AI Analytics Guide: Better, Faster Data Insights



The incorporation of artificial intelligence into business analytics has enabled advanced automation in the data discovery process. The result — AI analytics — performs the work of a data analyst without the same human limitations.

AI analytics can augment the workforce so that both analysts and business people can receive better, faster data insights that are more thoroughly researched and actionable than ever before.


Learn more about AI analytics with this guide.

What is AI Analytics?


AI analytics refers to a subset of business intelligence (BI) in which software exhibits behaviors typically attributed to humans, such as learning and reasoning, in the process of data analysis.

In practice, this means AI automates the steps that humans would take to complete analysis in an exhaustive fashion. AI can test every possible data combination to determine hierarchies of relationships between different data points— and it can do so much faster than a person could.

The sheer amount of labor that can be accomplished with AI analytics is staggering.

If the goal of analysis is to better understand data so that businesses can act accordingly, AI analytics is like a team of data scientists working around the clock, answering questions on demand with unparalleled speed and depth.

Before we launch into the details, let’s break down AI and its role in the current state of BI to put this conversation into context.


AI analytics makes it easy to break down sales value analysis.

A Brief Introduction to AI

AI has become something of a buzzword in both tech and marketing spaces and frequently appears in news headlines. With all the noise surrounding the term, it can be difficult to truly understand what AI is and how it’s practically applied in the business world.

AI refers to any system or machine that exhibits qualities of human intelligence. Such a vague definition means that the bar for AI is constantly moving as new technology develops.

Learn more about the history of AI with this timeline.

For example, AI has evolved from winning chess games against grandmasters to winning increasingly complex games like Go and Starcraft against pros. To do so, AI must match and exceed the ingenuity and problem-solving capabilities of its human opponents all while playing without human input or intervention. In other words, AI is capable of acting out fully realized human potential in certain scenarios— and these scenarios are constantly growing in number.

Likewise, AI offers the same benefits in business.

Another important term that’s relevant to AI analytics is machine learning. Machine learning is a subset of AI in which a machine is fed vast amounts of data and learns how to recognize patterns.

Machine learning algorithms that are employed in AI analytics are very powerful; they can parse through the incredible amounts of data that enterprise companies accumulate and identify the key relationships that drive business.

We’ll discuss these impacts, and more, in the next section.

Common Terms in Machine Learning


The State of AI in Business Intelligence

Want to learn more about AI analytics? In this eBook, we break down how AI is currently being implemented into business intelligence platforms and what to look for in an analytics solution.

Learn how to discern the value of different AI implementations and what to expect as the market develops in accordance with advanced, autonomous analytics.

Learn more about AI in BI and how it impacts AI analytics.

AI Analytics VS. Traditional Data Analytics

To understand the impact of AI analytics, it’s important to draw a comparison with data analytics in its current state.

For many businesses, data analysis is a drawn out process that’s relegated to technical teams of data analysts. These teams test their hypotheses against the data and generate reports for business people, who then ask follow up questions or act against the information in the report.

In practice the process looks something like this:


1. Sales are down.

2. Data analysts work to determine why sales are down by forming hypotheses, such as:

  • Sales are down because our competitor is gaining market share.
  • Sales are down because the weather has been bad.
  • Sales are down because our brand messaging is off

3. Analysts test the data against these hypotheses until they find enough evidence to support or dispute their claims.

All of these steps are essentially trying to answer the question, “why are sales down?” This process can be extensive, meaning that a business may not receive the answer to their question within the optimal timeframe to act.

AI dismantles this entire process. If we ask AI the same question, it won’t start with any assumptions. Instead, AI will query the data directly. AI understands that “sales” is the primary metric that matters in this question, and it will exhaustively comb an entire data warehouse to determine what’s actually driving the decline.

In this sense, AI is less biased than a person would be in its approach to research. Plus, it can test far more possibilities in a shorter amount of time. It may determine the same outcome (let’s say that all of the previously mentioned hypotheses are correct), but it will also be able to decipher the degree to which each driver impacted the decline.

This means that a data analyst would be correct to say that sales were negatively affected by weather. But AI could tell you that the weather’s impact was miniscule compared to the effect of declining brand penetration. For the business person, fixing the brand is what actually matters in their day-to-day workflow.

Because of the extensive work that AI can perform, AI analytics can be self-service, meaning that business people can use these tools directly without help from an analyst (which means analysts can offload time-consuming and repetitive tasks like routine reporting).

Learn more about AI analytics.

Traditional Data Analytics:

  • Analysis requires humans to test their hypotheses
  • Reports on commonalities in the data, such as trends
  • Used by data analysts and/or data scientists
  • Hours-, days-, or weeks- long process

AI Analytics:

  • Analysis is led by the machine, which can test
    an infinite combination of hypotheses
  • Determines causes behind commonalities in the data
  • Enables business people, as well as data analysts
    and scientists, to find insights
  • Takes seconds
Learn more about AI analytics.

