Tableau Conference - let's all data!

Stuart Lauchlan Profile picture for user slauchlan November 11, 2021 Audio mode
Let's all data, says Tableau. Here's how.

Let's all data

Together, we can make data everyone's favourite meal.

A colourful metaphor to kick off this year’s Tableau Conference from CEO Mark Nelson - albeit not quite as colorful perhaps as the opening song and dance number that kicked off the keynote! - but Nelson was in appropriately upbeat mode, declaring:

Thirteen years ago Tableau was a scrappy little start-up with a vision of helping people tap into the power of data. It's amazing to see how that vision literally changed the world. Data used to be a tool for only a small number of specially trained people. Now data is for everyone. Now anyone can experience the joy of exploring data, the thrill of discovering that first answer, and then asking the next question, and the next one, and the next one, until you've uncovered the root of a problem, or the kernel of an opportunity.

He pitched the usual apocalyptic vision for organizations who don’t get their heads and their data strategies around analytics:

If you're not data-driven, you will not be around in 20 years. That is really powerful. There's no better endorsement for the need for data than that. The amazing thing to me is that even with all these accomplishments there's so much more yet to be done. Research we recently did with IDC shows that nearly all CEOs want their organizations to be data-driven.

Other research shows that fewer than 30% of company leaders think they actually hit that mark. That is a huge delta and an incredible opportunity. This massive demand for data success has created an economy, a Tableau Economy to meet the need.

This Tableau Economy is built on three critical pillars, he explained:

The first is [a]  more than a million person-strong community. The next is  more than 1200 amazing partners. And third, at the foundation, is our Tableau platform.

That community element is particularly important, he argue:

Success in using data is much more than just using technology; it's also about the human part of the equation. It's about getting everyone using data. You have to ensure that people have the data literacy and skills to comfortably work with data that everyone has access to the data they need. And the expectation that decisions will be data driven, when everyone is working with data that gets to great outcomes together. Building and maintaining a data culture is a strategic investment in your data transformation. And it's absolutely critical if you hope to use data to remain agile and get to the outcomes you really need.


To make his point, Nelson pointed to the Seattle Seahawks American football team as a case in point, pitching that data is a key asset for helping the brand to deliver fan experiences that keep ticket holders happy. Amy Springer, Chief Revenue Officer, Seattle Seahawks, said:

We are the Seahawks, we're a football team. People expect us to be winning on the field. We're also a very incredible business organization and we want to be winning on the business side. That starts with a conversation about what we want to measure and manage. What winning means for us is to be a data-driven culture to really look at everything, to be able to peel it apart, dig into where we could be better and visually see it and that's where Tableau has been so critical for us.

The organization’s data journey started in terms of uncovering and identifying where opportunities can be, she explained:

To drive that culture, I start all in my meetings with my team with our dashboards.  Our main revenue dashboard, I pull it up and I highlight success. I can drill down into individual performance, and it's really rewarding to be recognized in that way.

This data-centric approach has strong executive support, she added:

Our President really has a core philosophy that we can measure anything and everything and that we should and that what isn't measured isn't managed. It only works if our whole organization is bought into it. Our President loves to pull up dashboards on his phone as well. He'll walk by people's desks and be like, 'Hey, I saw that you hit 90%, that's a great job. Tell me about that'. So just to be able to have that connectivity was really important for us over the pandemic, when we switched from [moving] out of office into a remote environment. The ability to be able to look at these dashboards in a real time way and how accessible it was across our entire team kept us communicating at a high level.

Being able to drill down to hidden detail is delivering benefits in many places throughout the organization. Springer cited an example where fan reaction to surveys found that there was a consistent complaint that supporters couldn’t hear the audio during games:

Until we used a heat map through Tableau and filtered all of our survey data by ticket location of the responder, we didn't know where this problem occurred. As soon as we did that, our heat map lit up in the four corners of Lumen Field [the Seahawks home ground] red. Red is not good. You don't want red, you want green. Green was everywhere else. But through that, we were able to make a very educated decision of where investment needed to be made to drive our fan experience and that happened directly through our Tableau heat map.

