Big Data is the underlying force that powers the fourth industrial revolution. Today, enterprises across industries are leveraging the capability of data to derive valuable insights and gain a competitive advantage in the marketplace.
In recent years, companies have moved away from a departmental Big Data model to one that’s business driven. With the emergence of stream processing, enterprises can now enhance agility by leveraging predictive analytics to achieve both short-term and long-term goals.
Predictive Analytics can be described as a division of data analytics that concentrates on predicting future outcomes based on historical data, statistical modeling, Artificial Intelligence, Deep Learning, and Machine Learning (ML). For example, this is what’s happening when Netflix makes an accurate movie recommendation that you’re bound to enjoy.
Sophisticated predictive analytics tools and models available today can generate future insights with a significant degree of precision. However, this process doesn’t have to take days or weeks to complete, we can now reliably forecast trends and behaviors in real-time.
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Predictive analytics has had a significant impact across industries. According to Zion Market Research, the global predictive analytics market is forecasted to reach approximately $10.95 billion by 2022.
Some best-in-class predictive analytics tools include the following:
So how has predictive analytics transformed industries? Let’s take a look.
With the emergence of the Internet of Things, predictive analytics tools have optimized the production process by improving uptime without the risk of malfunctions. For example, manufacturers can now engage in preventative maintenance based on the data captured inside the machines.
According to Dr. Sheela Siddappa, Head of Global Delivery – Data Analytics at Bosch, “whether it's focused on vibration sensors or temperature sensors, we can collect all of this data, move into the cloud, analyze it and then create a visualization from which we can plot decision-making mechanisms… If I follow the norm, that act can be asking an engineer to fix the problem. But if I look at the big picture, then there is a possibility of giving that engineer much more time to prevent a problem, and prevention is better than the cure...”
This approach will help ensure maximum operating efficiency and eliminate the costs associated with unexpected downtime during the production cycle. Predictive analytics in an industrial setting can also help forecast demand, increase production line productivity, improve quality control, and enhance machine utilization.
Most of us have already experienced predictive analytics when buying products from Amazon. In fact, the company has perfected the art of making product recommendations based on previous purchases and searches.
Knowing what will be in demand in the future not only helps retailers gain a competitive advantage, it also enables improved inventory management. This approach can prevent inventory shortages from occurring. While reducing expenses on inventory, you’ll also have a better chance of selling the stock you buy (instead of dealing with sunk costs).
By studying buyer behavior, predictive algorithms can also determine where products should be placed within the retail environment. According to Cisco, in-store analytics alone is worth approximately $61 billion.
We can’t talk about predictive analytics in retail without talking about the colossal coffee brand, Starbucks. If you have ever wondered how they have been able to profitably operate multiple branches on the same block, the answer lies in data analytics.
By leveraging predictive analytics tools, Starbucks has been highly successful in accurately predicting the growth potential of each new store location. Furthermore, predictive analytics also enables the delivery of highly intuitive and personalized experiences when customers walk in through the door.
Healthcare providers have been at the forefront of digital transformation. As a result, it’s not surprising that they have been one of the most enthusiastic early adopters of this technology.
For the healthcare industry, predictive analytics tools help them save money and improve patient care by enabling the following:
- Accurate identification of patients at risk (for costly near-term readmission)
- Optimized staff scheduling
- Smart allocation of facility resources (based on past trends)
- Smart pharmaceutical and supply acquisition and management
Predictive analytics tools can also be leveraged to do a lot more. Health Catalyst in Salt Lake City, for example, has used predictive analytics over the past decade to help hospitals achieve the following:
- Assess the risk of patients not showing up for scheduled appointments
- Prevent hospital-acquired infections by predicting the likelihood of patient susceptibility to central-line associated bloodstream infections
- Use ML to predict the likelihood of patients developing chronic diseases
Research suggests that approximately 57% of healthcare executives are already using predictive analytics. These professionals believe that predictive analytics tools can help them save 15% or more of their total budget over the next five years.
Going forward, you can expect this trend to continue because this technology will be critical to the future of healthcare providers.
Academic institutions have to have to juggle several complex issues like delivering quality education, maintaining compliance and accreditation levels, process grants, and enhance student satisfaction.
For decades, education providers have experimented with multiple ways of achieving long-lasting positive relationships by delivering enhanced student experiences. However, it has usually been a case of hit or miss.
Today, academic institutions are well-placed to leverage the power of predictive analytics to effectively engage in the following activities:
Enable the sharing of resources between students and institutions.
- Enhance learning outcomes
- Help educational institutions to plan better for the semester/year ahead
- Identify which students would need a lot of support at an early stage
- Intervene at the right time and provide appropriate support
- Resolve critical challenges during the course of the student lifecycle
When educational institutions take advantage of data analytics, they can map out improvement plans to achieve a diverse set of goals. For example, Northeastern University has been able to use predictive models to identify applicants who would best fit their school if admitted.
In this scenario, something as simple as keeping track of the open rate of emails provided valuable insights on whether potential students actually enrolled at the university as opposed to what they said during a campus visit.
In the digital age, data and analytics will be at the heart of everything we do. It will help enhance efficiency, improve customer experiences, identify new opportunities, and improve the bottom line. So if companies choose to ignore big data and predictive analytics, they will risk becoming irrelevant.