Traditional Data Analytics:

  • Analysis requires humans to test their hypotheses
  • Reports on commonalities in the data, such as trends
  • Used by data analysts and/or data scientists
  • Hours-, days-, or weeks- long process

AI Analytics:

  • Analysis is led by the machine, which can test
    an infinite combination of hypotheses
  • Determines causes behind commonalities in the data
  • Enables business people, as well as data analysts
    and scientists, to find insights
  • Takes seconds


AnswerRocket

AnswerRocket: An AI Analytics Platform


AnswerRocket is an advanced analytics tool that is currently helping business people make data-driven decisions, fast. AnswerRocket’s advanced analytics offers features like:

Automation

Automate analysis of KPIs, breaking down which metrics most impact the results on the surface in the order of their importance.

Industry

Understand data based on industry- and function-specific analysis with out-of-the-box algorithms tailored to employee roles.

Forecasting

Explore “what if” scenarios by testing how changes to certain metrics will impact related KPIs.

Insights

Automatically uncover important insights highlighting hidden trends, anomalies, opportunities, and predictions.

Why is AI Analytics so Important?


The ultimate goal of an analytics platform is to help business people make data-driven decisions. These platforms facilitate this decision-making process by translating business data into visualizations and insights that provide a launching pad for the user to take action.

AI plays into this relationship by automating the process of analysis and excavating deep insights. The impact of AI is twofold because it:

  1. Drastically increases the speed of analysis, enabling business people to get their questions answered immediately while simultaneously decreasing the amount of labor it takes to get them.
  2. Provides answers that are generally more comprehensive, targeted, and meaningful than a person could generate in the same timeframe.

Of course, these benefits raise plenty of larger questions, such as:

  • How can we trust the answers that AI is providing?
  • Can AI really replicate human work?

Let’s tackle each of these concerns.

Why is AI analytics so important?

How Can We Trust the Answers AI Provides in Analytics?

Transparency is critical to successful AI adoption.

After all, AI generally operates behind-the-scenes. As AI analyzes data, the user isn’t necessarily seeing the process occur. They just receive an answer once the analysis is complete.

Coupled with the short turnaround time between asking a question and receiving an answer, it’s reasonable for people to wonder about accuracy.

Different roles at a business will also have different frames of reference for data analysis. A data scientist who sees the machine learning algorithms that are invoked may implicitly understand what’s occurring. Further, since they have first-hand access to the data itself, they can verify the accuracy of the data.

In contrast, a business person isn’t used to the frontline of the research; for someone in these roles, they’re suddenly losing the person who provides results and are being asked to trust a machine that’s leveraging algorithms outside of their purview.


Open source software like AnswerRocket allows users to invoke Jupyter Notebook.

It’s important that business teams and data teams are aligned on AI from the beginning. With open source platforms, data scientists can actually see how machine learning algorithms are being employed and write their own algorithms to perform custom analyses.

These algorithms are verifiable evidence of AI’s accuracy.

In other words, business people can trust the data scientists who typically provide them with answers when they validate how AI analytics works.

Further it’s worth noting that AI’s approach to data analysis is not as biased as a human approach.

AI does not look at data with assumptions or preconceptions— a state of “mind” that’s impossible for even the most objective person. In this sense, AI can be even more accurate than humans in its analyses.


The State of AI in Business Intelligence

AI is a key component in advanced analytics. In this eBook, we break down how AI is currently being implemented into business intelligence platforms and what to look for in an analytics solution.

Learn how to discern the value of different AI implementations and what to expect as the market develops in accordance with advanced, autonomous analytics.

Learn more about AI in BI and how it impacts AI analytics.

Can AI really replicate human work?

AI analytics are still a relatively new concept in business. Autonomous machines can be unnerving to employees — from both the business and data sides.

Many may fear that their jobs are at risk. This fear can slow down the adoption process as employees push back against automation, deprioritize it, and otherwise see AI as an opposing force.

Still, others may be concerned that autonomous analytics sacrifices the human touch, that there’s a level of ingenuity in human research that is simply outside the scope of machines.

However, AI analytics provides an enormous opportunity for employees. The kind of labor it automates is generally the most painful and unfulfilling steps in a workflow.

AI analytics:

Eliminates the slog of routine reports.

With fast, in-depth answers that pinpoint the information that’s most relevant and important, business users can start acting seconds after they ask a question. Likewise, data scientists can spend their time building statistical models and otherwise leveraging their advanced degrees instead of generating the same reports over and over again.

Inspires curiosity.

With quick answers, AI analytics supports active brainstorming. Business people can ask about performance of KPIs and immediately follow up on any interesting points, leading to active data discovery. Curious about market share? Ask! Want to know what would happen if you changed x, y, and z? Ask!

Promotes creativity and agility.

With AI managing the heavy data lifting, business people don’t have to wait for Q2 to understand what happened in Q1. This means that business people can test creative campaigns and understand quickly what’s working or what’s not, helping to mitigate the risk of new ideas and products.


In this sense, AI analytics is an augmentation of the workforce.

This augmentation allows people to work more effectively on the tasks that are more enjoyable, engaging, and fulfilling.

As such, AI is even better suited to the repetitive and time-consuming steps that lead to insights generation because it doesn’t tire of methodical labor. It “replicates” human work in that it enhances work.

AI’s ability to automate insights frees up time and labor, allowing business people and data scientists alike to practice their creativity and problem-solving skills in their day-to-day roles.


AI analytics augment the workforce to be more effective.

Schedule a Demo of Our AI Analytics Solution

Schedule a Demo of Our AI Analytics Solution