The state of Tableau 2019 with CPO Ajenstat - if we don't fix the data performance gap, we can forget about analytics ubiquity

Profile picture for user jreed By Jon Reed November 21, 2019
Summary:
This year's Tableau Conference shook up how I see the company - and no, Salesforce isn't the reason why. There's no better foil than Tableau CPO Francois Ajenstat, who explained Tableau's shifts in pursuit of analytics ubiquity.

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What a difference a year or two makes. Seems like just yesterday I was talking to Tableau CPO Francois Ajenstat about Tableau's stock market ups and downs (mostly ups in recent years).

Now, with Tableau's acquisition by Salesforce heading towards a done deal, I'm pulling his views into a Tableau-at-Dreamforce preview:

There's a tremendous culture match. And, having been a long-time Tableau person, just engaging with my Salesforce counterparts has just felt natural. We have the same kind of mindset of customer focus, and innovation, and really innovating rapidly.

But I've already covered that off as best I could. Besides, that's not why this year's Tableau Conference jolted my thinking. In past years, I've pressed Ajenstat on a question that's really been bugging me:

Shouldn't data-driven companies outperform others?

But at this year's Tableau Conference, I picked up on a shift. Example: a McKinsey study on "breakaway analytics" issued a bold thesis:

A handful of the world’s companies have cracked the code on embedding analytics into every layer of their organizations.

Tableau's own executives cited similar stats, with "data-driven" companies outperforming their peers across a range of metrics, from Net Promoter Scores to new customer acquisitions. Okay, that takes the edge off my big burning question, but it raises another one: the data performance gap. As I wrote: 

For McKinsey, the good news of documented results also points to reams of companies that are being left behind. They might have analytics, but it's not pervasive. It's not in the cultural fiber. Why?

Tableau's push for "data ubiquity" - and a big move into data preparation

The Tableau version of that question is: why are some Tableau customers having greater measurable success than others, using the same tools?

To their credit, Tableau has some good answers to that question. But what does Ajenstat think? As he told me:

I think the way I would describe it is Tableau is moving faster than a river. It's expanding its breadth and depth of capabilities. And, Tableau is focused on driving what we would say is analytics ubiquity.

"Analytics ubiquity" is about building a passionate data culture - one of the key factors Tableau sees with its most successful customers. But there's more: to achieve that analytics ubiquity, Tableau knows it needs to go further into areas where customers need help, from AI (Explain Data) to natural language queries (Ask Data), to the thorny plumbing known as "data preparation." Ajenstat:

When you take yesterday's keynote into context, you start seeing how Tableau is moving beyond the core and serving new kinds of use cases. Data management, that's a radical departure for us. We've never really focused below the viz layer. Over the last 18 months we've been investing in data preparation, data cataloging, data management as a whole. And so, Tableau is becoming much broader, more robust of a platform.

Why move into data prep? Customers were loud and clear:

When we talk to our customers, they tell us, "Tableau is fantastic and we are getting tons of adoption for it. But, we've got a number of fundamental problems. Problem number one, Tableau is great if the data is ready for analysis." They're spending so much time trying to clean the data.

Anything that Tableau can do to make data preparation problems go away so they can spend more time doing data analysis is a huge win.

The push for analytics ubiquity means something else: self-service tools. Yes, data analysts remain the rabid core of Tableau's audience, but it doesn't stop there anymore:

The people who need to clean their data; they're not professional data wranglers. They're not data engineers. They might have been doing it in Excel, doing VLOOKUPs, or struggling. And, they really are the ones who have been under-served. And so, what we're trying to do is enable anyone, regardless of their skills, to be able to clean their data.

The BI space was designed for the specialists - not anymore

If anyone has doubts about Tableau's determination to reframe the entire data conversation, look no further than the Tableau Conference '19 keynote, which devoted considerable time to the remarkable story of the female codebreakers who played such a key role for Allied Forces in World War II. Did data literacy literally save the world as we know it? You could make that argument. But as Ajenstat says, there was a reason this story was front and center:

The story is a great story, but to me, it's the theme that came out of that story, which is that, in order to really create breakthroughs, you have to rethink the people that can do the work. And so, why does this matter?

Because in the business intelligence space, historically the tools have been designed for the specialists. They were designed for the experts, and data was then given to the people, and there was always this thing that, "No, no, no. You can't use the data." Either you don't have the skills, it shouldn't be your job. You can hear things like "You're not smart enough to do this." How do you know that you can trust the answers that you could get? Right?

That change doesn't happen overnight - but Tableau believes you can pull this off with your existing workforce:

There's a lot of just change management that comes in out of that. So what we are trying to dispel is, no, if you rethink the workforce that you have, you can get incredible insights.

Of course, it's not just the people; Ajenstat pointed to the need for training, tooling, proper data governance. That's why Tableau CEO Adam Selipsky closed off his keynote with the Tableau Blueprint:

The Blueprint is this idea that technology alone doesn't solve the problems, right? You can have two different companies with the exact same set of tools, the exact same desire. One will succeed with their analytics, and the other one will fail. Why? We have great tools. Don't we provide fast insights for everyone? Of course. What's different is the culture between those two companies.

My take

One pesky question: when you expand your data agenda across an organization, and engage more casual users with accessible tools like the NLP-powered Ask Data, doesn't have that have licensing implications? Ajenstat believes that Tableau's push towards subscriptions covers a lot of those licensing issues off:

When we moved to our new subscription model just two years ago, what that did is it really enabled, obviously, a lower price point to go subscription, but it lowers risk - and enables people to scale more easily.

Ajenstat cited customers who are surprised at how quickly adoption of Tableau goes from the few to the many. So are the majority of customers on subscription pricing yet? No, but the majority of Tableau's new business signs on via subscription.

As for cloud, Tableau is on a multi-cloud push, with a third of Tableau's total deployments on the public cloud. Ajenstat says 15,000 customers are on Tableau Online, a "fully SaaS offering." Data gravity dictates a "hybrid" model at most large enterprises for years to come; Tableau gears its deployment options accordingly.

If you check my Looker JOIN coverage from the prior week, there is a fascinating contrast: both companies have big new parent companies. Both have a community of passionate, self-described "data geeks." But Looker's leadership is less convinced that a passion for data can become universal in most companies. They think data-infused applications is the way to get casual users more hooked on data. That sets up a fascinating contrast; I'll be curious to see these two data worldviews play out.

We didn't talk much about AI this year, but I've already written about Tableau's AI push, in conjunction with its partners. We'll address that further with a piece by Neil Raden that digs into Ask Data and Explain Data.

My only regret about my time with Ajenstat? It was only later in the day that this idea crystallized in my head: I believe this new phase of Tableau is about eliminating the data analyst bottleneck, and empowering business users to ask their own questions and even help to prep their own data.

Data analysts might resent being referred to as a bottleneck, especially after all they've done to liberate BI from the IT quagmire. But truth is truth. However: armed with modern BI tools, and an attitude of empowering business users, analysts too can transform.

I'm would have liked to put that question to Ajenstat and hear his refinements to it. But next year will be here before we know it...

Image credit - Photo pf Tableau CPO Francois Ajenstat at Tableau Conference 2018 by Jon Reed. Yes, I cheated and re-used last year's picture, as it came out better than this year's.

Disclosure - Salesforce is a diginomica premier partners. Tableau paid the bulk of my expenses to attend Tableau Conference 2019.