Time to insight over precision - Tableau brings ‘data science’ to business users

Profile picture for user ddpreez By Derek du Preez March 29, 2021
Summary:
We speak to Tableau CTO Andrew Beers about the company’s latest release, which aims to bring Salesforce’s Einstein capabilities to business users.

Data going into bottle, 3D in blue © 3dkombinat - shutterstock
(© 3dkombinat - shutterstock)

When Salesforce announced its $15.7 billion acquisition of Tableau in 2019, Salesforce CEO Marc Benioff said that the company intends to bring ‘data literacy to everyone in business'. And we are beginning to get a sense of what this will look like for the now combined companies, with the latest Business Science release from Tableau. 

The acquisition also prompted questions over how and if there would be overlap between Tableau's BI capabilities and Salesforce's AI product, Einstein. The Business Science announcement from Tableau last week, which aims to put data science in the hands of business users, sheds some light on this question too - as it seemingly showcases how both companies can jointly bring the best of both worlds to the table. 

We spoke with Andrew Beers, Chief Technology Officer at Tableau, about Business Science, which brings Einstein Discovery to Tableau's 2021.1 release later this month. The company said that by integrating Einstein Discovery into the Tableau platform, this will help business users go beyond understanding what happened and why it happened, to explore likely outcomes and inform predictive action. 

But the most interesting part about the intentions behind the product is that Tableau is urging business users to recognize that there is a trade off between time to insight vs precision - where the later requires heavy investments in data science teams and tooling. Simply put, if you can get there faster, even if it's not 100% accurate, then there could well be benefits there. 

Beers says that the COVID-19 pandemic has amplified the need for business users to adopt more sophisticated data science tools, as data is at the centre of driving change across an organisation. In this context, he says: 

The challenge with any democratization effort around data I think is just encouraging companies to pull together the right data. We've done a lot to make data visible to the analyst, we've done a lot to make data sort of workable with the analyst. But getting that data pulled together is always challenge number one - Tableau has helped with that by making the data visible within the organisation, making it discoverable Tableau was built for the business user, but that landscape has just expanded over the 16 years that we've been selling software. 

So we think business science is a natural next step for us. Companies are very, very interested because it is about helping people make decisions in context and bringing business context into decisions. 

Getting insight, quickly

Tableau says that Business Science is being driven by demand from business users that want data science capabilities, but don't necessarily have the resources or time to support it for every use case. Beers explains: 

A lot of companies are starting to reach for those data science tools to improve decision making. And of course there's all kinds of challenges with that, like not everybody's got a data scientist. Data scientists are relatively rare. And so, I may not have one, or I may have data scientists and they're not necessarily gonna be focused on my problem. There's a lot of examples in the business domain, where I need some predictive power, where precision is not necessarily required, but something that is directionally correct is required because it's going to be injected into this place with all kinds of business context. 

And so that's why we think there's this opportunity to democratize advanced analytics, in particular putting some of these data science techniques into the hands of business experts. 

Beers says that traditional BI focuses on having a bunch of historical data, around something like the sales on products that your company is making. BI would allow a user to look at that data and slice it and dice it in a variety of ways - who's selling it, who's buying it, what do you know about the customer, etc. 

However, sometimes the information that you really need to get in front of your sales team is: how likely is this person going to renew this service? This more predictive approach allows said user to prioritize their work for the day. Beers adds: 

Does that need to be a super precise model? Probably not. It's got to be directionally correct, but it doesn't necessarily have to be precise.

We think that the trade off there is getting the ability for the business experts to build these models through discovery and change them as the conditions of the business change. And then getting that next version of the model out and into the hands of the consumers, which in this case is the sales team. We think that that ends up being a lot more important for these kinds of problems than let's say going through a rigorous redesign of a data science project.

Bringing the best of Tableau and Salesforce together

Beers outlines that the Business Science announcement highlights how Salesforce and Tableau are bringing the ‘full power' of both platforms together. In this case, inside the Tableau analytics products, users will be able to write some relatively simple calculations to call out to the Einstein discovery models, which will allow users to bring some predictive insights to their Tableau environment. This moves the needles for Tableau's user base that have typically relied on historical data for insights. Beers says: 

The models aren't being prescribed by Tableau. The models are built by the customer using Einstein Discovery. If you've got some sort of KPI that you're trying to maximise or minimise, Discovery is very good at building models that can predict where things are going based on your historical data. And then if you've told it ‘here are the things that I can control, here are the things I can't control', then it can say ‘well by controlling this variable, we think you're gonna affect the outcome in this way'. 

To make use of Business Science you have to be both a Tableau and a Salesforce customer, as you need access to both platforms.  Beers says that there will be a lot of upside here to both companies, as there are a lot of Salesforce customers that Tableau hasn't acquired and vice versa. And we get the distinct impression that this is a sign of the thinking behind how the companies are planning to progress as a combined entity. Beers adds: 

Salesforce has long had this message on helping companies go through digital transformation, this has been their message for years. And at the heart of any digital transformation is data. That's one of the driving reasons why they wanted to bring us into the fold, because they realise that data is at the heart of all these things. 

In terms of what we are prioritizing, we've got a lot of irons in the fire, absolutely. We're definitely gonna get better with the data in the Salesforce ecosystem. There's a lot of data there, that now we're part of the company, we're going to get some great access to. And then both companies are going to be leveraging each other's assets. And this release is a great example - we're leveraging the Einstein Discovery assets, bringing that together, expanding it to a bunch of new users.

My take

It's good to see the fruits of the Salesforce/Tableau acquisition being brought to market. This will be particularly interesting for Tableau customers, which could see Einstein bringing more predictive prowess to their traditional analytics platforms. Analysing historical data that's static isn't as powerful as putting it to predictive use. However, as ever, the proof will be in the customer stories and the use cases - which we will be chasing to get our hands on.