The quality of data-based insights has been declining over the last few years. That's according to nearly 50% of decision-makers. Largely, this is because traditional metrics and key performance indicator (KPI)-focused dashboards and business intelligence (BI) tools have limited capabilities, despite teams being more data-driven than ever. The information these tools generate tends to be high-level summaries without real actionable insights or forecasting capabilities. Moreover, changing the dashboards takes time and resources away from more important tasks.
With real-time analytics, businesses can access the most recent data sources and make more informed decisions. But traditional analytics tools cannot keep up with the large volumes of real-time data businesses must process. This is where artificial intelligence (AI) comes into play. With AI-powered real-time analytics, you can quickly and reliably turn large data pools into powerful insights to accelerate business growth.
Traditionally, analytics platforms offer insights into trends using historical data spanning months or years. But this isn’t enough in a business environment where things can change in the blink of an eye. You’re working with incomplete information that doesn't consider current trends that could affect outcomes.
To keep up with rapid changes, organizations are now investing in real-time analytics solutions, which supply the most up-to-date information to help them make data-driven decisions in the moment.
Real-time analytics helps with both preventive and proactive measures. It allows businesses to respond to events quickly by leveraging real-time data to identify and resolve any potential problems before they even happen. Businesses can also anticipate customers' requirements and allocate resources accordingly, providing the best customer experience.
However, updating your dashboard to process large volumes of data in real time is time-consuming and laborious. This is why businesses are now looking at AI to simplify this process and make it more efficient.
According to Forrester, 80% of enterprise leaders still depend on manual data analysis, while the remaining 20% use unintuitive BI applications not designed to process a million data points. What they’re missing are AI-powered dashboards in their workflows to collect and make sense of large amounts of data as quickly as possible.
Businesses can derive in-depth insights with AI by analyzing large volumes of historical data. They can identify business trends to:
Find new opportunities and create more effective marketing and sales strategies by tracking the performance of products and services.
Improve operational efficiency and cut costs throughout the business, from manufacturing to payroll.
Predict customers' requirements by understanding their behavior based on previous transactions. Reduce churn by analyzing customers' earlier brand interactions to improve future experiences.
AI applications can take real-time analytics to the next level by offering powerful insights into various facets of the business, like:
Predictive analytics offers insights into customer behavior and actions using statistical models to identify patterns and anomalies in data. By incorporating machine learning algorithms, you can automate predictive modeling.
An example use case is the healthcare industry analyzing large amounts of patient data for telltale signs of any disease, making it possible for medical practitioners to intervene earlier. This gives the patient a better chance at recovery.
NLP is an AI technology that allows machines to understand human language. It uses various types of analysis (syntactic, semantic, etc.) to enable technologies like chatbots to mimic human speech and search engines to provide more accurate results to user queries.
The most common use case of NLP in real-time analytics would be tools like Grammarly that check for spelling and grammatical errors while you are typing. But a more interesting use of NLP is training chatbots to converse and behave like a human would. Businesses can use chatbots to offer 24/7 customer support, ensuring customer queries are addressed quickly and efficiently.
PubNub lets you create your ChatGPT-powered chatbot using its OpenAI Chat API. The chatbot can provide answers to common support questions.
Put simply, deep learning is a subset of machine learning that simulates the human brain. Used in many AI applications because of its ability to process and analyze complex, unstructured datasets, deep learning is perfect for image identification, speech recognition, and predictive analytics.
One example is retail enterprises using deep learning to predict customer demand, supply chain issues, and future earnings. Deep learning can read scans more accurately in medicine, catching things the human eye might miss. It can also train chatbots like Siri and Cortana to converse with users naturally.
Traditional analytics can’t handle large volumes of real-time data. As such, businesses are looking to AI-powered real-time analytics to improve productivity, reduce costs, and enhance their decision-making capabilities.
Additionally, AI analytics is scalable and reliable, giving you more in-depth business information by processing different data types in real time. When you combine real-time analytics with various AI capabilities—machine learning, natural language processing, etc.—you get a completely new perspective of your business that would have otherwise gone unnoticed.
PubNub lets you quickly and easily integrate AI into your real-time apps without worrying about scalability, performance, reliability, or latency. With the built-in PubNub Insights dashboard, you can track the performance of these applications and more intuitively grow your business. And now, PubNub’s new Analyze with AI (available to Insights Premium subscribers) scans your data and generates natural language analysis tailored to your specific queries. Created using OpenAI’s GPT models, you can uncover more about your data quickly and easily.