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Category: Data Visualization

Posts related to data visualization

Why Tableau is Worth Billions: A Case Study on Becoming a Digital “Port City”

It’s appropriate that Tableau is located in Seattle, as they both became popular for similar reasons.

Seattle, started as a logging town shipping lumber down to San Francisco, then hit a big boom during the Klondike gold rush followed by a big shipping boom. It then moved into a big boom in aerospace followed by the growing influence of technology – starting with Microsoft. Access to resources, and a connector between multiple places. Seattle was big on logging, because there was an ocean that made it easier to transport lumber south, with the means to make it accessible and useful. Seattle was big during the Klondike gold rush, because you could take a ship from Seattle over to Alaska, and provided the resources and shipping to get there. Seattle was big into aerospace, because William Boeing got things kicked off here so there was the resources and buyers to set up a shop and build an aerospace business. Seattle then became big into technology, because Bill Gates changed the world with Microsoft Windows and people could come and leverage the resources that created.

Now lets look at Tableau – From wikipedia: “In 2003, Tableau was spun out of Stanford [9] with an eponymous software application. The product queries relational databases, cubes, cloud databases, and spreadsheets and then generates a number of graph types that can be combined into dashboards and shared over a computer network or the internet. The founders moved the company to Seattle, Washington in October, 2003, where it remains headquartered today” 

Tableau then, wasn’t famous because it invented data or created a better way to store data. Rather, the platform made that “digital lumber” we know as data more accessible. It became a way for an average user to reach out into the data space and extract useful information, which they can then use. In effect, Tableau is the digital “port” city for many business owners, that provides access to that raw material and the capability to make it useful.

Becoming a digital port city then, isn’t all about what the platform provides in and of itself but the material it helps you gather / process / leverage. Social media is billions of messages, but Adobe’s Marketing Cloud promises to make quantifiable sense of it all. Server log files are completely useless in and of themselves, but Splunk helps turn all that into a meaningful dashboard.

Lots of tools exist out there, promising to mine assets and turn them into something useful. But as data became a boom, and the trend grew, you could also see the rise of companies like Tableau growing along with that tide. If cities in the 1800’s decided to use clay instead of lumber, perhaps Seattle may never have taken off.

What’s important to note then, is that becoming a digital port city can produce a tremendous amount of value as long as the resource you’re accessing is growing in popularity. However, everything (even data) only stays a popular trend for so long. The hope is, then, that you’ve grown enough to sustain yourself until the next wave takes off and you can successfully adapt along with it. Tableau is in it’s first major boom cycle, as Seattle grew with lumber. As history has shown though, Seattle had many boom and bust cycles as time goes on. How many companies also rise and fall within a single hype cycle (ex: Detroit) ?

Becoming a digital “port city” and staying that way really comes down to 3 things

1. Don’t oversell the hype (to yourselves or your clients)

No matter how on fire your company might be today, every marketing pitch or slogan only has so much gas in the tank. Focusing on the broader industry issue (ex: revenue growth vs access to data) means you’ll continue to stay relevant long after the initial hype has passed. Take advantage of a trend’s popularity, but don’t so closely associate yourself to that one thing that you can’t exist without it – what if Kodak had focused on better living through chemistry vs film? As film declined, chemistry surely didn’t go out of style. And as it turned out, Kodak had some of the most talented chemists in the world working for them because film is a hard thing to make. What would have changed, if Kodak’s brand became focused around something that wouldn’t ever go out of style, vs a single product focus? 

2. You’ll have to think of the post-hype at some point 

Yes, it’s important to stay hyper-focused on your core competency and capability during a big sales cycle, but long term planning focusing on “what do we do when people don’t care about X trend any more” is important. Google will have to figure out ways to make money, after online advertising. Facebook may not be the hot social network 100 years from now it is today, and Microsoft is already starting to evolve in a world that cares less about personal computers. Tableau, too, has the talent and revenue to think about what’s next in the data space long after people stop caring about 2D data visualization in the form of accessible dashboards. Though we have examples, every company has to overcome it’s own culture and leadership challenges to continue to evolve and adapt. 

3. Build a foundation around the longer term trend, while capitalizing on the current hype 

Say you’re Boeing, and you’re contemplating life after airplanes, or perhaps investments that build a platform of services focused on a single brand element of your company. Do you diversify, by extending your reach into other areas of aerospace, or do you step back and say “well, our real purpose is to connect people, so lets invest in other ways to connect people outside of just flying them together”. It’s a tricky question, with no easy answer, which could mean botched acquisitions and a confusing marketing plan if you’re too broadly focused. However, tying in telepresence as part of the “connecting people together” strategy may mean infrastructure investments in aerospace communications networks, that you wouldn’t otherwise make, to allow video chats in airplanes while investing in smaller start-ups that focus on video codecs and compression algorithms that might net you a decent return down the line.

