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?

Self-driving vehicles, weather forecasting drones, fulfilment robots and robotic surgery are already transforming the lives of millions of people. It is deep learning computer vision (DL CV) — visual sensors coupled with the ability to make instantaneous, human-like sense out of streaming video — that make these applications possible. One might think that acute focus on DL CV applications would be sufficient to yield the necessary breakthroughs and successful industry applications.

Artificial intelligence became one of the hottest tech topics in 2017 and is still attracting attention and investments. Although scientists have been working on the technology and heralding its numerous anticipated benefits for more than four decades, it’s only in the past few years that society’s artificial intelligence dreams have come to fruition.

Qlik is at the forefront of bringing augmented intelligence even further into analytics, helping business users scale their ability to explore and surface key insights from all their data. As we expand the role and use of analytics through our customer organizations, we know that making it easier for users to interact with data is essential to both increased adoption and higher data value.

Data is the lifeblood of AI. An AI system needs to learn from data, as well as from humans, in order to be able to fulfill its function. Unfortunately, organizations are already struggling to integrate data from multiple sources to create a single source of truth on their customers, products, or other data. AI will not solve these data issues, it will only make them more pronounced.

The next generation of BI will heavily integrate machine intelligence and AI – and how these technologies are embedded and used will matter. The best solutions will support human-centered analysis, which takes advantage of new technology while not losing sight of value of the human perspective in decision making and analysis.