So much of our decision making is made based on firmly held beliefs and stories we have absorbed in our lifetimes. Generally referred to as type one thinking – this is fast, emotional and generally unconscious. It is a type of thinking that is very useful for making day to day decisions like what to wear, what to have for lunch or how to get to work. However, this type of thinking is inherently flawed and full of bias.
Jumping to conclusions has always been considered a bad way to do business (and most things in life). It implies making a rash, poorly considered decision without the facts and without an understanding of the wider implications associated with that decision. But there’s a new wave of technology that is changing everything.
Recently, I read an article by Jill Dyché which was a wrap-up of TDWI Las Vegas. She talked about speaking to an analytics professional who works for a bank and was building analytics on top of their applications. This comment really struck me because it means the bank’s software vendor is missing out on a great opportunity to create an enormous amount of value for their customer and their own business.
In October 2018, we launched two new products into the Yellowfin Suite: Signals, an automated discovery product that discovers critical changes in your data as they happen, and Stories, a data storytelling product which enables users to provide better context to the numbers and create a common, consistent understanding across the organization. What did we do next? Drink our own champagne, of course.
It dawned on me recently that I don’t actually use the Yellowfin mobile app. Like other BI apps, our app essentially replicated the dashboard experience on my phone. But I don’t like viewing a dashboard on my phone, I’d much prefer to look at it on my desktop because the screen is larger. We realized that there’s no point having an app if no one uses it. So we started to think about how people use their phones and set about reinventing our app.
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?
