What is Tableau Business Science?

One of the things we’re focused on at Tableau is how to get more people using data in the daily routine of business. When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when we see human potential unleashed, with amazing outcomes. However, there have been plenty of barriers between people who rely on data and the sophisticated analysis required to make the best decisions with it.

We want to reduce those barriers by introducing a new class of analytics: Tableau Business Science. Business Science brings the powerful capabilities of data science into the hands of business people.

Using AI, ML, and other statistical methods to solve business problems has largely been the domain of data scientists. Many organisations have small data science teams focused on specific, mission-critical, and highly scalable problems. But there are a large number of business decisions that rely on experience and knowledge in addition to data—and that would greatly benefit from applying more advanced analysis techniques.

This is where we see an opportunity to democratise data science capabilities, minimising the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights while they’re still relevant. At Tableau, analysis has always been about letting people ask that next question, explore that next hypothesis, test that next idea. Now, we’re taking it further and helping more people elevate their human judgment with practical, ethical AI that brings predictions into their business problems today.

By providing explainable AI and predictive analytics tools to analysts and business users, Business Science helps people make faster, more confident decisions across an organisation, while expanding their analytics use cases and deepening their understanding of their own data.

What is Tableau Business Science?

Business Science is a new class of AI-powered analytics that allows people with domain expertise to make smarter decisions faster and with more confidence, recognising that not all problems require exact precision at the expense of speed and business context. Business Science solutions are also rigorous and accurate, but enable decision makers to determine what they need for their use case with control and flexibility. A few examples of the way users can exercise control are input data, variable selection, and threshold setting. Business experts are empowered to enable a fully automated experience or have the choice to make guided changes in the model creation process. By equipping more people with governed, no-code AI-like predictions, what-if scenario planning, and guided model building—business teams can do more analysis themselves.

Business Science democratises data science capabilities and helps domain experts understand the key drivers of a model without having to learn traditional data science tools. With guided AI experiences in the hands of domain experts, teams can apply advanced analysis to more business problems and make important decisions faster and with more rigor, while still leaning into their human judgment. It’s not about fine-tuning super precise models, but guiding people closest to the problem in the right direction.

After all, business is inherently complicated and unpredictable, so domain experience and knowledge from people who understand the dynamics of their field is critical. And for this reason, Business Science is incredibly valuable for helping to address business problems that a data science team might not be able to allocate resources to or prioritise.

Who is Tableau Business Science for?

Business Science is for people with the context to understand what their business looks like, the important drivers to the business, and which data might be helpful for finding solutions. Business Science does not require someone with deep, technical expertise who writes, deploys, and monitors algorithms. By enabling business professionals and data analysts to leverage the predictions and insights that come out of ML models—without having to learn Python, statistics, or how to tune parameters for an algorithm—you’ve started to grow your team of data-driven experts.

We’ve observed countless scenarios where Business Science is the right approach that will result in the best outcome for the business—from lead scoring for marketing and assigning quotas for sales teams, to supply chain distribution and optimisation. Human resources might use Business Science to assess the likelihood of a candidate accepting an offer. A real estate team might apply Business Science to plan where to buy office space and explore the costs of moving people from one location to another. Any number of teams could apply Business Science to budgeting or resource allocation situations.

How is Tableau Business Science different from data science?

While Business Science uses some of the same statistical and computational techniques of data science, domain expertise and time to value are more important than statistical rigor. Business Science operates under a different premise—both with different goals and different typical users than data science.

In data science, the output is a machine learning algorithm that can be put into production to enhance a recurring process. Often data science tries to answer a “yes/no” question or determine if a predicted outcome crosses a certain threshold—for example, fraud detection is a great data science use case. Historical data trains an algorithm to recognise fraud by analysing patterns over hundreds of thousands, even millions of occurrences, and applying a prediction as to whether a transaction is fraudulent. And fine-tuning a model like that is critical because the slightest difference in accuracy—even a fraction of a percentage—could cost a company millions of dollars.

With Business Science, the objective is to move a KPI, not to perfect a model until it’s the most precise. By facilitating a more iterative, revise-and-redeploy process than that of traditional data science cycles, Business Science eliminates barriers for people with business context to quickly build models and use predictions. More people can get value faster from these advanced analytical techniques, and make smarter decisions when and where they need to.

For example, a retailer might want to know what product to add to its stores to increase profits in a certain region. A business professional would understand how factors like supplier relationships, regional trends, and other impactful, qualitative implications could affect the decision—details that a machine might never understand. Together, human expertise, judgment, and contextual awareness combined with the rigor, automation, and scalability of machine-generated insights drives better business outcomes.

Data science would be more suitable for analysing the efficacy of a vaccine in a clinical trial, whereas the problem of distribution and allocation of a vaccine—a problem whose nuances continuously evolve, relying heavily on human judgment—would be more suited for Business Science. More metaphorically, think of Vincent van Gogh like a data scientist, but Business Science is someone with a paint-by-numbers of Starry Night. Data science is “37.7914° N, 122.3951° W + 2,03 x10^5mm in 44,7° => 37.7932° N, 122.3947° W,” where Business Science is “take the third left, mind the traffic, and you have arrived at One Market Street.”

Learn more about Tableau Business Science

We couldn’t be more excited to establish this class of AI-powered analytics with Salesforce’s Einstein Discovery engine in Tableau. Built with transparent and ethical AI, it’s a proven technology that gives business teams visibility into key drivers behind the results and the potential for bias, allowing for deeper understanding and confidence in decision-making. And it’s built directly into business workflows, bringing users the most value where they’re already working.

Want to dive deeper into Tableau Business Science? Check out our whitepaper.

Editor’s note: This article originally appeared on Tableau.com