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Tag: analytics

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 Reasons Why You Need a Chief Analytics Officer

Data has exploded in a way that rivals mobile’s explosion ten years ago. Everyone is out there buying masters degrees, data visualization licenses, and data scientists by the truck loads in a way that mimics corporations buying mac laptops, mobile developers, and app store branding when iPhones blew up the smart phone space.

The Analytics ‘Trend’ Isn’t New

There are a lot of great things taking place right now with all the interest around data analysis, but the funny thing is that data analysis is nothing new (neither is data science). There’s a good 30-40 years of work on data, from data architecture to database administration (not to mention the millions of excel spreadsheets that corporations are running critical business functions on) that live inside companies and create a legacy layer that this latest wave of data analysis is building on.

Other new trends, such as big data analysis and the cloud computing revolution, have further spurred companies to consider ways to extract usefulness from their existing data and move away from churn or ARPU and develop distinctly competitive analysis with phrases like “regression analysis” and “predictive analytics” becoming much more common in corporate board rooms.

Translating Data

The big problem is, as was the case with mobile, is that you have to be able to translate interesting technology into impacting ROI-laden investments that drive top or bottom line revenues (or create efficiency and lower costs of course, as well). There’s a good deal of buzz around big data being an overused term, and a hundreds of millions of dollars spent on visualization tools will, at some point, taper off when the average business user turned dashboard builder runs out of things to visualize due to saturation, bad data, etc.

So Who / What Is This Chief Analytics Officer?

A Chief Analytics Officer could be a Director of Data, or a VP of Analytics, but having someone at an executive level that can drive a centralized data strategy for the company should exist for these three reasons.

  1. Centralizing Your Data Resources Will Help Avoid Silo’ed Capabilities

To turn all this hype into profit, it means building a centralized capacity. A capacity which sites outside of the IT-to-business politics and hype to buy visualization tools, and instead focusing on building a stack of capabilities, from the data lake to the dashboards, geared around revenue generating use cases taken from business partners who need more usefulness from their data without having to build silo’ed data science teams that rely on fractured data sets.

When anything is this pumped up, every department is going to want to get involved and build capabilities, since every business group uses data in some form or another. The problem is that it takes a variety of experiences and backgrounds, along with investments, that need to be built at a corporate level with a plan to centralize some capabilities and decentralize others with a clear data strategy that everyone can get behind.

Centralizing this capability means one strategy, one leader, and limitless opportunities for everyone to participate without each department deciding their own game plan for riding this data wave.

  1. Consolidating data to maximize usefulness, while aligning that effort under a single leader

The topics around big data, and data lakes are growing overwhelming, with more and more companies working to consolidate all their data in one place to allow for both advanced analytics & traditional business intelligence functions. At the same time, a data lake built in the wrong way can cause latency along with too many executive peers building extensive requirements which ultimately brings any progress to a halt.

Bringing your data consolidation effort under a single leader, tied to a data strategy that brings the bigger outcomes into focus and alignment while leaving the smaller day to day details up to a single org unit means your company can spend less time planning & debating, and more time driving value from your data lake.

  1. Impact is prioritized, over ‘interesting trends’

Much like the millions of dollars spent on corporate mobile apps that never got traction, companies today are spending millions of dollars on real time streaming, data visualization, and corporate education on DAX programming all in an attempt to capitalize on the data analytics hype and create a stronger bottom and/or top line revenue stream through the use of data analysis.

The thing is, data isn’t a new domain for technology, nor is investing in Big data going to revolutionize your company.

There’s a good deal of effort being spent on building impressive looking visuals, which add no incremental value over the same data displayed in an excel chart. Furthermore, companies investing in hiring legions of data scientists without clear revenue-driving hypothesis will find they spend a good deal of time figuring out just what to focus on.

As is the case with any over-hyped technology, whether it’s enterprise wide tableau licensing or infrastructure to support web traffic analysis for real time personalization, the tools are only as good as the capabilities on the team and the business cases they are actively working towards.

Focusing on a single leadership structure to come up with the real tangible value for investment in data analytics means there’s a common set of goals that’s driving the spend, and a clear idea of what each department and employee is focusing on.

It’s not so much that a single team owns every analyst, but rather each instrument is calibrated so the whole company sounds like a beautiful concerto vs a number of instruments playing at different rhythms.

Furthermore, when it comes to the vendor onslaught and procurement nightmares that naturally arise in the midst of a technology boom, there’s a clear investment strategy for how the company plans to leverage capabilities such as big data or advanced analytics. This can influence everything from recruiting and training, to infrastructure and software licensing, and help ensure each investment is additive vs expensive and lacking in impact.

There’s a good deal of interesting happenings in the data space right now, but companies need more impact to back up the cost.

There are no doubt other benefits I’ve missed out on taking data seriously, and putting someone in charge who is somewhat removed from the politics and inefficiencies that come from burying the capability inside an existing org (similar to the CIO coming of age, and now no longer reporting to CFOs in most companies).

