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Self-service analytics is perhaps one of the most powerful tools organizations have to enable widespread data-driven decision making. The goal of this business intelligence (BI) branch is simple – enable line-of-business professionals with query and reporting capabilities to perform citizen development and analytics without significant support from IT (Gartner, n.d.). The first wave of self-service analytics relied on simple-to-use BI tools such as PowerBI, Tableau and Google Data Studio (now Looker), enabling business users to connect to and access organizational data, explore relationships within and across different datasets and pull together simple dashboards to visualize key performance indicators (Shea, 2023). With time, the evolution of such tools has become imminent as descriptive analytics has become second nature for well-nurtured citizen analysts. Line-of-business professionals will most likely never run out of variations of the “what happened?” question but it is only natural that more advanced questions – requiring equally as advanced analytics techniques to be answered – will start being posed.

Although drag-and-drop tools to deploy citizen data science exist, there is a new kid in town. Generative AI tools have the ability to help parse meaning from vast amounts of data at scale, generate bespoke codes (Sankaran, 2023) and help alleviate the burden of repetitive or mundane tasks in the analytics lifecycle (Porter, 2023). The novelty of the technology and its natural language processing interfaces only makes the imaginable impact potential cosmic. Examples of how generative AI is adding value to analytics processes are already widespread. For instance, GitHub reports that their copilot solution has been signaled to aid developers to perform tasks around 55% faster than without the solution (Kalliamvakou, 2022). One can further speculate what this number would be if accurate and user-friendly versions of generative AI analytics tools would land in the hands of less data-proficient user bases. With a positive outlook, I like to imagine the number could reach high levels.

The potential of implementing generative AI tool in self-service analytics is, thus, great. That being said, for anyone working in analytics and BI branches of IT, empowering the entire workforce with accessibility to build advanced analytics solutions with generative AI, sounds like a nightmare. As highlighted by Shea (2023), such a system “(…) without the right tools, technology, and processes, it is scary and can devolve quickly into data chaos”. Consequently, governance, becomes one of the big concerns for organizations worldwide. In fact, in a survey performed by Evanta, 46% of Chief Security Information Officers report that their biggest concern is either data privacy, or secure deployment of solutions (Hiestand, 2023). Alongside governance, other challenges overshadow potential large-scale applications of generative AI in citizen analytics practices. The accuracy and reliability of outputs remains a concern as evidence from practical use showcases that solutions incorrectly construct outputs, also referred to as hallucinations (Porter, 2023). Most students who have tried to generate references using Chat GPT, for instance, will recognize this as a concern. Such hallucinations can have significant impact on the accuracy and potentially mislead line-of-business professionals in conclusions. There is therefore a real need to develop approaches to safeguard utilization of such tools in business intelligence.

Personally, I believe that generative AI is still far from ready to be scaled in organizations. I do, however, feel immensely optimistic that businesses who are able to deploy small scale applications with proper governance and guardrails in place will have superior advantages. This, especially in building systems to support every-day use in self-service analytics.

References

Gartner. (n.d.) Self-service Analytics. Gartner Retrieved October 14, 2023 from:  https://www.gartner.com/en/information-technology/glossary/self-service-analytics#:~:text=Self%2DService%20Analytics%20is%20a,own%2C%20with%20nominal%20IT%20support.

Hiestand, L. (2023). The Current CISO Outlook on Generative AI. Evanta. Retrieved October 14, 2023 from: https://www.evanta.com/resources/ciso/blog/the-current-ciso-outlook-on-generative-ai

Kalliamvakou, E. (2022). Research: quantifying GitHub Copilot’s impact on developer productivity and happiness. GitHub. Retrieved October 15, 2023 from: https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/

Porter, T. (2023). Generative AI for Data Analytics: A Force Multiplier. Alteryx. Retrieved October 15, 2023 from: https://www.alteryx.com/blog/generative-ai-for-data-analytics-a-force-multiplier

Sankaran, A. (2023). How Could Generative AI Impact The Data Analytics Landscape? Forbes. Retrieved October 15, 2023 from: https://www.forbes.com/sites/forbesbusinesscouncil/2023/05/24/how-could-generative-ai-impact-the-data-analytics-landscape/

Shea, B. (2023). Everyday AI: Next-Generation Self-Service Analytics. Capgemini. Retrieved October 15, 2023 from: https://www.capgemini.com/insights/expert-perspectives/everyday-ai-next-generation-self-service-analytics/

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The dexterities of generative AI applications, like Midjourney, DALL·E and stable diffusion enable users to explore vast multimedia spaces layered and powered by an equally vast amount of data (Epstein et al., 2023). Simultaneously, they have stimulated discourse in the arts, encouraging opposing opinions on whether such large language models bring about more benefits or drawbacks for creative workers. In 2016, Microsoft (2016) presented one of the first examples of what artificial intelligence could mean for the world of art – a machine learning algorithm trained on works by Rembrandt, that further generated a piece of art? I intentionally end my previous sentence with a question mark, hoping to further explore how the notion of aesthetics and style subsist in the world of computer-generated artistic pieces.

The team behind the new Rembrandt project referred to the generated piece as “(…) a visualization of data in a beautifully creative form” (Microsoft, 2016). However, a big shift has occurred since 2016. The images we generate through large language model driven tools now, unless specifically instructed to use a certain style, will suffer from aesthetic merging. Put simply, the style is either similar, or very much the same. And there is a good reason for that. The newer versions of AI generated art will never start with a tabula rasa, the synthetic content, rated by users, is already being utilized to train future models. This, in turns, creates what Epstein et al. (2023) refer to as a “(…) self-referential aesthetic flywheel that could perpetuate AI-driven cultural norms” (p. 6). This is something one can quickly observe by trialing one of the tools, something I set out to do. My idea was simple, I wanted stable diffusion to create a fictional creature, not bound by any sense or a priori knowledge, the prompt – evidently generated by my best friend ChatGPT – was as follows:

Generate an imaginative and unique image of a fantastical creature that defies preconceived notions of conventional objects. This creature should be a harmonious fusion of the following elements: the body of a car, a dog, a green stick, and a red circle. Let your creativity run wild and craft an image that transcends traditional boundaries and expectations.

I am in no way blown away by the results. In fact, it feels like something I have seen what now feels like one too many times before, as if I already knew the aesthetic that was going to meet me on the other side of my prompt. In a recent talk about the impact LLM has on art, Ph. D. candidate at the Trondheim Academy of Fine Arts, Martinus Suijkerbuijk referred to one of the major threats to the art industry as cultural Darwinism (M. Suijkerbuijk, personal communication, 26 September 2023). A notion referring to the nature of versioning in large language models and the existing aesthetics convergence, that in a way, imposes style on content. Creative expression and intention that is extenuated by the human process in traditional artforms, slowly fades away in a sea of synthetic images. However, as illustrated very well by my failed attempt to create a piece of art that could blend everyday objects into an abstract and unbound mélange, there are certain parts of my imagination that AI still cannot access and that I still cannot explain using natural language. Therefore, what remains the question for digital artists rather becomes is how they can exert artistic intention and their own notion of style into the creative process. 

References:

Epstein, Z., Hertzmann, A., Herman, L., Mahari, R., Frank, M., Groh, M., Schroeder, H., Smith, A., Akten, M., Fjeld, J., Farid, H., Leach, N., Pentland, A.  & Russakovsky, O. (2023). Art and the science of generative AI: A deeper dive.

Microsoft (2016, April 13). The Next Rembrandt. Retrieved September 29, 2023 from https://news.microsoft.com/europe/features/next-rembrandt/