This article explains what augmented analytics is and how it’s become a key driver for many organizations modernizing their analytics capability
Analytics has become ubiquitous in our day-to-day life. It’s a key component — be that via monetization or measurement — in creating and identifying value in our business.
However, the complexity and volume of data every business accumulates is a common challenge for those that need to make decisions. Data is a continuous and always growing consideration, and it can be particularly challenging for larger enterprises.
For business users, being able to comprehensively track, identify, understand, and act on what is most important, and inherently know the best action to take is an increasingly impossible task to accomplish manually. Often resulting in decisions that might not be entirely data-led and rely too much on instinct.
There’s an ever-pressing need to keep up with the fast-growing data we need operationally and long-term, this is where solutions like augmented analytics come in.
Today, identifying, managing and understanding data is made faster and easier through the use of automation, algorithms and natural language capabilities that enhance — not replace — the key steps along the analytics life cycle.
Augmented analytics is the use of technologies such as artificial intelligence (AI) and machine learning (ML) to transform how analytics can be built, consumed and shared.
Originally defined by global advisory firm Gartner, using augmented analytics as part of your data analytics life cycle — data preparation, data discovery, insight generation and insight explanation — is done to not only help everyone better explore, analyse, understand and act on data, but to also transform, democratise and automate the use of data for all types of users.
There’s an emphasis on ‘all’ users here, as the augmented analytics approach is designed around automating analysis processes previously typically found in specialised data science and machine learning (DSML) products. These were usually IT-led and geared toward specialists, which made augmented techniques largely inaccessible to the larger business population.
In the last few years, the proliferation of visual-based data discovery tools has seen AI and machine learning capabilities increasingly and directly incorporated within analytics and BI platforms to assist the business user specifically, rather than just data experts. This has brought together data, analytics and DSML, where they were once considered and managed separately.
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