Predictive analytics applies the intersection of math, statistics, and computer science to businesses, nonprofits, enterprises, agencies, and organizations across industries and sectors — using data to leverage the past and present in order to optimize the future.
In addition to discussing some key techniques used in predictive analytics, below we dive into the real-world applications of predictive analytics and explore career opportunities in the field. So, if you’re interested in learning more about how predictive analytics makes our modern world turn, read on.
What Is Predictive Analytics?
Predictive analytics is a branch of advanced data analytics that harnesses the power of real-time data collection, data mining, statistical modeling, and machine learning to make predictions about future outcomes based on present and historical data.
Focused on identifying trends and predicting future behaviors and events, predictive analytics differs from traditional analytics, which is focused more on the descriptive (what happened) and diagnostic (how and why it happened) aspects of analysis. Traditional analytics uses historical data, basic statistical analysis, and traditional reporting methods to describe what happened and how it happened.
Comparatively, predictive analytics relies on current and historical data and uses advanced statistical modeling, data mining, and machine learning algorithms to generate forecasts.
While traditional analysis is useful for businesses to evaluate performance and learn from the past, predictive analytics takes the power of data analysis to the next level, enabling business leaders to prepare for the future instead of reacting to the past. Business leaders use predictive analytics to improve strategy, strengthen operations, and make better leadership decisions.
Key Techniques in Predictive Analytics
The predictive analysis process relies on several advanced tools, techniques, and concepts to thoroughly analyze data, identify trends, and provide highly accurate forecasts and predictions. Three key techniques leveraged in predictive analytics include machine learning algorithms, regression analysis, and time series analysis.
Machine Learning Algorithms
Whereas predictive analytics uses math to identify trends and patterns by running data through statistical models, machine learning provides algorithms for predictive modeling to automate the analytical process. Machine learning is a type of artificial intelligence (AI) that aims to emulate human learning in computers with several different types of algorithms, such as:
- Neural networks and deep learning
- Linear regression
- Logistic regression
- Clustering
- Decision tree
- Random forest
In order for machine learning to create accurate statistical models for predictive analytics, users must provide large, high-quality data sets for analysis and pattern identification. If the AI is provided with too little data or poor-quality data, then the statistical models created with machine learning techniques will not be accurate.
Regression Analysis
In business, countless factors can impact outcomes. Some of those factors are more influential or important than others, which can make it difficult to gain a clear understanding of past performance and even more challenging to predict future outcomes.
For example, the performance of a marketing campaign depends not only on your design choices, messaging, and channels but also on:
- How familiar your audience already is with your business when they see the ad
- The content displayed on the webpage adjacent to your ad
- The time of day
- How close it is to their payday
- Whether they’re at home or the doctor’s office
- The weather
- Their mood
This list doesn’t even begin to scratch the surface of potential factors impacting clicks and buying decisions. This is where regression analysis comes in.
Regression analysis helps data analysts identify, sort, and rank all the potential factors in outcomes. This powerful statistical tool is commonly used in predictive analytics for modeling the relationship between one (or several) independent variables and a dependent variable.
Regression analysis is useful for both forecasting and understanding forecasted outcomes. In predictive analytics, regression analysis not only identifies the related variables that impact an outcome, but it also weighs the strength or weakness of the connections between dependent and independent variables, which reveals how strongly certain factors influence outcomes.
Time Series Analysis
Having large data sets is vital to creating forecasts based on understanding the what, why, and how of the data set. Data scientists use time series analysis to measure, track, and understand the when. Time series analysis uses statistics to study the order of data points in time, revealing patterns, identifying dependencies, and uncovering trends to create better predictive models that take into account timing and seasonality.
Used within predictive analytics, this statistical technique reveals the timing of each piece of information within a big data set. This makes it possible to identify patterns and better understand behaviors in addition to monitoring seasonality or other time-based trends revealed in the data.
Real-World Applications of Predictive Analytics
With a basic understanding of the concept of predictive analytics and how it works, we can examine some of the real-life applications of these tools to see how predictive analytics is transforming the way the world operates.
