Today, virtually every company in the world produces an immense amount of data each day. According to some predictions, the amount of unstructured data will increase to 175 billion zettabytes by 2025.
While it seems like a boon for decision makers to have so much information at their fingertips, trying to find the one datapoint that will actually make a difference to your competitive advantage can be impossible when faced with what feels like a “tsunami” of data.
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Without the right technology to process and analyze all this data, business and data analysts are hard pressed to extract meaningful insights and deliver them in time to still be relevant. This is where AI provides the life raft that companies need to turn their data into valuable decision-making tools.
Surface the Right Information
AI is a powerful partner in business decision-making because it simplifies the overwhelming task of finding the right data. Rather than an analyst going through hundreds of data points manually, AI does the heavy lifting of ingesting and sifting through large and complex datasets, and then automatically pulls the right insights to the surface.
However, while this type of AI tames the data tsunami and eases the burden on analysts, what business decision makers really need is an AI-powered analytics platform that emphasizes explainability, interaction, and AI-generated visualizations. Platforms like these not only surface insights, but then suggest, in plain language, what to do next to explore the data more in-depth.
Taking it one step further, business consumers with non-analytical backgrounds can rely on the technology to help explain findings, answer questions, and produce 3D visualizations that make the data even easier to interpret. These kinds of AI platforms are truly like having a data analyst always at your side, advising you on which data will help you make the best decisions for the business.
Surf the Data Tsunami: 3 Practical Applications
By leveraging AI, businesses can transform their data into actionable intelligence, driving innovation, efficiency, and competitive advantage. Here are three examples of what that looks like:
Improve personalization and customer experience
AI-powered analytics offers a powerful approach to customer segmentation by leveraging advanced algorithms and machine learning techniques to identify behavioral trends and anticipate changes in customer needs.
This can help healthcare providers, for example, tailor treatment plans based on patient-specific characteristics and preferences, which improves treatment efficacy, minimizes side effects, and enhances patient outcomes. Additionally, companies can leverage AI to perform customer segmentation tasks, surfacing data that decision-makers can use to really personalize products, services, and marketing efforts for different customer segments.
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Predict areas of risk
AI can recognize patterns in your data, enable real-time analytical capabilities, and deliver more accurate and nuanced predictions. Furthermore, the algorithms don’t just process historical data, they learn from them, which also helps improve their predictions over time.
Cybersecurity teams can leverage AI to sift through data and determine if any communities have a significantly higher likelihood of falling for a phishing email. Once it’s possible to proactively identify those who might click on a malicious link, the next step is to define potential strategies for stopping breaches before they happen, such as more training, banner alerts, or information security changes.
Financial institutions can also use AI to predict and mitigate risks related to fraud and compliance violations. Say a credit card issuer wants to detect and predict risk factors. By exploring and analyzing their enormous, dynamic, complex data lake, the algorithm can identify subtle, recurring patterns and define suspicious activities that humans can’t spot due to scale.
Improve operational efficiency
AI helps analysts deliver reports that are more accurate since some models can highlight errors or even predict potential issues before they happen. With cleaner data, company leaders feel more confident that they’re making decisions from the right datasets and, therefore, are pursuing the best solutions for the business.
This is music to the ears of manufacturing decision makers. Knowing what to do to keep a piece of machinery from failing provides immense benefits and savings. Sophisticated AI applications can analyze data from disparate systems to identify patterns and anomalies associated with component failures, assign risk scores to individual components, and recommend the best course of action to take.
Similarly, supply chain and logistics leaders can leverage AI-driven analytics to assess historical and real-time data from multiple, disparate sources, giving their data analytics teams clear, streamlined reports on predicted potential disruptions. Explainable AI (XAI) technology can also surface alternative suppliers and routes or suggest how to optimize inventory levels, helping businesses avoid the biggest risks that may be coming up.
It’s Time to Ride the AI Wave
As the business landscape continues to grow more complex, the ability to make data-driven decisions could mean the difference between success or failure. AI enhances the decision-making process, revealing the narratives hidden with your data and providing insights in an approachable manner, so business leaders can spend less time on searching for answers and more time developing the best data-informed strategies.
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