a number of different regions. And how could we help enable them to collaborate across distances the same way that we did with the physical environment and collaborating within the buildings? So with that, we now have G Suite, and we put that in. We wanted to make sure that we were actually getting collaboration appropriately. So some of the questions that we had, and one in here at the bottom is for today. But some of the questions we had was, first of all, what is successful collaboration? Just because there's a number of people that we're sharing with and they're in the different documents that doesn't mean that we're actually collaborating. The second one is how are we being effective as a team?
And this is my team. Are we being effective in the efforts that we're doing to try and help folks to collaborate and to get better at this in G Suite adoption and transformation? And then the third one today is why did we use Data Studio to take a look at all of this and put it all together. So for us, collaboration, there was a really easy indicator that we could look at for collaboration, and it was in the Hangouts tool. Hangouts for us was a real easy one, because it means every time you've got somebody using a Hangout, you've got somebody that's face-to-face that's not sitting next to each other. So that means that, yeah, we are enabling folks to work across distances appropriately. So we could even look at this one-to-one and say, OK, every time we're using Hangouts, we're getting value out of this, and we're helping folks to get their work done. The things that we looked at, if you take a look at some of the slides up here, we do look at our weekly usage.
So how many Hangouts are happening on a day-to-day basis. We also want to look at not just that, but then how many actual meetings are being done by Hangouts. So not just the individual people, but the actual individual meetings themselves that we know are being conducted by Hangout. One of the interesting metrics that we had was when we turned Hangouts on in 2015 in January, after a year, in 2016, we were running at about 1,000 Hangouts per day. So that's not individual Hangouts, but 1,000 meetings were actually being held over Hangout per day. This year at that time, we're now up to about 3,000 meetings per day on Hangouts. So just exponential take off of this. But without being able to look at the actual data behind it, we would have no idea. So the trending is good. But the other thing that we did was we invested in Chromebox for meetings. And if you don't know what those are, those are the Hangout rooms, so you can install them into a physical location, tie them into your calendar, and then walk in, and with one click, start a meeting up, and get a Hangout going.
Those have helped with adoption tremendously, because now it's very easy for people in different locations, especially in our manufacturing plants, we've got those scattered all over the globe, so we now can connect to those places like we never had been able to before. The way that we put this dashboard together, and this is all in Data Studio, is actually we pulled an APIs out of Google. And this is a unique solution using both Data Studio and Google Sheets. So we pulled the APIs in right into Google Sheets so that we could take a look at all of that raw data in Sheets. Well, in that format, it's not really that easy to take a look at for us, because there's so much of it, and it didn't answer the questions immediately what we had. So then what we did was we pumped it all into Data Studio. So now we've got these really big robust dashboards, and then we can put those URLs out and give those to our leadership and they can take a look and see how we're performing with Hangouts.
The second one, though, is that these Hangout rooms that we had put in, these Chromebox for meetings, the neat part about this was that we just had a Google sheet in there, and we manually update that every time we add another room to the inventory, and then it pulls it back in. And now we've got a dashboard that is coming through a couple of different locations. But I have one place that I can send leadership to and say here's how we're adopting and yes, we are getting the most out of our investment. So the second question for us was how are we with our personal– the Winning Workplace, training and embedment– and are we really helping folks to get better at Google Tools? So one of the things that we do is we go out and we have these sessions. We call them immersion days. And we have folks out there, we ask them to take three to four hours out of their day, and we bring departments on all at once. And then we work with them to give them some help on just what's out. Sheets, Slides, Docs.
How to share. How to use Drive better. The thing is it's a pretty big investment for those folks, and it's an investment for our team. And we want to make sure that we're actually helping them by going through all of this training. Because you've all been through training classes that you've sat through the training and gone, well, there's three hours of my life shot. So we're hoping to avoid that. So what we're looking at here is a couple of things. One is we want to, obviously, we want to see the Google tools, the usage increase. We also want to see the other tools that we're using go away, some of the competitor tools and other things in this space. So we're tracking both of those. But the important part of this is that we're also tracking the folks and how they're feeling about the tools. So we have surveys that go out before each session and ask people so what is your self-proficiency in Slides, Sheets, Docs, Gmail. And then afterwards, we ask them the same thing and see if we've helped them with their proficiency.
