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 your business data culture. Today, let's take a look at how AI and machine learning (ML) can close that adoption gap.

We decided to pursue a regional growth strategy quite early on. As an Australian company, we believed it was necessary to look overseas to grow our business quicker. We knew it wasn’t going to be easy, but there were three important lessons that we didn’t expect to learn when we set out overseas.

In the first blog 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 analytics 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?

Software vendors that are looking to accelerate their path to AI need to take advantage of the AI already in analytics platforms. Gartner believes that the future of analytics is augmented. That is, analytics will be AI-driven and all end-to-end use cases will be automated. I also believe it won’t be long before analytics is no longer on our desktops - instead it’ll be embedded in applications.

If, as we saw in part one 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?

Can we fix the plague in analytics with AI? 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?

I often get asked why Yellowfin decided to go global. While the Australian market is relatively large, it’s also quite risk averse, which makes it a challenging market to sell into. While VC-backed software vendors have the benefit of selling to others within their VC family, as a bootstrapped startup we had to forge legitimate markets outside of Australia. We knew that we had to spread our wings to grow and expanding overseas quickly gave us the opportunity to sell faster.

Recently, I read why companies are failing in their efforts to become data-driven in the Harvard Business Review. It said that 72% of Chief Data Officers believe their organization doesn't have a data culture and 92.5% of them blame their people. But I think they’re wrong. The real issue is that people aren’t using the tools that their CDOs have bought for them. That means the problem isn’t with the users, it’s with the technology they’ve been given.