Qlik

Over the past few years, numerous analysts have predicted that “Smart Data Discovery” will represent the next wave of disruption in the analytics market. We’ve already seen businesses leveraging artificial intelligence and machine learning to automatically analyse information and generate insights. The potential for this technology to significantly shift analytics to the next level is clear with capabilities such as automated data preparation; natural language processing and generation, advanced statistical analysis of information for pattern recognition, correlation, and outlier identification. However, all this seems almost too good to be true.

The “issue” per-se is that businesses today are focused on driving value right now and delivering on their individual, team, and organisational goals. Additionally, while the volume of data, computing power, and desire of individuals to work with data is increasing exponentially, they are constrained by the data literacy of the wider organisation. This is why a different approach to data analytics with machine intelligence is necessary to deliver true business insights. [CIOS_QUOTES_REPLACE] Business analytics approaches must shift their focus to the end-user and supporting the human analytical process—helping as many people as possible to generate insight and value across an organisation to meet their goals.

The role of the end user

While there is no doubt that machine intelligence offers many new possibilities that will significantly enhance the analytical process, it can never replace user-driven visual analytics, at least not any time soon. So, to drive value now, businesses need smart data discovery capabilities that work in combination with human-centric analysis and exploration to satisfy the full range of users and use cases in an organisation. When machines and people work together, it creates a multiplier effect known as Augmented Intelligence.

Artificial Intelligence vs. Augmented Intelligence

There is an enormous difference between Artificial Intelligence and true Augmented Intelligence. True Augmented Intelligence aims to make people smarter by using machine intelligence without restricting users to predefined questions or analytical frameworks dictated by the machine. Sometimes machine intelligence can point out statistically derived insights that prompt users to look at things differently and ultimately remove bias and broaden knowledge. However, other times users will anticipate and explore ideas using intuition, knowledge, and context that the machine doesn’t have.

The bottom line? Best in class solutions will emerge to enable both machine intelligence and people to work together, to create a relationship where the whole is greater than the sum of parts. There is no doubt that the promise of AI is huge, but to drive true business value and insights from our data, leveraging both human and machine intelligence is essential.