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.
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. Generative AI refers to AI machines that can generate new content.
AI offers businesses many advantages over manual labor by automating mundane tasks such as data entry and customer service. AI can help businesses process information faster, more accurately, and more efficiently than ever before. By freeing up employees from mundane tasks, companies can focus their attention on more important business objectives. In addition to these advantages, AI opens up possibilities for businesses to explore new markets and develop innovative products.
Artificial Intelligence Milestones
1956
“Artificial Intelligence” is coined by John McCarthy at Dartmouth.
1966
ELIZA, the world’s first chat bot, developed by Joseph Weizenbaum at MIT.
1981
DEC employs AI to help configure orders for new computer systems, saving $40 million a year by 1986
2008
Google app with speech recognition appears on the iPhone.
2015
ImageNet challenge declares that computers would more accurately ID objects in visual data than humans.
2018
OpenAI releases GPT-2, a natural language processing model that is capable of generating coherent and convincing text.
2019
OpenAI releases GPT-3, an even more advanced natural language processing model that can perform a wide range of language tasks, such as translation, summarization, and question answering.
2020
The emergence of AI Systems that can detect and diagnose COVID-19 from medical images, demonstrating the potential of AI in healthcare.
2021
The emGPT-3 is used to develop new AI assistants, which can help people write, create audio and video content, generate images, write code and more in a fraction of the time.
AI is used to develop autonomous vehicles that can navigate complex environments and make real-time decisions to avoid accidents.
2022
OpenAI launches ChatGPT on November 30th, breaking all records for adoption of any online service with one million users in 5 days.
2023
OpenAI launches GPT-4 in March which can understand photos and video inputs.
For example, AI has evolved from winning chess games against grandmasters to complex Large Language Models that are capable of communicating and even completing tasks just like humans. To do so, AI must match and exceed the ingenuity and problem-solving capabilities of its human opponents, all while functioning 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.
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 Definitions in Machine Learning
Supervised Learning
The data used to train a machine learning algorithm has been labeled for the machine. For example, a person would indicate whether a picture featured a cat or a dog. The machine would analyze tons of these pictures and learn what attributes make a cat a cat and what attributes make a dog a dog.
Unsupervised Learning
The machine receives a vast amount of raw data and, without human input, identifies hidden structures and patterns to group things together. Unsupervised learning often beats human performance.
Reinforcement Learning
The machine is trained via a reward system. When the machine identifies a correct causal factor, the behavior is reinforced so that the machine learns to apply the same reasoning to different scenarios.
ebook
The State of AI in Analytics & 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.
AI Analytics vs Traditional 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 and acts as an AI assistant to the business user. 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).
traditional data analytics
ai analytics
AnswerRocket – An AI Analytics Tool
AnswerRocket is an advanced analytics tool that is currently helping business people make data-driven decisions, fast. AnswerRocket’s advanced analytics offers features like:
AnswerRocket’s AI is designed to help businesses extract knowledge and insights from their data.
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:
- 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.
- 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.
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.
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.
ebook
The State of AI in Analytics & 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.
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 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!
In this sense, AI analytics is an augmentation of the workforce. Think of it more as an AI assistant than an AI replacement.
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 act as an assistant 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.