So the way that we put this together was we pull all of our Google data. We actually put this into BigQuery. So it's a different dataset than the Google Sheets. But we put this into BigQuery and then we also put in a bunch of data on the other tools that we're using as well, and tie that all together into a few different datasets and BigQuery. Then we actually put in a hierarchy data too, so we can actually put in all of the people and understand where they roll up in the organization by department and by senior leader. And then we can take a look at that information and understand, OK, this department is doing really well. And this department needs some help. So that's the data flow on that top to get us to the dashboards. But on the bottom one, what we've done is we've used Google Forms, and we send the self-assessment questionnaire out in a Google form. That information flows into Google Sheets. We take some of that data, we put it into the Data Studio to use for analysis.
But some of that we leave right in Google Sheets, and we just constantly update it. And then now what that does is it gives us a little bit of a tie-in and a context to, OK, we just met with this organization. They didn't feel really good beforehand. We did all the training. We're seeing the usage spike, and now afterwards they're feeling great. That's what you're hoping for. What we actually have found out by building this solution out is we will go into an organization, say they need some help. They're not feeling too good. They're not very confident in the tools. But boy, they're using it. So that means that the team is out there actually using the tool for a lot of their day-to-day work. But they still don't feel competent at it. So either we need to help them with a little bit of training or they're struggling. And it's just an analysis we never would have gotten to if we hadn't put all these things together in the tools in the way that we did.
So with that, I believe I hand it off to Dan. DAN GUNDRUM: Thank you, Andy and Jeremy as well. So you first saw us talk to you about Google Sheets, Data Studio, why we think it works well for companies. But also a few of our partners too. From Analytics Pros and Whirlpool on how they're already using these tools in small and large companies to really make an impact, get some useful insights, and make important business decisions. Basically, to just to quickly summarize, what you've seen is a lot of this was really easy to get set up. There's seamless integration between products. Things update easily and efficiently. It's live updating. Everything's stored in the cloud. You saw me pull in data from another spreadsheet. Nick was able to take that exact same data source and start piping it into Data Studio. And then you saw the partners do basically the same thing with their use cases as well. And again, I just want to reiterate this is all available already in G Suite.
And as Nick was referring to, it's Data Studio's completely free as well. So looking ahead too to hint of things coming. Nick mentioned just yesterday they had a big announcement. Data Studio is basically going across the world. All of us are working with people in other countries in different locations, and so Data Studio is now from 20 countries all the way up to 180 and probably growing, right? You can expect tighter integrations between products in the future, so look forward to that. And several more powerful enterprise ready features. I think the one thing to really take away is that we're super committed to the enterprise. And we really do want to add more powerful features to help you perform analysis and to make your businesses run more efficiently and more smoothly. So with that, here are a few links to check out. If you do want more information on where G Suite is going check out that first link g.co/cloudconnect. This actually lists a few features in our roadmap coming up.
So things that we haven't necessarily launched yet, but we will be announcing and delivering soon. Stay tuned to also our G Suite updates blog. If you're not familiar with it, that lists the things that have launched. And check out our Help Center for more information if you want to learn more about the connectors inside of Data Studio and connecting to Sheets and making sure that happens. And of course, check out our partners and the links there. I'll also plug another session. I believe it's later in this week. I think it's on Friday. A whole session devoted to our roadmaps. So if you are interested on that, check that out too. It's in the schedule page. [MUSIC PLAYING]
The proliferation of data has resulted in heightened demand for speedy access, analysis and visualization of data. In this video, you’ll learn how to use Google Data Studio and Sheets to satiate the hunger for data in today’s enterprise.
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