Image via Yellowfin

How does augmented analytics really benefit your organisation? We break down its many practical advantages in usability, time-savings and value it unlocks across the entire analytics life cycle.

Augmented analytics uses emerging technologies like automation, artificial intelligence (AI), machine learning (ML) and natural language generation (NLG) to automate data manipulation, monitoring and analysis tasks and enhance data literacy.

In our previous blog, we covered what augmented analytics actually is and what it really means for modern business intelligence. In this article, we focus on helping you learn the many practical benefits that augmented analytics can bring to your business, across the three core pillars of the analytics lifecycle: preparation, analysis and insight delivery.

#1 — Augmented data preparation

Image via Yellowfin

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. …

In the last three parts of this four-part series, we have looked at: research on the state of analytics today and the lack of BI adoption; the history of BI and how we have arrived at the augmented era; and the four main blockers to BI adoption that is stunting the growth of data culture. Today, let’s take a look at how AI, machine learning (ML) and automation can close the gap.

How AI-driven technology can close the adoption gap

This is part 3 in a four part series exploring AI and its impact on adoption in BI. Part 1 and Part 2 can be found here.

In the first post of the series, we saw the dire state of analytics adoption. This problem feeds into the low usage and governance of data across organizations. Then, in the second post, we saw how the evolution of BI has brought us to a prime position for augmented analytics. But will this new wave of augmented analytics break through the barriers to BI adoption? …

Part 2 in a four part series exploring AI and it’s impact on adoption in BI.
Part 1 can be found here.

If, as we saw in part 1 of this series, 77% of businesses are ‘definitely not’ or ‘probably not’ using analytics to its full extent and the adoption rate of analytics platforms is an abysmal 32%, something drastic needs to happen. Can the era of augmented analytics with its machine learning and AI fix this adoption issue? To find out, we need an understanding of how we got to where we are with analytics in businesses to date. We need to know the strengths and pitfalls of each era of analytics to know if augmented analytics is really the answer.

How we got to the era of augmented analytics, a short history of BI

1985–2005: IT leads the way

Can we fix the plague in analytics with AI-driven technology?

A Gartner survey note suggested the adoption of analytics products was about 32%. In my previous analytics consulting life, this percentage was a lot smaller.

Clearly, the adoption of analytics is a huge problem to be solved because it highlights just what a small…

Daniel Shaw-Dennis

SVP @YellowfinBI. Passion for technology, AI & growth. Enjoy the occasional keynote and a frequent coffee.

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