“AI analytics”—AI for expediting analytics—has grown in popularity recently thanks to awareness brought about by generative AI like ChatGPT. But separating hype from reality in this subfield can be tough. This post tackles 10 myths about this technology to help analytics and business leaders gain a better picture of AI’s strengths and weaknesses when it comes to analytics uses. To learn more about this topic in greater depth, join our upcoming webinar, “Demystifying AI-Powered Analytics: Separating Hype from Reality.”
In This Post
- Myth 1: ChatGPT will replace AI analytics experts
- Myth 2: AI can turn raw data into insights
- Myth 3: Modern analytics already has AI baked in
- Myth 4: LLMs are deterministic
- Myth 5: AI analytics is plug-and-play
- Myth 6: AI-based analysis is always right
- Myth 7: AI analytics is a one-size-fits-all solution
- Myth 8: AI-powered analytics is expensive
- Myth 9: AI analytics requires a complete overhaul of existing infrastructure and processes
- Myth 10: Everything must be tidy before pursuing AI analytics
Myth 1: ChatGPT will replace AI analytics experts
Reality: We are still in the early days of large language models (LLMs) like OpenAI’s popular ChatGPT. Hallucinations (i.e., generating text that is factually incorrect or nonsensical) necessitates a human-in-the-loop approach to utilizing LLMs for analytics to vet outputs. AI can automate certain repetitive and time-consuming tasks in data analysis. But it doesn’t replace the need for human involvement. Analytics experts are vital in defining metrics, formulating questions, interpreting results, and making strategic decisions.
Myth 2: AI can turn raw data into insights
Reality: AI-assisted analytics drives real efficiencies and value for business users, analysts, and experts. However, it is not a “magic bullet” for turning poor data into valuable insights. Today, AI cannot automate data quality, governance, or—critically—the underlying data strategy, encompassing people, processes, and tech for data management. Not all data is equal, and the principle of “garbage in, garbage out” applies to data used for analysis. Moreover, while AI can produce summaries, visualizations, and insights, it cannot replicate the human capacity to effectively communicate complex information through storytelling, which influences decision-making.
Myth 3: Modern analytics already has AI baked in
Reality: Most analytics vendors promoting AI-powered capabilities offer narrow AI implementations that automate specific parts of the analytics process, bolted onto their existing software, which can be seen as “AI- or GPT-washing.” Simply hoping that various analytics tools’ roadmaps will eventually form a cohesive AI solution with your tech stack can lead to missed opportunities for significant speed improvements and transformative changes. It’s akin to replicating legacy processes in the cloud but failing to harness the cloud’s full potential. Consider, instead, actively deciding how to adopt and implement AI in your tech stack. Counterintuitively, most legacy vendors are playing catch-up when it comes to truly augmenting their software offerings with AI, while modern analytics companies have been leveraging AI for years. Rather than settling for half-baked solutions, consider exploring comprehensive AI offerings that are available in the market.
Myth 4: LLMs are deterministic
Reality: LLMs are great at summarization, translation, and generation work. In analytics, this means they are useful for summarizing charts, translating complex Python code into human-legible code explanations for non-technical users, or generating SQL code from human language prompts, amongst many other analytics use cases. But LLMs, by their nature, are pre-trained artificial neural networks on billions of weights, so the same inputs will not necessarily generate the same outputs every time. In some cases, 2 plus 2 may not result in 4. With hallucinations and trust issues arising from that, it is critical that proper guardrails and human-in-the-loop implementations of LLMs are done for analytics uses because accuracy-related errors erode trust.
Myth 5: AI analytics is plug-and-play
Reality: While AI-powered analytics tools have become more accessible, the successful implementation and integration of AI analytics still requires patience and collaboration amongst data and domain experts to maximize its benefits effectively. Ask deeper questions of vendors painting a picture of immediate plug-and-play or suggesting their implementation is as simple as typing a prompt in ChatGPT. Setting up, mapping, and fine-tuning the solution to the business’ unique situation is still a reality. Of course, leading AI analytics platforms also offer numerous automations to shorten time to insights, such as pre-built models, out-of-the-box calculations, templates, quickstarts, and the ability for the system to learn and grow more personalized with time.
Myth 6: AI-based analysis is always right
Reality: AI algorithms excel at processing vast amounts of data and performing complex tasks, yielding valuable insights. However, they are not immune to errors. Issues such as data quality, biases, inadequate training data, incorrect assumptions, and hallucinations can introduce inaccuracies and reinforce societal prejudices. In the realm of data and analytics, accuracy is essential for building trust. Analytics experts possess the necessary expertise to safeguard data integrity, validate results, and provide contextual understanding of findings. Human judgment and domain knowledge play a pivotal role in identifying analysis pitfalls, formulating pertinent questions, and ultimately facilitating well-informed decision-making.
Myth 7: AI analytics is a one-size-fits-all solution
Reality: Implementing AI analytics requires customization and adaptation to specific business needs and goals. Different organizations may require unique AI models and configurations based on specific use case, data, and organizational needs. Some firms, for example, start their AI analytics journey primarily utilizing natural language query to unlock access and ad hoc exploration for non-technical users, others leverage automated insights, and others utilize more predictive technologies like AutoML all at different points and maturity levels.
Myth 8: AI-powered analytics is expensive
Reality: AI analytics, like advanced analytics in the past, may appear daunting or reserved for large global firms that can apply significant investment. However, this is a myth from a time when creating an analytics department demanded substantial resources, including numerous statistics PhDs. Today, the cost of data storage has significantly decreased with low-cost object storage options like S3, ADLS, and GCS; analytics software pricing has become more accessible through SaaS models; and collecting data has become easier. Additionally, open source and low-cost AI and analytics tools further contribute to the affordability of AI analytics, making it feasible for most firms to embrace it in a cost-efficient manner to quickly witness tangible benefits and even surpass traditional analytics setups.
Myth 9: AI analytics requires a complete overhaul of existing infrastructure and processes
Reality: While integrating AI analytics may necessitate some adjustments, it does not require a complete overhaul of existing infrastructure and processes. Leading AI analytics platforms offer the flexibility to integrate rapidly with cloud-based and legacy systems, while also providing scalability to grow as data grows. Just like any technology, AI analytics can—and should—be piloted and proven out, and then implemented so organizations have time to assess the impact while minimizing disruption and risk.
Myth 10: Everything must be tidy before pursuing AI analytics
Reality: You don’t need to have everything perfectly tidy (e.g., MDM, cloud strategy, etc.) to get the most out of AI analytics. While having clean and well-structured data certainly improves the quality of insights, AI analytics can still provide valuable results even with imperfect data. AI analytics is designed to handle noisy and heterogeneous data, making it adaptable to various data environments to identify patterns, trends, and correlations that might be difficult for traditional analytics methods to uncover, even in less-than-ideal data conditions. Your business isn’t waiting for answers.
Separating the hype from reality of AI-powered analytics is crucial for making well-informed decisions about its integration into your analytics stack. By gaining a comprehensive understanding of AI analytics, dispelling common misconceptions, and acknowledging the actual opportunities it presents, you can navigate the current data and analytics vendor landscape with clarity and open eyes.
Throughout this process, finding a balance between automation and human augmentation is vital. Leveraging the right tools and technologies can significantly enhance the efficiency and effectiveness of your analytics processes. Additionally, consider the specific use cases and potential ROI of AI adoption while fostering a data-driven culture. Approach AI-augmented analytics with a realistic mindset, setting the stage for success.
Check out Tellius’ approach to AI analytics here.