Trio of trends influencing analytics, says Tableau's Beers

One-stop shop

Speed and agility require efficiency.

It is inefficient to jump from one tool to clean and prepare data, to another to find data sets once they've been curated, and to still another to visualize and analyze data after its been extracted from its storage location and loaded into a business intelligence platform.

It was once the norm, but now vendors have developed capabilities that enable customers to work with data in a single location. Through integrations, customers of traditional BI vendors such as Tableau are now able to work with BI platforms directly in their data warehouses.

Meanwhile, as the amount of data organizations collect grows exponentially with the rapid evolution of the internet of things and advent of new technologies -- e.g., 5G -- Tableau and vendors such as Qlik and SAS Institute are among those now offering data management capabilities.

"It's no longer the data explosion; it's data chaos," Beers said. "The challenge is to control this chaos, harness the data and turn it into the asset on which they build success."

Simplicity -- a single location to find and work with all the data -- helps manage the chaos, he continued. That location can be the cloud, on premises, and in APIs or applications.

In that location, organizations can catalog their data and apply the data governance guidelines needed to ensure regulatory compliance, scale analytics across entire organizations, and give end users more flexibility and more advanced and easier-to-use tools.

"When data management capabilities are integrated with analytics tools, you can help people in your organization make the right calls at the right time with the right data, even as you scale," Beers said.

Embedded analytics

Just as the ability to work in a single location is a significant analytics trend, so, too, is the ability to work with data within any workflow.

With many people now working from home due to the coronavirus pandemic, and many others now splitting time between the office and remote work, people do much of their work in collaboration platforms such as Slack, Microsoft Teams and Google Docs.

Others, meanwhile, do most of their work in office applications from vendors like Salesforce and SAP.

The ability to work with data while remaining in those environments, therefore, creates speed and agility. Having to leave Slack or Salesforce to go into a Tableau or Power BI dashboard is inefficient.

Embedding analytics assets -- and even entire platforms -- into collaboration tools or business applications leads to speed and agility.

"People can ask questions about the data whenever and wherever they want [with embedded analytics]," Beers said. "If they are in their collaboration tool, they can stay in that app and continue to collaborate with others and keep the business moving forward with that speed needed to drive transformation."

Advanced analytics

Amid the pandemic, rear-facing reports and dashboards were no longer enough for organizations. Past performance no longer accurately predicted the future.

Organizations needed forward-facing analytic assets -- models that could give them clues about what was to come so they could act preemptively, rather than react perhaps when it was already too late.

For businesses to thrive in the future, they have to be making bets on data now.

Those models require advanced analytics capabilities such as augmented intelligence and machine learning, but not every organization is equipped with a team of data scientists able to write the code needed to build predictive models.

Even before the pandemic, however, organizations were realizing that to be quick and agile, they needed forward-looking models.

As a result, another emerging analytics trend includes the way many vendors have used AI and ML to develop low-code and no-code tools to enable end users and data scientists to develop models.

Tableau, for example, recently released an integration with Salesforce's Einstein Discovery -- a no-code tool within Salesforce's Einstein Analytics platform that uses AI and ML to enable predictive modeling and prescriptive recommendations -- to create a concept Tableau is calling business science.

Business science is the vendor's term for enabling business users to use data science capabilities with AI and ML.

"I believe it will change the way we work with data, empowering more people across the organization to drive transformation," Beers said.

Beyond model development, vendors have been using AI and ML to enable users to automate certain repetitive tasks, query and explain data using natural language processing, and send push notifications to alert users to changes in their data.

"These smarts make it easier for more people to get the answer they need when they need it, regardless of their technical expertise," Beers said.

Tableau's development plans

With the analytics trends he highlighted as guides, Beers said Tableau's recent product development strategy features capabilities that enable users to do all their analytics work in a single environment, do that work within their normal workflows, and take advantage of AI and ML to do some of the work only data scientists could in the past.

Tableau Data Management, introduced in 2019, integrates with Tableau's traditional BI platform to create a single environment and includes data preparation, data cataloguing, governance, security and storage. Tableau also now offers Embedded Analytics, a tool that enables users to embed and integrate Tableau into other workflows.

In addition to the integration with Einstein Discovery, Tableau recently introduced an improvement to Ask Data, its natural language query tool, and automated data quality warnings.

"We believe [these analytics trends] will allow organizations to gain the speed and agility required for success," Beers said.