The aim is however, to ensure your data analytics efforts are making a meaningful impact, and driving the kinds of returns most companies never experienced during the mobile app boom almost ten years ago now. And in so doing, benefiting every company that invests in the great capabilities a data-driven org has at its disposal.

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Drive Revenue Growth Without Driving Off the Road

Data – it’s something everyone is hearing about these days, whether it’s IBM stating how twitter can help you build better products to Google talking about self driving cars saving the environment through the power of data-driven route optimization.

That’s all well and good, but when you’re sitting at your desk looking at the list of things you have to accomplish today, and bring about new ways to grow revenues or build efficiencies into your business, where does one even begin?

No, you don’t always need a small army of data ninjas (though that can sometimes help) nor do you need a lot of high priced tools and solutions to help find buried gold in your troves of silo’ed business data.

To really drive to change, and begin leveraging your data to drive revenue it starts with the following steps

A) Start with the right hypothesis

If you don’t know what questions you’re trying to ask, then it’ll be very hard to find what you’re looking for that’ll help you achieve your business objectives. Asking questions like “where should I sell my goods” or “what products should I build” would require very expensive, intelligent systems capable of translating english into problems that computers could try and solve along with the tribal knowledge and understanding of the industry you’re in. Those systems exist, but man are they expensive along with the IBM consultants you’d need to hire to go between you and the outcomes.

Instead, focus on a specific question based on the types of data you know your business has. Start with things you think you know about your business, like what your key demographic is or where you source your raw materials from. Then go deeper and ask why those folks are your core demographics, or why you went to that one country for zinc. Good data mining starts with understanding the problem space better, and exploring things at a granular enough level that you can understand where intuition, guessing, or laziness came into place vs finding the right outcomes at the right level. You tell me you sell products in Seattle to women 44-55 years of age, I’ll ask what neighborhoods is that least or most true and if that answer is biasing you from growing your customer base because you’re focusing on metropolitan areas from a broad national study you paid a firm to do 5 years ago.

Sure, the same answer might be true, but having those metrics and answers in place means you’ll be able to see the shift and know when those answers are no longer the case, more importantly it’ll cause you to ask why the answers are they way they are and those levers or foundational factors will become more obvious and allow you to get granular enough to spot the outliers biasing your answers which get lost in the high level aggregated dashboards most execs use today.

B) Understand where your data lives

If you live and die by your profit and loss statements, or your quarter earnings reports, chances are that there is a complicated network of data analysts and administrators that compile all that information together to come out with a single answer. If you are getting your core business metrics from the same group you’re measuring against outcomes, be careful about unintentionally biased data that leans on rounding up vs rounding down and know where that data is coming from.

Too much data exists today, and decisions get made by people along the way on how that data is compiled and delivered, so build a culture of transparency and make sure you don’t have data points stacked on top of data points where errors can slowly creep in.

Aside from transparency, agile systems built the right way means you can do ad hoc reporting and build your own metrics with the ability to drill up and down without having to wait weeks for someone to compile a report on only the question you asked. Too much legacy infrastructure, and data scattered across the company along with an over reliance on key information being locked away in spreadsheets means a mess for really getting down to the bottom of things.

C) Figure out how the data is (or is not) related

If you’d like to see how twitter is affecting your supply chain, spend a little time thinking about how the two inter-relate. Twitter is going to be something tagged by date and location at a high level, but is a bunch of key words and a user name so prepare to invest in interpretive systems that aggregate and analyze or figure out ways twitter data might tie to a critical business system. There’s lots of ways to get at the answer, but high quality dashboards with pretty graphics may be just interesting and not at all useful if you don’t have the right data behind that tool giving you meaningful answers. It’s not about big data, it’s about meaningful data.

D) Ask an expert (whether or not you intent to hire one)

Data, like engineering or medicine, is a very complicated space that gets increasingly complicated by the day. Rather than becoming a data scientist yourself, find someone you know and trust that works with data and use them as a sounding board to run your ideas and suggestions by. It’s not that you may have a bad idea on how to leverage data to achieve business insights, but having it structured in the right way while learning what’s possible and what isn’t without a lot of investment is important to finding meaningful, bite sized ways to leverage data without breaking the budget and overspending for fancy whiz bang data systems.

E) Start small, grow big, track and measure along the way

The most important thing is to not bite off a big problem, like how do you end world hunger, but something small you know you could get good insights around relatively easily, such as where you spent what and how that goes against what you make with the ability to drill down into where that changes. If you typically get profit and loss statements saying you’re profitable in washington state, understand what city that is and is not the case and why one would be different than the other. Sometimes it’s just getting more granular data to what you already receive that can have the biggest insights into your day to day business.

At the end of the day, data isn’t a silver bullet, but it can make a difference in a big way when approached the right way. Start small, build a meaningful hypothesis, and strap in for the revenue growth that will follow.

Daniel Maycock is the Director of Strategy and Analytics at Transform, a data services
company. Our mission is to help drive impactful outcomes with data for our clients. We do this by providing tailored solutions that help people get tangible applications from their information.

His new book, Building The Expo, was published in January, 2015.

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