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Keeping Data Analytics Human


In an era where natural language processing (NLP) and large language models (LLMs) are making significant strides, there's a burgeoning narrative that machines might soon outrun human capabilities in various fields, including data analytics. Yet, despite this impressive technological progress, human judgment remains not just relevant, but critical across the data analytics spectrum. This article delves into the 'how' and 'why' of human involvement's enduring significance.


Analytics refers to the systematic use of data and quantitative methods to support informed decision making.  It goes through iterations of the same 6 steps as highlighted, to help users gain incremental knowledge by asking the next better question.  We will now explore the steps where Human involvement or AI can play a greater role. 

Framing the Problem

The start of any analytical process is with the definition of the problem – a step that's inherently human. Machines can churn data, but it's the human analysts who frame the questions that guide the analysis. They bring an understanding of business goals, cultural nuances, and the subtlety of human behaviour which are beyond the purview of even the most sophisticated algorithms.


Hypothesis Development

Next comes the development of hypotheses. Here, human ingenuity is irreplaceable. It involves creativity, domain knowledge, and the ability to draw on diverse experiences – facets that are currently beyond AI. This stage of the analytics process is as much an art as it is a science, where human intuition plays a pivotal role in formulating assumptions that AI would typically not conjecture.


Data Collection & Curation

While AI excels in gathering and processing vast datasets, humans oversee the crucial aspects of data quality and integrity. It's the human analyst who ensures that the data collection methods are ethical, that personal biases are identified and mitigated, and that the data accurately represents the reality it's supposed to model.


Interpreting Results

The interpretation of analytical outcomes often demands a nuanced understanding of socio-economic factors and an anticipation of how results might impact different stakeholder groups. These are areas where emotional intelligence and the ability to gauge the broader implications come into play – aspects that machines do not possess.


Communication & Influence

Lastly, the culmination of the analytical process is the communication of findings. Here, humans translate complex data into compelling narratives. This process of storytelling, persuading, and influencing action based on analytical findings is a uniquely human skill set, one that machines cannot replicate.


The Way Forward

The future of data analytics is not about choosing between human intelligence and artificial intelligence, but rather about the symbiosis of both. The goal is to leverage the computational power of AI to augment human judgment, not to replace it. As we navigate through the torrents of data, the human element – with its judgment, intuition, and ethical considerations – remains the compass that guides decision-making.

In conclusion, human judgment in data analytics isn't just surviving in the age of NLP and LLMs; it's thriving. As technology evolves, the analytics field must continue to cultivate human expertise to steer the course of this ever-more-data-centric world.

If you are interested in refreshing or building your data analytics skills in the age of AI, check out our upcoming virtual workshops here.

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