How can a company engage people with data, beyond simply doing analytics—reaching people who need to use data in their daily life but don’t need to author a dashboard.
Ellie Fields is a Senior Director of Product Development at Tableau Software where she manages strategy and execution of the capabilities that help Tableau customers scale their analytics deployment, including mobile, collaboration, data driven alerting, and more. She leads the “self-service at scale” development team, and has been at the company for more than eight years in a variety of different product roles. We caught up with her while she was on one of her many trips, evangelizing on data and business intelligence.
Solver International: You were with Tableau early on, right?
Ellie Fields: I was with Tableau pretty early, when it was under 100 people, and I’ve seen the company grow a lot.
SI: You have a BS in engineering from Rice and an MBA from The Farm, Stanford Graduate School of Business. When you were there, did they do anything with systems like Tableau? Or was that before the MBA program focused so much on analytics?
Fields: Now they do a little bit with it, but no, the data industry wasn’t there. I’ve been a data geek my whole life and in college I was an engineer. I used a tool called Matlab, which is still around, a wonderful tool but it requires scripting. When I got out of school and went into the finance industry, I used Excel—because everyone wanted to be able to see what was happening. There was a critical collaboration element that kept people from using more sophisticated tools like Matlab.
And then I went to Microsoft. That was after business school and I had to use Cubes. It wasn’t until I got to Tableau that I felt like I had come to how you should work with data, which is this visual, fluid, drag-and-drop, almost touching your data kind of experience.
When I was in business school, Tableau was still very, very young. The way you worked with data was with Excel, and the Business Intelligence (BI) industry thought that a separate group of people would build the dashboards for you. You would put in requests and wait to get a dashboard back, and then tell them what else you wanted or how they could fix it, and they’d send it back to you. You’d have this process, back and forth over a period of weeks, if not months, and finally—maybe—you’d get a dashboard that would be useful to you. Tableau really disrupted that by saying, “Hey, anybody who cares about any data should be able to work with that data.”
SI: Now, we’ve got dashboards and visualization tools, everything seems to be aimed at bringing out the information from the data.
Fields: I would cast it slightly differently. There are a lot of chart tools and ways to visualize your data. I think what’s interesting about data is when you can actually think with data. Sometimes you write a document to think through a subject; sometimes you draw a diagram on a whiteboard. With the right tools, you can actually think with data, too.
So you do more than thinking, “Oh, I have this chart or this dashboard, and it is my output, my answer.” There’s so much data in today’s world and so many ways to look at it, really what you want to do is have an interactive experience, where you can question and answer—maybe by yourself, maybe with others—and you can go through a thinking process with data. That’s what people are seeking when they look for a data tool, a spreadsheet, or business analytics. A lot of times, they end up with a tool that does charts, and they get a bar chart of their sales or what have you, and they think, “Well, that’s nice, but I still haven’t had a chance to ask and answer questions of my data.”
SI: You want to be getting information out of the data, something that you can use, not just looking at numbers.
Fields: Like any thinking process, it’s not always linear. Just looking at the endpoint doesn’t always get you what you want from it.
SI: Modern tools allow collaboration. People can refine their data and look at things from a different point of view, based on what their success points are, and then combine them with input from others on the same team.
Fields: There are a lot of different ways to do that. I think of that as data conversations, and people use Tableau for that all the time, in a variety of different ways, whether it be simply sitting at the same screen or working together remotely. There are salespeople, for example, who have Tableau on a tablet, and they collaborate using data, building a shared data story with their customers. We’re building a feature right now that allows you to collaborate with data right inside a spreadsheet. There’s a set of comments that you can add, and you can snapshot the data as you add those. If you add a comment, I can see what you were looking at, and I can, moreover, go in and interact with the data after that, to continue the thread of thinking or look at it a different way. Data conversations are a real thing. They’re happening now.
One interesting, large-scale social data experiment, is called Makeover Monday that some people in the UK run. I find it fascinating. What they do every week is they pick a data set, and it could be on anything—sports, the environment, politics, anything—and they put that data set out to the community. People take it on Sunday and Monday—Makeover Monday—and create different looks at that data to try and understand it in different ways. You can see the conversation happening. It usually develops on Twitter or on people’s blogs, and they say, “Oh, that’s interesting, you found this after you did that. I looked at it this way, and I found this additional thing.” It’s a way to have different people collaborating on the same data. You see that happening in companies, too, but of course you don’t always get the visibility to it from outside of the company.
SI: Does it matter how large a company is to benefit from this type of a conversation?
Fields: I think any two people could benefit from it. At Tableau, we have independent consultants who use Tableau, and they benefit just by themselves. Our customer base goes from one-person companies all the way up to some of the world’s largest organizations. Again, if you look at it as thinking with data, or talking with data, those concepts are useful at any scale.
