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Automated Business Analysis Is Spotting Insights Humans Missed

From spotting patterns to finding correlations analysts wouldn’t even think to look for – we explore how automated business analysis is transforming the way organizations use data to uncover valuable insights

Automated Business Analysis Is Spotting Insights Humans Missed

Written by Solomon Radley on Aug 21, 2019 2:34:00 PM

AI

Data overload is a growing problem for enterprise businesses. Analysis teams must often work manually to navigate seas of data and generate the specific insights their colleagues request.It can take multiple analysts weeks to gather, integrate and process the data they need. As a result, the insights they uncover may no longer useful by the time they’re generated. Worse still, this process means many valuable insights will never even be looked for in the first place.

Automated business analysis offers a potential solution to all these challenges. In fact, Gartner predicts these tools are among the top 10 data and analytics technologies that will transform global business by 2020.

“If you think about data in terms of a Rubik’s cube, there are 27 cubes in a Rubik’s cube,” says Rita Sallam, Research VP, Business Analytics at Gartner. “Even just 27 variables to analyse represents thousands of combinations that we have to look at when exploring data.”

“People and organizations want to explore bigger and bigger Rubik’s cubes,” she continues. “So, the complexity and the number of variables just make it almost impossible for people to analyse data manually.”

Solving the ‘Rubik’s Cube’ of Data Analysis

Although this class of augmented analytics technology is relatively new, Gartner says it will soon be a major selling point for BI solutions. In fact, many enterprises are already using these tools to streamline their analysis operations and uncover valuable insights humans would have missed.

For example, most restaurant chains wouldn’t think to get their analysis team involved when they needed to close a store for remodelling.

However, when you have 20,000 restaurants worldwide, events like this that might seem insignificant can often generate highly valuable data. And thanks to automated business analysis platforms, one global restaurant chain was able to stop these insights from slipping through the cracks.

Their technology alerted the company’s data team to a sudden spike in fountain drink sales following a refit of one of its stores in Ireland, which the team was able to trace back to a change in restaurant layout.

Thanks to this one insight, the chain is now looking into changing the layout of more stores to increase its fountain drink sales worldwide.

Diagnosing An Unseasonal Demand For Sandals

Designer footwear and accessories brand Jack Rogers has a similar story to tell. The company has a small analytics team, but it needs daily insights to spot and respond quickly to regional changes in demand.

To achieve this, the team uses Outlier AI’s automated business analysis platform to automatically analyse the company’s Google Analytics, Google Ads and Facebook Ads data. The platform then communicates to the company when it spots anything unusual.

So, when Outlier AI alerted Jack Rogers’ marketers to an unseasonal surge in sandal sales in Florida, they discovered that summer had come early in the region and were able to adjust their campaigns accordingly.

“We didn’t expect to see interest in this particular product category so early in the season,” says Megan Petersen, Director of Ecommerce at Jack Rogers. “Thanks to Outlier we were able to adjust our email marketing campaigns in real-time to take advantage of the interest.”

“If your business knows when consumer behavior begins to shift, when consumer preferences change or when demographics shift you can be proactive,” adds Outlier AI CEO Sean Byrnes. “It’s an entirely new model of data-driven decision making.”

The Future of Automated Business Analysis

Automated business analysis has implications that stretch far beyond the retail and restaurant sectors.

Companies in industries as diverse as financial services, life sciences, the public sector and beyond are using it to streamline supply chains, detect fraud, track social media campaigns and more besides.

As enterprises become increasingly interested in analysing vast datasets, the importance these new technologies looks set to increase.

“AI and machine learning are being infused across the analytics workflow, much as they are being infused in business applications,” explains Sallam. “I don’t think it’s a matter of if or when [these] capabilities are making their way into platforms, so you need to plan for them.”

“In the future, automated business analysis will become a required toolkit for all companies,” agrees Byrnes. “Business decision making will become more flexible, advanced and measured as data becomes part of the decision making process instead of just an input.”

Platform’s like Outlier’s are already helping organizations sift through vast quantities of data to identify the data stories that matter. As these tools become even more sophisticated, they look set to transform the way enterprises use data and analytics.

Integrating natural language processing capabilities with automated business analysis tools could empower analysts to explore data models more easily to find the insights they need.

Meanwhile, systems that convey the results of data queries using natural language have the potential to remove bias from data interpretation and reduce the amount of specialist skills required to use these tools.

That’s why Gartner believes these technologies have the potential to vastly increase business analytics adoption in enterprise businesses within the next few years. If the consultancy is correct, it’s something every data leader should be exploring right now.

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