Tech direction

So that’s a good example of how Tableau tech is delivering real world benefits for an end users. But there’s still a lot to do, said Chief Product Officer Francois Ajenstat:

With all of the successes that we've had, we still haven't reached the full potential of analytics. There are still too many people that aren't getting answers from their data, too many people that aren't data rockstars yet, and too many organizations that don't have a data culture yet. In order to make analytics truly pervasive, we need to make data dramatically easier to use, more powerful than ever before, and more trusted than ever before. And we need to enable you to get the insights faster. This is the journey that we're on and the first step on this journey is to make analytics more collaborative, and more accessible.

At this point if you don’t detect the word Slack heading in the direction of the conversation, then you really haven’t been paying attention to what Salesforce has been up to for the past few months. Sure enough a key announcement at this year’s conference relates to bringing the full power of Tableau data into Slack where people can collaborate using it in various ways.

Specifically, the Tableau pitch for Slack-First Analytics involves a number of offerings:

  • Ask Data in Slack to allow anyone to talk to their data and ask questions using natural language and automatically get rich data visualizations as answers.
  • Explain Data in Slack to help customers understand the 'why' in their data with AI-driven explanations behind the trends in a dataset.
  • Einstein Discovery in Slack to provide predictions in the flow of work that identify leading causes and produce actionable recommendations on next steps.

While it’s an all digital, work-from-anywhere world, data and analytics are not integrated into all of the apps that people now use every single day, argued Ajenstat:

People have to leave these collaboration tools to go somewhere else to get the insights from their data. And if they have additional questions for the experts, well, they have to yet again go somewhere else. It's frustrating. We think there's a better way. That's why we're making data part of the team, where data feels more like a colleague, where you can ask questions and it gives you answers directly, and where it will proactively reach out to you when there are problems or opportunities that you should be aware of, and where you can have a conversation and collaborate on your insights and get the answers faster. This is Collaborative Analytics, analytics that work the way you work, wherever and whenever you need answers from your data. And with Tableau for Slack, we're bringing the full power of Tableau right where the conversation happens. It's easy, direct, immediate and powerful.

Next up is the need for AI and advanced analytics to be built everywhere that people work, he went on:

The complexity of the questions you have just keep getting higher and higher. We hear from [users] that [they] want to solve more advanced analytics with Tableau. [They] want to be able to easily add predictions in [their] analysis, to know not just what happened, but what might happen next. [They] want to be able to analyze different scenarios to understand outcomes with 'what if' analysis and [they] want to be able to classify [their] data to determine the next best action and streamline decision making.

The problem is that not everyone has access to AI and advanced analytics. Today, it requires specialized tools with specialized skills and a lot of time and effort to drive success, while we believe that everyone should have access to intelligent, actionable insights. That's why we created a whole new class of analytics called business science. Business science brings data science techniques to more people so they can solve more complex business problems, making the people with the business context equipped with more powerful insights than ever before.  We can democratize this to put it in the hands of every analyst.

Specifically, this involves two new offerings:

  • Model Builder to enable business teams to collaboratively build and consume predictive models, using the Einstein Discovery engine, without having to leave their Tableau workflows.
  • Scenario Planning to help customers make better decisions by comparing alternative scenarios, building robust models and understanding expected outcomes.

Ajenstat explained:

We took the first step earlier this year by introducing Einstein Discovery in Tableau. This brought predictions and recommendations throughout the Tableau platform, in dashboards, in calculations and in Tableau prep. Today we're expanding Tableau business science capabilities even further with Model Building and Scenario Planning. This will empower more people to discover advanced insights and predictions, build and deploy models and compare a variety of outcomes and scenarios directly in Tableau.

My take

A confident  and energetic start to the conference. I must admit I wasn’t ready for primary-colored adults dancing around and miming to “Don’t be a hater, let’s all data!’, like a hi-tech version of The Wiggles. But that cynicism aside, the Tableau Community emphasis is hugely important and that runs through the DNA of the keynote. That’s the bedrock of this offering. We’ll pick up more from the event over the coming days.

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