SI: Some of the things that our magazine looks at are forecasting, data mining, prescriptive analytics, simulation, risk analysis, and optimization. While there are a lot of software tools for all of this, how does Tableau fit in?
Fields: We think of Tableau as part of an ecosystem of data. There are a lot of new ways to store data; there are also a lot of different ways to analyze data. Tableau is the best tool to “think with data,” to ask and answer questions of your data, and to do that at scale, in an enterprise way, across a big or small organization.
But there are also tools that do very specialized things. For example, R (see Glossary Page 50) is a very widely adopted tool that does statistical analysis really, really well. Python is taking hold, as well. At Tableau, we want to solve most of the common business cases. To do that, we have some forecasting algorithms in the product. For people who are doing hard-core forecasting all day long, they’re likely to adopt a tool like R, and we allow you to reach out to R and to Python, and bring those results into Tableau to visualize them and do some interactive analysis on them.
So really, we take an ecosystem approach. There are some tools that are good for some things, other tools are better for other things. We want to solve the business case use. If most business users need to do things like simple forecasting or data analysis, connecting to different data, we want to be a tool that they can use extensively. But when you get into data science and some of the more advanced practices, we see people bringing their results into Tableau and using our tools cooperatively.
SI: In the 1980s and ’90s, interactivity and integration of software products was the Holy Grail. Everybody wanted to be able to do it, and nobody could figure out how to do it.
Fields: Integrating tools has been hard in the software industry. At Tableau, we use Open Standards, like ODBC to connect to applications where we don’t have a native connection. It’s always hard to integrate enterprise systems, but today there are a lot of good ways to do it, too.
SI: The term business intelligence, BI, is sometimes used rather indiscriminately. Companies are at different levels of maturity in getting their data together to be able to create intelligence and make it available as needed. What is your perspective on that? What industries do you think are the most advanced in BI?
Fields: When I think about analytics maturity, I think about how widely analytics is used in a company. Is it a semi-priesthood of a few people who are allowed to use data? Are there generalized data skills throughout the organization? Do people have access to data, and are they using it in a forward-looking way to make decisions? As opposed to what were our sales last year, it’s how do we grow our sales next year. And those, to me, are the elements of analytic maturity.
A lot of times, you see that in very fast-moving industries. For example, the tech industry tends to change very, very quickly, so there’s a high value for tools that help them navigate that change. That industry is pretty far along in terms of data.
The healthcare industry is trying to use data. They have a lot of restrictions, but there are a lot of healthcare companies trying to use data in a very strategic way. Retail is definitely one of the leaders in terms of using data and being smart about it, and using that data to influence their business, rather than using it as a scorecard. So those are three off the top of my head.
SI: Data is everywhere. It’s generated by almost every type of device that we have, from cell phones to Xbox to your refrigerator. How can you zero in on what you need?
Fields: That’s a question that a lot of people are grappling with now. Machine learning is going to be very important in terms of helping us catch exceptions. For example, you have strings of sensor data coming in. You may not need to handle that data most of the time, but a machine can tell you when things are off by a certain standard deviation, and you can get some automated reporting on that. That’s one way.
A lot of big data just needs to be monitored. The key is to figure out what we need in order to get more answers, and what we just need to monitor. If you’re trying to understand root causes or get into correlations or important patterns, that might be a time when a human being dives into that data and starts looking around and working with the big data. But from an operational perspective, I think the key is to figure out what’s worth watching, and use computers to help us do that.
SI: The IoT, the Internet of Things, is going to generate tons of data that’s just going to be floating around out there. We’re going to have to find a way to figure out what we want to monitor and what we want to deal with.
Fields: Right. And which ones we want to keep. I mean, if you’ve got thermostat data from a building that you own, or your own house, it may be that the last two or three days of data is all you want to keep. You don’t necessarily need to go back years and warehouse all that data. Some of the streaming data is worth keeping around, and some of it might just go back into the ether.
SI: Users have many data tools they use, including Tableau and Excel. Which one is the best? Obviously, you have a bias here—but is there a love-hate relationship between Tableau and the others in the industry, or do you work together?
Fields: We definitely work together with some of them, especially the data science-oriented tools. When people are trying to do advanced analysis, they want an open ecosystem. We do have competitors that we don’t necessarily work with. We are a partner of Microsoft so we both compete with them and partner with them.
When you look at any tool, you need to ask, “What are the things I want to do with this and what am I trying to get out of it.” If you want to do a quick calculation of your grocery costs, maybe you throw that into Excel; but if you can get the data in a systematic way, then maybe Tableau is a better tool because you can connect to the system.
It’s really finding and using the right tool at the right time. Again, what we’re trying to do with Tableau is let business users answer their questions across an enterprise in a very self-service and scalable way. The data scientists, on the other hand, may end up using very sophisticated tools that they create themselves in Python.
