The Impact of AI on the Data Analyst

The introduction of AI, automation and data storytelling to the world of analytics has not only had an immediate impact on the end users of analytics but also the people that work in the field. While many analysts may fear they will be replaced by automation and AI, CEO of Yellowfin, Glen Rabie, believes that the role of the data analyst will increase in significance to the business and breadth of skills required.


Stop shackling your data-scientists: tap into the dark side of ML / AI models

Developing Artificial Intelligence and Machine Learning models comes with many challenges. One of those challenges is understanding why a model acts in a certain way. What’s really happening behind its ‘decision-making’ process? What causes unforeseen behavior in a model? To offer a suitable solution we must first understand the problem. Is it a bug in the code? A structural error within the model itself? Or, perhaps it’s a biased dataset?


Part 4: How machine learning, AI and automation could break the BI adoption barrier

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.


Part 3: How machine learning, AI and automation could break the BI adoption barrier

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?


How to accelerate your path to AI

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.


Part 2: How machine learning, AI and automation could break the BI adoption barrier

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?


Part 1: How machine learning, AI and automation could break the BI adoption barrier

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?