“Every second of every day, our senses bring in way too much data than we can possibly process in our brains,” said X Prize Foundation executive chairman Peter Diamandis during a TED talk in 2012. Regardless of your industry, it’s a really good point that applies to all of us in our work and home lives. Diamandis went on to talk about how the human brain has a built-in data analyser called the amygdala in the temporal lobe, a sort of early warning system. The point is that to operate efficiently, humans need to process data really quickly, at the very least, to protect ourselves from danger.

The notion that our brains would log into a separate system, or even worse, send the data off to a completely different department to analyse before knowing how to react, is beyond comprehension. It just wouldn’t work. We would be in mortal danger, for one, and operate completely inefficiently. So, why do we expect our business data to operate that way? 

The short answer is often capability. Either we don’t know how to do it, or the software and systems are not actually capable of doing so. There’s no shame in that because business intelligence has been operating that way for some time. It’s had analytical limitations, which have often demanded taking data from one system to another to get any form of result. This is one of the reasons why embedded analytics is so important.

So, what exactly is embedded analytics?

According to Gartner, “Embedded analytics is a digital workplace capability where data analysis occurs within a user’s natural workflow, without the need to toggle to another application. Moreover, embedded analytics tends to be narrowly deployed around specific processes such as marketing campaign optimisation, sales lead conversions, inventory demand planning and financial budgeting.”

Its purpose is simple. As the name suggests, the analytics function is embedded within whatever application you are running, to speed-up data workflow and decision-making capabilities. There is no need to take the data from one application and then log in to another application to make sense of it. And that’s just the start. 

A bit like Diamandis’s overflowing brain, there comes a point where even this approach can reach saturation point. As we know, businesses and organisations are being overrun with data and we are constantly told about shortages of data scientists and data analysts, so throwing more humans at the problem – even clever ones - is not always going to be the answer.

So how do organisations effectively manage large amounts of data? How can businesses ensure that not only is everything in one place but that its analytics function reduces complexity and errors, guarantees data accuracy and minimises bias?

Using AI to automate data analytics is the logical next step in embedded analytics. Gartner refers to this as augmented analytics. According to Rita Sallam, VP analyst at Gartner, “As businesses become inundated with data, augmented analytics becomes crucial for presenting only what’s important for users across the business in their context to act upon at that moment. It drives less biased decisions and more impartial contextual awareness; transforming how users interact with data, make decisions and act on insights.”

While improved operational performance is undoubtedly a key goal, it’s also worth noting that automated, embedded analytics also has considerable value for users. Employers that work within a core application no longer have to try and learn a different analytics application to gain insight into data. The embedded experience is more natural, there are less costs and results can be arrived at more quickly and more accurately.

This means management decisions can be made more quickly and with greater confidence, a bit like a human brain weighing up the pros and cons of risk. As far as analytics goes, time is increasingly of the essence – and augmented analytics can get you there faster.

About the author

Manjit Johal is the Co-Founder and Chief Technology Officer at Avora.