The Human Factor in Data Analytics


Science Fiction movies often feature a computer that thinks for itself, processing volumes of data, making predictions, forming judgments, and then taking actions to achieve an outcome. In other words, artificial intelligence mimicking human actions. It is important to realize that automation is susceptible to a loss of insights without an experienced human analyst. Harmonizing human decisions with machine learning is key to unlocking insights through analytics. An augmented intelligence approach can leverage both human and machine learning, and analytics efforts are best served when harmonizing the two.

A recent HBR article, The Simple Economics of Machine Learning, summarized that all human activities can be described by five high-level components:

All human activities can be described by five high-level components


As the cost of the platform elements of data acquisition and predictive analysis plummet with cloud computing and storage, we are seeing an increased value of the higher level function — human judgement. Digital transformation expert Eric Action recently posted in Artificial Intelligence: Teachers and Students, “We should not be concerned about the machines taking over the world and rendering us humans useless. The machines are our students. They need to learn, and we are the ones to teach them.”

Combining the speed and processing power of a computer with a human’s interpretive capabilities leads to superior results. This is Augmented Intelligence in its basic form — computational algorithms helping to put the human end user in the best position to make an informed decision. Algorithms read and analyze data to show the “who”, “what”, “when” and “where”. A human then uses this intelligence to determine the “why” and chooses the appropriate course of action.

An increased reliance on human background knowledge, intuition, and deliberation creates a greater demand for data exploration processes that leverage user expertise from across an organization. This function has traditionally rested with data scientists in the organization. However, this IEEE research shows promising results from making front line workers “citizen scientists”. Increasing usability places a demand on visualization, as it is one of the most effective ways for humans to process information.

Usability is further enhanced when visualizations are customized, annotated, and shared with colleagues. Collaboration on analytics provides many benefits:

· Speeds the time from data to outcome, using social interaction to speed decision-making.

· Opens communication, identification of insights, and creation of action plans helps align strategies. This keeps everyone on the same page.

· Enables organizations to extend the analysis to a greater portion of the enterprise so timely and useful insights can be added by other team members.

· Increases information visibility across the organization.

· Improves organizational cohesion.

Simply put, teams can make better decisions by working collaboratively, rather than working independently and linearly.

We believe that the best analysis results when data is presented to humans in a form that is easy to digest. That’s why we built Stratifyd Signals™, an enterprise Business Analytics platform enabling a seamless transition from data to outcome through augmented intelligence.