The Internet of Things (IoT) demands predictive analytics. Data is the lifeblood of IoT. Its exponential growth is driving the widespread adoption of devices, networks, and other technologies capable of connecting to the internet. Beyond that, data is ushering in a new generation of powerful devices that will profoundly change the world of business.
It’s estimated that there will be 75.4 billion devices by 2025. These devices continually generate massive volumes of data. By 2025, the world is going to produce 163 zettabytes of data annually, with 20% of it coming from IoT. Despite all of this data, it won’t be valuable unless it’s actionable. The challenge now is to collect, evaluate, and convert data into actionable insights for businesses.
That’s where predictive analytics comes in.
What Is Predictive Analytics?
A branch of advanced analytics, predictive analytics uses statistical algorithms and machine learning capabilities to determine future outcomes based on historical data. The patterns determined from historical and transactional data can help businesses to determine the risks and rewards for future endeavors.
Predictive analytics empowers organizations across all industries by interpreting big data effectively to move their businesses forward.
What Does Predictive Analytics Need?
Organizations have four different classes of software to choose from, they are:
Use case-specific solutions
This particular type of software provides its users with quick, easy access to complicated mathematical and statistical methods. They come with user-friendly interfaces, ideal for professionals without technical know-how.
Business intelligence solutions
A form editor usually comes with business intelligence (BI) products. It lets the user store basic math calculations and determine the value within the reports. That means a designer can store a calculation that applies exponential smoothing in identifying forecast values, like the next quarter’s sales.
Advanced analytics platforms
The larger the company, the more complex their operations. In such settings, use-case-specific capabilities may be limited. BI products will have their limitations when it comes to complicated predictive analytics endeavors such as modeling, neural networks, and text mining, which is when organizations may need to implement advanced analytics platforms that support these tasks.
Also called closed-source software, proprietary software is usually owned by the people who built it. The source code of such products is kept from its users. This means organizations don’t have the freedom to modify the software according to their needs.
Why Does Predictive Analytics Matter?
In the digital era, organizations deal with unprecedented and overwhelming amounts of data. They need to collect and analyze large datasets to be able to avert potential disasters and discover new opportunities.
Predictive analytics is a relatively new discipline. As this technology matures, it’s going to be used in increasingly critical applications. Here’s why predictive analytics matter:
Enables fraud detection
No matter the industry, organizations are vulnerable to a wide variety of threats. Perhaps none is as difficult to diagnose and overcome as fraud. Failure to address fraudulent activity can cost businesses heavily. Not to mention, it cripples company integrity, as well as its bottom line. Through predictive data science methodologies, organizations can identify fraudulent activity before it can cause any harm.
Nurtures team collaboration
Large companies’ teams often work in silos. That doesn’t exempt marketing and sales teams. Far too often, they show very little effort to collaborate for the benefit of the company. With predictive analytics and IoT, teams receive notifications whenever there are new, relevant developments. This promotes better transparency between teams, especially when it comes to decision-making.
Prevents human error
With predictive analytics doing all the heavy lifting, organizations can prevent and possibly even eliminate human error. This means professionals can focus on what they do best. Of course, they’ll also have more time to dedicate to their customers.
Who Uses Predictive Analytics?
Every industry can benefit from using predictive analytics. Here’s how some different industries can apply predictive analytics:
Driver assistance algorithms will make the roads safer for everyone. Predictive analytics powers autonomous vehicles and uses data derived from sensors on vehicles that are connected. This enables organizations to determine a driver’s bad habits on the road. Telematics is another exciting innovation revolutionizing the automotive industry. By placing sensors in cars, everyone can record the driver’s behavior, how fast it was, etc. With the ability to connect with the driver and access to driving data, you can provide insurance with accurate information.
One of the most important applications of predictive analytics in Healthcare is for fraud detection. But it has several other capabilities that can benefit this industry. The health insurance sector, for instance, uses predictive analytics to determine patients who are at risk of chronic disease, enabling them to intervene and save lives.
Pattern-detection algorithms are also used to detect lung diseases such as asthma and chronic obstructive pulmonary disease (COPD). A device analyzes how the patient’s breathing sounds and delivers feedback in real-time through a mobile app.
All the different types of equipment manufacturers use are valuable. Any issues can lead to major setbacks, and of course, losses. But now, they can avoid such a conundrum through predictive analytics. Some of the most recent equipment used are now automated and far more complex than ever before. However, with predictive analytics, it becomes easier to manage and maintain these assets.
Traditionally, manufacturers have to set regular maintenance schedules to prevent potential issues and deploy service teams only when the machines fail. With predictive analytics, they won’t have to conduct maintenance services when they’re not necessary. IoT predictive maintenance solutions alert companies if a machine has a high chance of failing. Then, they can deploy service teams to preemptively address any issues before they can cause the machine to malfunction.
Remember that study that showed men who buy diapers almost always buys beer at the same time? That’s one of the patterns that predictive analytics can pick up from your data. Retailers can use this technology to determine how effective promotional events are or identify the best offers for specific consumers.
The collection of weather-related data — e.g., temperature, sun levels, wind speeds, etc. — allows analysts to determine patterns, as well as trend-related variables. For instance, predictive analytics can be used to determine how the weather affects the sales of sports clothes.
Predictive analytics is becoming more and more important because of the rise of big data and an increasingly competitive landscape.
To get started with predictive analytics, first determine the problem you want to solve. Fraud? Maintenance? Next, you’ll need data from your devices, sensors, and more. It’s best to have experienced professionals on data management on your team who can interpret your data for you. To put your data to good use, find and deploy the ideal software for your company’s needs.
In the world of IoT and the increasing amounts of data connected devices and system generate, predictive analytics isn’t a trend. It’s here to stay, and companies who use it will consistently outperform companies who don’t.
Meet UIB Guest Blogger Danielle Canstello
Danielle Canstello is a Content Manager for global enterprise business intelligence and analytics software leader Pyramid Analytics. In her spare time, she writes all over the web to share her knowledge of the marketing, business intelligence, and analytics industries.