From weather forecasts to health-monitoring apps, predictive analytics has made it possible for industries to produce innovative solutions over the years.
Thanks to advances in supporting technology, predictive analytics is now widely accessible. Businesses can now make sense of their big data with data analytics services. Businesses rely on predictive analytics to get ahead and improve overall business operations. When combined with prescriptive analytics, these two processes allow for optimal decision making.
Here’s a look at what makes predictive analytics so valuable for businesses today.
This process uses analytical queries, statistical analysis, and machine learning algorithms to create a predictive model. This model will place a numerical value on the likelihood that an event or outcome will occur giving businesses the opportunity to plan and strategize accordingly.
Businesses across industries use predictive analytics to improve their operations and bottom line. From retail to aerospace engineering, predictive analytics is an invaluable tool to optimize business operations.
Aerospace Engineering wouldn’t be nearly as advanced as it is today without predictive analysis.
The field of predictive analytics is far reaching across many industries. Here are some examples of what predictive models can do for various industries.
Autonomous vehicles and driver-assistance technology use predictive analytics to analyze sensor data from vehicles and create driver-assistance algorithms. Automotive manufacturers also use sensors to measure vibrations in automotive parts. These sensors will signal the need for maintenance before the vehicle fails while in use.
To monitor aircraft engine health, a real-time analytics application can predict anything from performance for controls, fuel, oil, liftoff, and mechanical health. This improves safety, reduces maintenance costs, and reduces aircraft downtime.
Maximize cross-sell or up-sell opportunities.
The energy sector uses predictive analytics to forecast electricity price and demand. Forecasting apps monitor power plant availability, historical trends, seasonality, and weather.
Forecasting the demand for electricity on the electrical grid allows for effective resource planning (I.e. scheduling for various power plants).
The healthcare sector benefits from predictive analytics by being able to use information technology data to help develop efficient management plans and offering better healthcare to Canadians. Predictive analytics also helps reduce false alarms in patient monitoring devices in hospitals. Which, in turn, improves patient comfort and safety.
The noise from false alarms disturbs patients while they’re trying to sleep and recover. False alarms also risk desensitizing hospital staff. So when real alarms sound, they may not be as quick to act.
Predictive analytics predict equipment failures and energy needs. It also reduces downtime and operating costs.
Insurance companies — health, home, auto, or businesss — use predictive analytics to find patterns and determine the best solutions to eliminate risk and fraud.
Predictive analytics gives retailers insight into customers’ wants, needs, and shopping behaviour. Retailers can determine appropriate offers for customer, which products to stock, and the effectiveness of promotional events.
In the increasingly competitive market, predictive analytics helps businesses gain a competitive edge. Businesses can make well-informed decisions based on their data.
From coast to coast, predictive analytics help businesses make better decisions across a broad range of industries.
Businesses use predictive models to forecast inventory, manage resources, and operate more efficiently. This could be the use of customer data to offer specific products and services at certain times and prices.
Hotels predicting the number of guests for a given night to increase occupancy and revenue.
Predictive analytics helps businesses optimize their marketing campaigns to attract and retain customers—especially their most profitable customers—by predicting their responses and purchases.
What’s the Difference Between Predictive Analytics & Prescriptive Analytics?
Once businesses successfully implement predictive analytics, they can use that knowledge to create prescriptive analytics. While predictive analytics estimates future outcomes, prescriptive analytics details the best way to react to the outcome.
There are many various data analytics services available today but prescriptive analytics uses predictive models to suggest actions to take for the most optimal outcomes. This branch of data analytics uses optimization and rules-based techniques for optimized decision making.
With advancements in technology, it’s never been easier to make sense of business’ ever-growing data and use that knowledge to advance business operations.
Predictive analytics helps businesses get ahead of the competition with invaluable insights into behaviours, trends, and expected outcomes.