Data is booming. Analytics solutions are innovating rapidly to keep up with the influx. At the forefront of that innovation is AI.

Since AI analytics is a relatively new technology, it can be difficult to discern its true benefits or even the extent to which it deviates from more traditional analytics. Let’s dive into the essential differences.

1. Traditional analytics is static. AI analytics is dynamic.

Traditional data analytics typically relies on dashboards composed of visualizations. These dashboards are based on common business questions and are predefined well in advance. Answering a new question requires time and technical skills, usually multiple days (or weeks) and assistance from a data analyst or scientist.

These dashboards are static and can’t adapt to the changing needs of the business, as new questions and challenges that arise can’t always afford to be put on hold.

AI analytics, in contrast, allows users to dynamically request and combine information to answer business questions— without technical assistance. 

AI systems that support a conversational interface allow users to ask questions in natural language, via natural language processing. That means users can ask questions like “how did our brand perform last quarter” and receive an answer, also in natural language (via natural language generation).

This process is much more akin to the consumer experience. Just like asking Siri about the best local restaurants, business users can ask questions of AI to better understand their business quickly and without technical assistance.

2. Traditional analytics answers “what.” AI analytics answers “why” and “how.”

Both traditional analytics and AI analytics attempt to answer core business questions like “why are sales up?”

AI analytics can answer these “why” questions directly. Traditional analytics answers a series of “what” questions and leaves the user to determine “why” through their own analyses. 

Dashboards can present the facts of the situation— what sales were, whether they changed or not, etc. But dashboards can’t interpret these answers or put them in context with each other.

Instead, users download data from dashboards into spreadsheets. From there, users sort and filter the data and test various hypotheses to try to understand what’s driving the change in sales. Technical team members like data analysts or scientists may assist with this process.

When users hit deadlines, they do their best to formulate an answer and create an action plan. The process of analysis takes up the majority of their time, as opposed to developing and executing the action plan. These circumstances make it difficult for business people to take advantage of opportunities or deal with challenges. They simply don’t have the time or resources to act quickly and efficiently 

Or, users forgo analysis entirely due to the time it takes and the insufficient answers they receive. Employees guess, rely on gut feelings, or maintain status quo decisions.

These challenges with traditional analytics prevent growth and opportunities for cost savings, all while driving up costs as more analysts are hired to support the workflow.

In contrast, AI analytics automates the process of analyses, essentially eliminating the manual work of downloading spreadsheets, filtering the data and testing hypotheses. 

AI applies the appropriate machine learning algorithm to the appropriate problem, automating complete and exhaustive analysis of an entire data warehouse to answer business questions. This process takes seconds, instead of the days or weeks that humans could spend testing hypotheses one after the other.

Instead of producing disparate visualizations, AI generates a data narrative in natural language. This narrative explains “why sales are up” in words that business people can understand. By directly explaining “why,” AI tackles the part of work that machines are really good at (computation, classification, regression, etc.).

This means humans have more time to focus on the action plan, on setting strategy and thinking creatively. Once employees understand the “why,” they can determine which actions will result in meaningful impact.

3. Traditional analytics is driven by hypotheses. AI analytics is driven by data.

As mentioned, dashboards are typically predefined based on common questions or a certain view of the business. These dashboards are inherently biased because they predetermine what’s most important and only show the data that’s relevant to that set viewpoint.

On top of this, answering questions relies on the hypotheses described in the second section. These hypotheses will be influenced by the individual’s experience as well as the limitations of their time and energy. 

In contrast, AI analyzes all the data, producing unbiased answers from exhaustive testing. Allowing the data to lead the analysis, AI won’t miss important insights that are hidden underneath the metrics on the surface.

Of course, biased data can produce biased answers, and it’s important that companies ensure data is as complete and neutral as possible to fully leverage AI applications. Cleaning data to be unbiased is action that should be taken regardless of investments in AI.

But when it comes to what traditional or AI analytics can do with the data, AI analytics can generate comprehensive answers that lead to an action plan, while traditional analytics can display the data. At the end of the day, AI analytics allow business people, not just data analysts and scientists, to make unbiased and informed decisions.

Prior to AnswerRocket, Pete Reilly founded and led Retality, a firm focused on helping companies conceive, build and bring new technologies to market. Before founding Retality, Pete was a founding team member and SVP/GM of BlueCube Software, where he led the Workforce Management business unit before the company was sold to RedPrairie. BlueCube was a spin-out from Radiant Systems, where Pete spent eight years driving the development and market introduction of new products at the company. Pete got his start at Accenture working with Global 2000 organizations. Pete has a B.S. in Computer Science/Economics from Union College in Schenectady, NY.