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AI can do just about anything and as it turns out, it can tell the future too. AI predictive analytics is a trend in data analytics where you feed historical data into a machine learning algorithm and ask it to predict future outcomes based on that data.
Sounds simple enough, but how does this AI-powered approach really work, and what are the concerns and limitations? Let’s find out.
AI predictive analytics is the use of artificial intelligence and machine learning to look at past datasets and forecast future outcomes. AI algorithms for data analysis take into consideration the data you feed them and then use predictive analytics and automation to determine what the data will look like in the future.
Or, in simple terms: let’s say you have historical data on sales in your business, for the past two years. It takes into account seasonality, sales rep performance, new products added, supply chain issues, and more. You feed that data into AI and it tells you what your sales will look like in the upcoming months.
It’s a way to make more informed decisions about the future of your business without taking hours to do complex data science and data analytics.
AI predictive analytics can be used for real-time insights and predictions across different industries.
Predictive models can help healthcare institutions better understand their patients and prepare for the future. For example, AI can consider seasonal trends for illnesses such as flu to predict when outbreaks can happen. This can help with resource allocation, such as preventing how many beds and medical staff should be available. Ultimately, healthcare institutions can reduce wait times and improve patient outcomes.
AI predictive analytics can be used in finance to spot unusual patterns in transactions. If there are strange patterns, AI can analyze them for fraud detection and risk management. This helps spot risky customer behavior early on before it escalates and hurts your profitability.
AI predictive analytics in retail can reveal actionable insights by analyzing purchasing history and consumer trends. With AI systems, retailers can determine which products will be in demand and when. This helps them stock the right products at the right time and reduce chances of overstock and stockouts.
Companies in manufacturing can use machine learning algorithms to monitor their equipment. This will show when the equipment needs maintenance and replacement, based on data points such as temperature, vibration and machine usage.
Energy companies can use predictive analytics to forecast demand and grid management, as well as do predictive maintenance. Based on factors such as historical usage, weather, and economic indicators, they can determine energy demand and adjust their production levels.
Compared to traditional business forecasting, the digital transformation brought by AI has a few advantages.
The speed of insights: feed big data into AI models and you can get fairly accurate predictions within minutes. Traditional predictive analytics with its complex workflows, statistical models, big data sets and a large number of stakeholders can take weeks or even months to get to actionable insights.
Cost reduction: if pricing is your main concern, AI predictive analytics can cut costs significantly because you’ll need fewer hands on deck. However, you’ll need at least one data scientist/analyst/engineer to prepare and model the data before analysis.
Improved customer experience: by anticipating customer needs ahead of time, you can provide better customer experiences. You can use the information obtained from data mining and predictive analytics for process optimization, helping you with both retention and churn.
Risk management: companies across different industries can use AI predictive analytics to reduce risks in operations related to finances, logistic support, customer support and more.
Increased revenue: through statistical models and accurate prediction, these models can help find new ways for upselling, cross-selling and targeting new market segments.
Competitive advantage: businesses and providers can stay ahead of the curve by leveraging AI for predictive analytics instead of taking weeks to analyze data manually.
Using AI-driven analytics to predict future trends and outcomes has some limitations that any business should be aware of.
The quality of AI predictive analytics depends mostly on the data you feed to the predictive models. Garbage in = garbage out. The data you feed into the AI should be clean, well-structured, complete, unbiased and free of duplicates. For accurate decision-making, look into the quality of your data first - each subset should be immaculate.
If you use predictive analytics in sensitive areas such as healthcare, hiring or criminal justice, it’s important to bear in mind that many AI models can be biased by default. The model is trained on existing data, which can be inherently biased and flawed.
Any data analysis that involves sensitive data requires special care. When entering data into deep learning models, make sure you comply with data protection regulations and that your customers know how their data is handled.
The AI predictive analytics functionality can be a game-changer for many businesses, until they start asking one question: how does it reach the decisions it makes? Many of the AI models function as “black boxes” without any explanation on how the model derives its outcomes. While data scientists can explain how they reached their conclusions with various analytics tools, AI can only give you the final results.
For important business decisions made in front of important stakeholders, this can be a blocker.
AI tools for predictive analytics can sometimes rely too heavily on historical data, ignoring the rapidly changing nature of a certain industry. If the business environment changes on a daily basis, the machine learning models should be fed new data in order to produce relevant outcomes.
Predictive analytics models have come a long way and for some use cases, they can be an invaluable tool for predicting trends. However, just like any AI tool, it has a long way to go before it becomes fully usable for business intelligence.
But, there is hope.
If you use AI for data analysis, you’ll quickly outgrow traditional models and want to make them more specific for your use case. To get the most out of predictive analytics, you'll need someone on your team to iterate on predictive models frequently and you stand a strong chance of getting better, more accurate results.
Second, predictive analytics is just one part of the puzzle. Even though these predictions and forecasts are super powerful, you'll still need data visualization to help the end user understand what is happening behind the data.
At Luzmo, we offer embedded analytics for software companies that want to provide amazing data-powered software products. You can connect historical and forecasted data to Luzmo and create beautiful, functional dashboards for your product.
When your customers get more control of their data, your product provides more value. As a result, you can upsell your customers more easily, lower your churn, and open up to new markets.
With Luzmo AI, your customers can get insights from their data faster through an intuitive, natural language interface.. They can type in natural language queries and generate dashboards with a single click, allowing them to streamline dashboard creation on top of historical data or predictive analytics.
Luzmo’s AI is API-first, which means that your data is safe with us and never shared with third-party sources.
Want to learn more about how Luzmo can help your business? Book a free demo with us today.
Experience the power of Luzmo. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