SI: Going back to our earlier discussion regarding MBA programs, our audience includes many MBA students and instructors who are teaching analytics to MBA students. They expect to get into business—some of them are in business and taking night school or online classes, for that matter—and they need to know something about data and analytics, but at the same time, they don’t need to be experts at it. What advice would you give them? What’s essential that they need to learn?
Fields: I think they should be looking at three big buckets. The first is, before you start diving in and getting hung up on how many different data sources you have, try to spend a few minutes framing up the questions and what you think the important things are. There is so much data out there, so much you could do with it, that very quickly, you can get lost if you don’t have an intent, a way of working with data. That’s number one: apply good old-fashioned strategic thinking.
The second is getting conversant with some tools and understanding the basic structure of data is helpful. I wouldn’t get too hung up on any one tool or type of analysis for an MBA, but being able to understand data, understand ways that people are analyzing data, the different patterns, and being able to pick up a tool like Tableau and work the data is really powerful. You can answer questions quickly. You can get some insight. You can really get a leg up by understanding your business better.
Then the third thing is to not underestimate creating change with that data. A lot of times, what people say is, “Oh, I did this analysis. I have the answer. Let’s all go do this now.” They forget that a lot of working with data is communication. It is helping people with changes, helping people see things a different way. One thing that can be useful to effect change is to have a conversation based on data––like we were saying earlier, a data conversation. You invite people into the analysis. You let them play with the data, understand the data, look at their own hypotheses, so that you can have a much richer interaction.
It’s a very different model of making change than just saying “I have the answer and I’m going to talk at you until I convince you that I have the answer.” We see entire companies that have adopted this kind of self-service analytics and data conversation approach, and they are really changing their culture to a culture that’s much more curious, in many ways much more egalitarian, because everyone has access to data and has the right to have a theory about the data. You end up with these very rich conversations about data, versus just pre-canned answers.
SI: Companies now can get into data analysis without even having software on site; they have it in the cloud. What is the position of Tableau as far as the cloud is concerned?
Fields: The cloud is clearly important. We have a cloud product in Tableau Server that can sit on cloud services, like AWS (Amazon Web Services). Tableau Online is a fully-hosted cloud service. We’re moving a lot of our analytical capacity from Tableau Desktop into an authoring tool in the cloud, and we connect to a lot of cloud data sources. Our fundamental philosophy, at least at this stage, is that some companies want their systems to be on premise; some want to be in the cloud. Most are on a journey somewhere in between. Some companies say no cloud, totally on premise. They can use Tableau. Some are trying to be 100 percent cloud. Our job is not to tell companies where they should be on their cloud journey; our job is to help them do analytics wherever they are.
We like to think of Tableau as kind of the Switzerland of data. Imagine a company with multiple data sources. Some are cloud, some are not. Some are transitioning to the cloud. With Tableau, you can simply connect to that new cloud data source when it comes online, and you’re still doing analytics in the same familiar tool you were using before. You’re just connected to a new cloud data source. That’s one thing that makes it easy for companies to transition to the cloud, because they can continue to access that data within their Tableau system.
SI: How much does a good analyst need to know about big data, the data sets that are really big, bigger than an SQL database can handle? Is it necessary to have a special tool to work with this, is it something you have to deal with in a different way?
Fields: I think the fundamental concepts are still the same. It depends on the data source, and how it’s ingested, and how clean it is, and how much special handling it needs. From the analyst’s point of view, a lot of times their company or their data team will ingest big data and make it available to them, for example as a data source in Tableau Server. And in that case, there’s not really that much more an analyst needs to know. Clearly, you can find a lot more—usually, in a very large data set, there’s just more to work with. But you don’t necessarily need to approach it differently, if you’ve got your data performing enough for your analysis.
You probably need to adjust your techniques, depending on the size of the data. Working with big data can be a little bit different, but again, a lot of that is in the data pipeline. If that data pipeline is solid, the analyst ought to be able to work with very, very large data. We have business analysts who use Tableau and work with data that’s in Hadoop clusters or in Teradata or in Google BigQuery, and they’re able to work with it quite fluidly, even though it’s very large data.
SI: While a lot of people use Tableau, many haven’t really looked at it or any other data visualization tool. What would you say to the non-users of visualization? Why should they learn data visualization, and specifically Tableau?
Fields: I think you’re hard pressed to find a job these days that doesn’t somehow touch data. Even if you’re working on a warehouse floor, there’s data about how the goods are moving around the warehouse. There’s data about how people move through a store. There’s data about everything. The mission of Tableau is to help people see and understand their data. You don’t necessarily need to be interested in data visualization. You don’t have to be a data geek. You just need to care about your business and care about answers. We see educators, whether it be principals or teachers, working with data about students to try and help their students achieve better. They don’t really care about data. They don’t have to care about data. They just need to use the data to do their job. I think Tableau is the best tool to let you interactively work with your data, reach out to any kind of data you want and have that conversation with your data, without having to be a specialist with it.