By Rachel Lee - August 19, 2021
Humans have been making decisions based on data for thousands of years, so it’s a misnomer to think that “data-driven” decision-making is a 21st century phenomenon.
It’s just that data analysis looks different, and data itself is vastly more plentiful, these days. What’s really changed is our ability to analyze data more effectively using artificial intelligence and machine learning. In fact, AI is so essential to data analysis and big data, we should be asking why “AI-driven” decision-making isn’t the term being used more often?
Whenever a business takes a structured, analytical approach to decision-making, they are using some form of intelligence. But when algorithms are involved—either to make recommendations or to incorporate automation into the decision-making process—then AI systems are also at play.
But what is AI? The first thing to know is that while AI systems imitate human behavior, they are intrinsically different, more like an alien intelligence. That’s because even when AI executes a task symmetrically to how a human would do it, AI systems achieve those outcomes in very different ways.
To understand that further, you must consider how AI learns, also known as machine learning. Machine learning is what allows AI systems to get progressively smarter, improving their execution of tasks over time.
Some AI systems are powered by deep learning, a subset of machine learning that specifically uses neural networks, which are computer systems modeled on the human brain’s synapses and structures. Neural networks equip AI systems with an incredible ability to recognize patterns in numerical and textual data, and increasingly, visual data, too.
Let’s take a look at an example of AI that’s already embedded in our everyday lives: autocorrect.
Autocorrect runs on natural language processing (NLP) or the ability of AI systems to apply linguistics and reproduce human language. Autocorrect is brilliant when it comes to predicting the next letters of a word or phrase, but not because it understands language to the extent that humans do. The AI has never held a conversation; it doesn’t even know what a conversation is! Autocorrect is simply making suggestions based on advanced pattern recognition, after being trained on large datasets of text.
While autocorrect is useful for predicting words and letters, NLP remains poor at grasping other aspects of the human language, like tone, sarcasm, and metaphor. This points to some of the unique advantages of human intelligence: applying concepts like empathy, creativity, and emotional intelligence to arrive at better decisions. These are all concepts that AI has yet to—and some say, will never—master.
Automation can be a scary word, but there’s an entire spectrum of expert systems using AI that don’t require computers to displace humans. You can bring augmented AI into your business, which is when algorithms get used to enhance human intelligence but never replace decision-makers. A good example is resource optimization, a complex situation that most decision-makers hold onto, but a process that can benefit from advanced analytics, another application of AI.
You can also have AI automation in other areas, which is when AI has the power to make interventions on its own, typically seen with lower-stakes decisions. One good example is the automation of adjustments to work orders, which AI can do autonomously as customer data shifts in real time.
How do you decide when to integrate AI into your business? And where do you decide on automation over augmentation?
When large analyses are involved, AI can vastly strengthen business decision-making. Simply put, AI systems are capable of spotting trends and patterns in data within seconds that would take human intelligence years to find. By mining data around customer behavior, customer experience, and social media impressions, AI has gained a major foothold in marketing decision-making.
But they can also be a helpful tool to remove biases from certain decisions, like workforce optimization. AI algorithms can be used in decision support systems to plot a course of action for decision-makers that isn’t based on the feedback of employees, which is bound to contain biases.
Healthcare is one industry where AI both frees up human capital and reduces bias. Throughout American healthcare, racial and economic bias persists in a number of ways, including the disproportionately high rates of readmission for Black patients at hospitals. But a recent study showed how a Wisconsin hospital leveraged augmented analytics to address avoidable inpatient readmissions, and therefore, provide more equitable care.
Augmented analytics is the use of machine learning and AI to improve problem-solving related to the future of a business. According to a recent Gartner report, augmented analytics is today’s biggest battleground in business intelligence. Some analysts predict the global augmented analytics market will reach at least $30 billion by 2027.
Why? Augmented analytics let you fast-track every step of your data analysis. These platforms will unify and organize analyses, while using AI to draw insights that go beyond the point-in-time visualizations of BI dashboards.
You can easily incorporate AI into an existing data analytics stack by downloading one of the many augmented analytics platforms on the market. These platforms have never been more accessible, thanks to recent advances in computer science that have simplified data processing.
At Sisu, we consider the metrics that matter to you most and let you forgo the time-intensive process of running endless queries on your data. Get the facts with Sisu today by reviewing our webinars or requesting a demo and see how customers like Samsung, Mastercard, Wayfair, and Gusto are using Sisu to get more return on their data investment.