AI in Data Analytics: The Future of Business Intelligence
Data analytics using artificial intelligence (AI) and machine learning (ML) has become mainstream in a wide range of industries, including retail, financial services, healthcare, manufacturing, and many others.
With AI and ML, it’s now possible to efficiently analyze extremely large data sets and deliver a more sophisticated level of business intelligence.
AI in data analytics is the future, but to take full advantage of it, organizations must up their commitment to data organization and the development of internal data analytics expertise.
Artificial intelligence (AI) and its powerful subfield, machine learning (ML), are transforming the world of data analytics. Adding AI and ML to data analytics means it’s now possible to more quickly (and repeatedly) analyze large volumes of data — Including unstructured data — and achieve the more sophisticated level of business intelligence needed to drive real competitive advantage.
Considered bleeding-edge until recently, AI/ML projects have now passed a “tipping point” in industry after industry, according to the 2023 Global Trends in AI Report.
An IDC survey of 2000 organizations found that those that have made a strong commitment to AI reported a 39% improvement in customer experience and a 33% improvement in employee efficiency and accelerated innovation.
AI-Powered Data Analysis: How Does It Work?
To put machine learning for data analysis to work you need data — lots and lots of data — and a trained analytical model that keeps learning how to deliver better results.
As for the amount of information needed for AI, an often-cited rule of thumb is to have ten times the amount of data as there are variables in what you are trying to measure.
For example, if you’re trying to predict housing prices and using 1000 variables (location, neighborhood, bedrooms, floors, bathrooms, etc.), you need 10,000 examples. That’s why getting data ready for AI — compiling, organizing, and managing it — is one of the leading challenges for any AI undertaking.
Armed with the right information, the next challenge is to set up an appropriate analytical model — linear regression or decision trees are common options, but there are many — and then put the model to work on a portion of the data.
The goal here is to “train” the model. As it searches for patterns and data linkages, have it learn and adjust its internal parameters (i.e., different weights and biases) to achieve the most accurate possible outcomes.
As an AI model shows promise, it gets tested against larger and larger data sets. Even after it’s deployed, a model keeps learning and adjusting its parameters to stay accurate.
Data Analysis Using AI vs. Traditional Data Analysis
A major distinction between AI-driven data analytics and traditional approaches is AI’s reliance on algorithms rather than rules.
A rule-based approach typically involves the application of an explicitly defined, unchanging logical principle — a rule — that gets hard-coded into the system. For example, a financial institution might have a rule requiring a minimum credit score for certain kinds of mortgages. The assumption is that anyone who doesn’t achieve that credit score represents too high a risk for that specific kind of mortgage.
In contrast to rules, algorithms are more abstract. Instead of dictating a specific action based on a specific variable, an algorithm will provide a set of instructions for handling a wide range of inputs and scenarios. This allows the algorithm to be applied to large-scale, structured and unstructured databases with the goal of discovering previously hidden patterns.
In the case of the mortgage application, AI might be used to profile a subset of customers who lack a good credit score but have other attributes that make them good candidates for a mortgage, simultaneously widening the potential market while keeping risk under control.
AI Applications in Data Analysis
Because they can rapidly sort through vast amounts of information in different formats, such as text, video, and audio, pattern recognition is a primary use case for AI-based data analytics tools.
A typical example would be analyzing photos to single out those displaying a specific architectural feature. Sure, you could hire a person to do this, but a properly trained AI solution can now do it for millions of photos in a fraction of the time.
In addition to finding previously unseen patterns, AI-based solutions excel at identifying anomalies. For example, AI can enhance supply chain management by being trained to detect and send out alerts on inventory outages, shipment delays, or erratic supplier behavior.
Another common use case for AI in data analytics is to build a model that accurately predicts future events. For example, by analyzing data collected from multiple touchpoints — e-commerce sites, mobile apps, store locations, social media platforms, and more — retailers can understand customers on a deeper level and predict their behaviors in a more personalized way.
One of the most beneficial outcomes of using AI is that it makes it possible to develop a single source of truth (SST) – a vetted source of information that is now available across an organization to democratize data for better decision-making.
The Future of AI and Data Analytics
Data analytics has long been essential to business success, and many of the traditional data analytics tools, such as spreadsheets and statistical programs, will continue to have an active role. But as the amount of data at our disposal keeps growing, we need new tools to make sense of it. That’s why AI/ML-based data analytics solutions are now becoming critically important.
A trained, proven, AI-based solution can be used to analyze data at a scale and speed that previously was simply impossible. And once it is set up, it can do this over and over. As a result, AI and data analytics for business opens the door for organizations to become truly data-driven in their decision-making.
But does this simply eliminate the role of the data analyst in data analytics consulting? That’s unlikely to happen anytime soon — if ever. As even this brief overview shows, a great deal of expertise is needed to understand the data sets and implement the models that are at the core of any AI undertaking.
AI is essential to the future of data analytics. So are people who understand how to put it all together. Contact us to learn how our expert business intelligence consultants can help you leverage this technology for smarter, faster business decisions.