Focusing on just building airplanes though, Boeing would never invest in a Skype, but down the line will it be too entrenched to see a decline in aerospace with the will to shift their focus? Skype would have been a bad idea for Boeing, but what about investments in technologies that make it easier to transmit video which is entirely something they could leverage today? It’s not easy to do, and a lot of companies get it wrong here, but focusing your core message and internal alignment on something bigger than the immediate trend or fad is important, if you want to build a company that’ll be around 30+ years down the road.

If you do those three things successfully, whether you’re a city near the Ocean or a data analysis tool helping unlock value, you’ll no doubt continue to justify the value you bring long after that initial wave has past. It’s why Seattle continues to thrive, whereas cities like Detroit have struggled, and why Tableau is worth billions as a tool that accessed data without developing/ hosting/ managing most of the backend infrastructure that makes up those data systems. Stay beholden to only one path, or one product and you could go from the top of the pile to getting buried by your competitors. Toyota would say it’s not a car company, but a transportation company – because cars are only relevant for a period of time, but people will always need a way to get transported.

Become a digital “port city” by making a useful resource accessible and useful, then focus on continually evolving as the thing people need access to changes.

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3 Ways to Make Data Visualization Useful

Data visualization, the front end of data analysis that makes everything more understandable, interactive, and attractive, is taking off like never before. Many companies have risen to helping establish accessible data tools that are both easy to operate, and understand to where anyone today can point the tool at a data sent in particular and begin developing dashboards, and charts like never before.

However, democratizing data analysis and working towards creating self-service BI can have very negative implications if the discipline and rigor behind that data analysis isn’t handled along with developing all those great dashboards. Furthermore, you can easily find yourself building interesting charts without being able to articulate why those charts are impactful, and what knowledge you’d need to back up if something irregular pops up that someone calls out in a meeting.

People would agree, for the most part, you shouldn’t take someone off the street that isn’t an engineer and have them design & build an airplane. Even if the tools to design an airplane become accessible, engineering is important to make sure the plane flies successfully when it’s actually constructed and launched.

The same should be true for data, though most data isn’t related to life or death situations, there is data misused or incorrectly calculated that can bring a company to it’s knees – from bad sales figures, to bad market analysis resulting in tweets that spin the company into damage control.

With that in mind, I have 3 steps to make your data visualizations more accurate and useful, so your understanding of data can go hand in hand with your energy to leverage it.

1. Understand Data Modeling Fundimentals 

This may sound / seem like overkill, but if you’re going to work with a tool like Tableau or Qlikview, having the basics down around how data works and how to model it effectively means you can go into some data source somewhere, understand it down to the elements themselves, and join that data together in a way that allows for meaningful and accurate analytics.

If you don’t know what an inner vs outer join is, then you’ll have a hard time even pulling together the data into a tool like Tableau without potentially impacting the outcome.

The best book to dive into here is “The Data Warehouse Toolkit” By Ralph Kimball (Kimball is the godfather of dimensional modeling, and is used by most all BI people to develop “cubes” for data analysis). http://www.amazon.com/Data-Warehouse-Toolkit-Definitive-Dimensional/dp/1118530802/ref=sr_1_1?ie=UTF8&qid=1443215078&sr=8-1&keywords=data+warehousing

It’s a hard thing to get through, especially if you want to stay out of the weeds of data management, but it’ll get you deep enough in the fundamentals around good data governance and management, that you’ll be far more effective at building compelling and accurate data models.

2. Focus on Impact vs Interesting

The world is full of interesting data, that would make for all kinds of interesting conversations. However, very little of that translates into impactful data that can make a material impact on a company’s bottom line. Knowing what’s interesting vs impactful can make the difference between a bunch of nice looking visualizations, vs an impacting dashboard that drives business change and makes what you’re doing both useful and practical.

There are a lot of books out there that show examples of data visualization, and the majority are certainly interesting and informative. However, if they don’t have a direct impact on helping change your business in some way, then you might as well frame and hang those pictures on a wall. Develop a clear hypothesis, know what you’re looking to get from the data, and work the problem through to a conclusion.

Data journalism is a great approach towards this, that combines story telling with a clear impact, call to action, or outcome.

A good resource for where to begin is at http://datajournalismhandbook.org/1.0/en/

3. Know your audience

The most important step in making data visualization useful is to know your audience, and tailor the output in a way that makes it the most useful for the consumer. A CFO typically won’t want to see the same visualization as a CMO, and with the majority of data visualization tools allowing for the ability to filter / slice / drill based on the data available in a cube, you can tailor content like never before and peform ad-hoc data analysis with less construction up front required.

Understand what questions your audience might ask ahead of time, and consider how your material is fluid enough to respond in turn. You might be building a dashboard for showing sales nationwide for your company, but what if they ask for one product vs another? Can you build your data model to support that, then add a filter or will you have to go back, work for a week, and bring the specific chart back?

There’s tons of great data points out there, waiting to be discovered and shared via data visualization platforms to help enhance and enlighten business users at all levels of the organization. Make sure though, before jumping into the fray, that you have the foundation, direction, and foresight to develop something meaningful.

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