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

Algorithms can be highly effective at uncovering hidden patterns and insights within huge volumes of data. Specialists have been using algorithms to solve business problems for years, but…

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? …

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

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

Every Business Intelligence (BI) and analytics vendor is integrating a form of artificial intelligence (AI), machine learning algorithm (ML), and natural language generation (NLG) into their products. ‘Augmented analytics’, is the hot new topic and full of hype right now, but can it fix the fundamental flaw that has plagued BI tools for decades — adoption?

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…