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The business benefits of automating and embedding BI

Automated insights and embedded BI are getting decision-makers the data needed to make quick decisions, accelerate business processes and lessen manual work required.

Business intelligence continues to serve as a competitive weapon and the use cases continue to become more sophisticated. At the same time, BI use has been democratized among organizational leaders and "citizen data scientists" so the organization can benefit from data-informed insights.

Augmented analytics makes the democratization of BI possible because unlike traditional BI, it includes natural language processing (NLP), so users don't need to understand a query language to pose a business question. Augmented analytics also streamlines collaboration using NLP to explain or "narrate" data visualizations, so users don't have to guess what data visualizations mean.

Automation enables augmented analytics to work as advertised, including its use of NLP. In an NLP context, it's the automation of natural language to machine language and vice versa. Automation also accelerates business processes and lessens the amount of manual work that's necessary to drive insights from data.

"Automation is much more than a feature; it's at the core of analytics," said Ashley Kramer, chief product and marketing officer at augmented analytics platform provider Sisense. "Automation, via well-trained, responsible AI, is the driver behind the true promise of augmented analytics: giving users the actionable insights they need, when they need it, without having to ask or perhaps without realizing they need it in the first place."

Augmented analytics is also being embedded into third-party applications and firmware for user convenience and to drive additional insights that weren't possible or practical to do before.

Automated insights save time

The modern business environment made traditional BI on its own obsolete. As the global business environment continues to move toward real time, business leaders can no longer wait days, weeks or months just to receive a report.

"The inflection point here is the difference between a rules-based [approach] and self-learning or training," said Tomás Puig, CEO and founder of marketing and analytics company Alembic Technologies. "There's good cases where the new fraud automation systems no longer require me to tell my bank I'm going on vacation because I have my cell phone with me that has GPS."

Automation, via well-trained, responsible AI, is the driver behind the true promise of augmented analytics: giving users the actionable insights they need, when they need it, without having to ask or perhaps without realizing they need it in the first place.
Ashley KramerChief product and marketing officer at Sisense

The main benefit of automated analytics is faster time to insights. A secondary benefit is saving humans time. For example, in a call center the logs are analyzed to understand what's working, what's not working and why. It's now possible to have the call transcripts automatically generated and analyzed, with the key moments identified, tagged and highlighted for faster issue resolution.

From an operational standpoint, call centers can achieve greater consistency among call center agents who are interacting with customers. If there's a human QA team combing through transcripts and identifying issues, some of them may use all the criteria provided while others only use partial criteria.

"[The] current main use cases [in call centers] are automating the quality assurance process so users understand how the different agents are doing across different teams, geographics and time zones," said Jithendra Vepa, chief scientist at intelligent workforce platform provider Observe.ai. "It's important for the contact centers to know what their top agents' scores are compared to their bottom performers, where the bottom performers are lagging, how they can be coached and how they can improve their efficiency and performance over time."

Embedded BI provides convenience

Embedded BI extends insights out to the "edge," which provides the business with insights it didn't have previously, such as the impact of agricultural field health on a wine producer's long-term real estate planning. It can also provide users with greater convenience when BI capabilities can be accessed from within another application, so users don't have to switch back and forth between the two.

For example, Personica embeds Sisense BI into its SaaS platform so restaurant owners can launch a loyalty program and use analytics to understand how well that program is working and with which customers.

A classic use case is providing diners with a free entree after a certain number of visits during a given month. Then, using the analytics, the restaurants can understand how many customers visited the requisite number of times and what their check size is --which indicates how profitable or unprofitable customers are.

"Restaurants that analyze their diners' buying behaviors can target them with offers that appeal to them," said Dave Arthurs, chief product and technology officer at restaurant loyalty and personalization platform provider Personica. "If a customer always orders a certain type of burger, personalized email analytics show that emailing them offers for that particular burger will be more successful than plying them with a general burger or meal deal. It's a game changer for smaller shops and makes their diners' experience personalized and enjoyable."

Restaurant owners use that intelligence to optimize many things including their marketing campaigns, food items on the menu and staffing -- business decisions that impact the bottom line.

Augmented analytics helps organizations answer more types of questions about more areas of the business in a simplified way. Automated AI speeds time to insights. Embedded BI extends the reach of intelligence out to the edge and provides users with greater convenience when the BI capabilities can be accessed from within an application utilized on a regular basis.

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