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How AI Analytics Augments Human Work

How AI Analytics Augments Human Work


How AI Analytics Augments Human Work

How AI Analytics Augments Human Work
By Pete Reilly


The objective behind any analytics platform is to enable users to make better data-driven decisions. Through combinations of visualizations and insights, analytics platforms explain and illustrate data for the end user.

The rise of AI has changed the scope of analytics capabilities to the extent that human work can be augmented in significant ways.

Let’s discuss the broader implications of AI analytics and how it can shape the role of both technical and non-technical employees in the workplace.


Augmentation Through Automation

One of the most important advantages of AI in analytics is automation. While AI-driven automation may inspire images of robots on an assembly line, the impact of such automation in analytics may not be so vivid.

AI automates analytics workflows with machine learning algorithms. These algorithms can analyze an entire data warehouse and test every data combination to uncover key drivers and surface hidden insights. 

These algorithms work to understand why the business operates as it does. For example, machine learning algorithms can pinpoint which factors contribute to and detract from key performance indicators (KPIs) and to what degree. They can also forecast performance based on past trends and project hypothetical scenarios with different input values to answer “what if” questions.

Perhaps most significantly, AI can perform this time-intensive analysis quickly, in seconds.

In contrast, data analysts without AI would have to perform this analysis manually. A question like “why are costs increasing?” could take days or weeks to answer.

In the current state, analysts must determine hypotheses and cyclically test them, iterating on analysis by merging and filtering data until they have enough evidence to present a story to business users. This process is time-consuming and repetitive. Pressed with hard deadlines and bottlenecks of reporting requests, analysts simply don’t have the time to be completely thorough in their analysis

Often, analysts don’t even have time to evaluate their biases, meaning that the results of their analyses may be skewed from the outset.

And since analysts possess the technical skills necessary to access and interpret data, they must also explain the results of the analysis to the business users who ultimately make strategic decisions. This dynamic puts analysts in a difficult spot. Business users haven’t been involved in the analytics process and don’t have the context of the analysts’ assumptions. Plus, business users may have follow-up questions that require additional analysis.

Of course, by the time the analyst can get to those follow-ups, the business has moved forward, and the answers may no longer be relevant. Business users act on partial information or skip analytics entirely.

That’s why AI analytics offers such a compelling advantage. Machine learning starts augmenting human work by removing the burden of routine analysis from analysts and putting answers directly in the hands of business users.


How does this impact the role of the data and analytics professional?

The reality is that cyclical reporting doesn’t leverage the full skill sets of data analysts. The kind of high-value work that can move the needle on business outcomes is often buried under ad hoc requests, day-to-day reporting, and the needs of the business users.

With AI analytics, data and analytics professionals have an opportunity to automate analytics workflows and lead big-picture initiatives across their organizations.

Specifically, these professionals can focus their time on tasks like:

  • Developing machine learning models for custom analyses and integrating them into an openly extensible analytics solution.
  • Optimizing the data warehouse and pipeline.
  • Centralizing an AI analytics solution and enabling self-service functionality for business users.
  • Managing data and analytics best practices across the organization.
  • Operationalizing their work and eliminating shelfware.

In this sense, AI analytics works in tandem with data and analytics professionals to put their work into practice. It’s not just about saving time and increasing efficiency; AI analytics is a conduit for data pros to shape the production of meaningful insights across an organization.

Likewise, business users receive answers to their questions in a timely manner. AI analytics leverages natural language processing to allow users to ask questions in plain English and natural language generation to produce insights in an understandable fashion. As such, business users can ask questions as they occur, dive deeper with follow-ups, and receive answers that make sense without interpretation from an analyst.

The ability for business users to ask their own questions without a delay in answers inspires curiosity and reinforces the value of asking in the first place. In a sense, the augmentation that AI provides is about eliminating roundabout workflows that stifle human brainstorming and enhancing natural thinking patterns. 

Ultimately, AI analytics can make work more human.

AI Analytics Shifts Work to Ideation and Execution

Every analytics workflow begins with identifying KPIs for analysis or key questions to be answered.

From there, AI analytics can automate the following steps:

  • Accessing the data model and prepping the data
  • Building and publishing dashboards and views
  • Conducting root cause analysis
  • Interpreting the results of the analysis, developing stories, and sharing findings with decision-makers

The remaining steps in the workflow account for ideation and execution: making decisions, taking action, and measuring the impact of results. 

When AI performs the labor of analysis and explains the output, end users can spend the majority of their time on these three critical steps. 

Machines excel at tasks like computation, classification, and clustering. Humans excel at creativity, at setting strategy and executing initiatives based on the insights they receive.

AI analytics augments workflows such that ideation and execution become the cornerstones of a role. With better, faster insights in hand, employees can make smarter decisions, manage risks, and try new initiatives.

This augmentation alleviates the drudgery of repetitive analysis to enable humans to focus on the tasks where humans shine— tasks that are generally more creative, enjoyable, and fulfilling.


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