Business and Finance
Predictive analytics has countless powerful practical applications in business and financial services, where the ability to predict market trends and consumer behaviors can be used to optimize almost every aspect of operations, product development, marketing and sales, strategy, and leadership.
Some of the most commonly utilized predictive analytics applications in business and finance include:
- Investment forecasting to optimize returns
- Credit scoring to determine the likelihood that a borrower will repay a debt
- Anomaly detection to quickly identify signs of fraud
- Consumer behavior prediction for optimal customer segmentation, messaging, and marketing channels
- Improving customer experience with customer response management systems and user experience
Historically, businesses have done their best with traditional analytics, using trailing 12 months (TTM) reports and cash flow forecasting to attempt to predict outcomes based on past performance. With the ability to analyze enormous data sets with automated machine learning capabilities, predictive analytics has revolutionized and vastly improved business forecasting.
Healthcare and Medicine
Applications of predictive analytics have also transformed the healthcare and medicine industry, and they continue to become increasingly integral to clinical medicine. Predictive analytics tools have already improved patient care, reduced hospital readmissions, and accelerated drug discovery. With large data sets, artificial intelligence and predictive analytics can also help with disease prediction, diagnosis, and the creation of personalized treatment plans and wellness recommendations.
Supply Chain and Logistics
If the COVID-19 pandemic taught us anything about supply chains and logistics, it was that supply chain disruptions are notoriously difficult to predict. Predicting the impact of consumer behaviors and trends on demand is also tough to forecast. However, retail and logistics companies like Amazon are using advanced machine learning tools, powered by artificial intelligence, to reinvent their predictive analytics models for enhanced supply chain management — and their technology is working.
AI’s ability to consider vast sets of data at once is enabling Amazon to anticipate its consumers’ needs well in advance; its algorithms now appear to know what customers are going to want to buy before they even start shopping. As a result, Amazon’s predictive analytics tools can anticipate consumer needs, forecast demand, optimize inventory, and prevent logistical disruptions. Plus, it’s able to offer rapid shipping and delivery.
Benefits of Predictive Analytics for Businesses
In all kinds of businesses across industries, predictive analytics offers benefits that enhance leadership, optimize operations, and elevate outcomes.
Improved Decision-Making
With predictive analysis, business leaders can use strategic (rather than reactive) leadership, basing the decisions they make on real data instead of gut feelings.
Risk Mitigation
With the routine use of predictive analytics, business leaders can reduce a variety of risks in business by anticipating and responding to problems before they actually occur.
Business leaders can leverage predictive analytics to improve:
- Cybersecurity – Predictive analytics can be used for proactive, increasingly accurate cybersecurity threat detection.
- Financial health – Identifying seasonality, predicting trends, anticipating consumer demand, and improving cash flow all strengthen financial health and reduce financial risk.
- Compliance monitoring – Predictive analytics can quickly identify anomalies and patterns that could indicate compliance problems and individual regulatory exceptions, helping business leaders identify and fix problems before they escalate.
- Fraud detection – Predictive analytics can analyze security systems and processes to identify potential weaknesses and vulnerabilities. Additionally, it can identify anomalies and data that stand out from the usual patterns to help business leaders detect fraud and implement better systems for prevention.
Enhanced Customer Experience
Predictive analytics may also improve the customer experience (CX) that a business cultivates. Predictive analytics can analyze patterns of customer behaviors to personalize customer experiences, create highly targeted marketing campaigns, and power customer recommendation engines by anticipating the individual customer’s needs, desires, and preferences.
Career Opportunities in Predictive Analytics
The advancement of artificial intelligence and growing demand for predictive analytics in business are generating plenty of diverse career opportunities in the field, providing opportunities for predictive analytics experts to work in countless industries and settings. Explore a few examples of career options in the field:
Data Analysts and Data Scientists
Transforming data into information, data analysts and scientists use technology and other automation tools to collect and organize data before extracting meaningful insights.
Machine Learning Engineers
Machine learning engineers help bridge the gap between software engineering and data science by designing and implementing the machine learning tools and models that data analysts use.
Business Intelligence Specialists
Business intelligence specialists use the information that data analysts extract from predictive analytics tools and apply the knowledge to business operations. They identify the questions that need to be answered and metrics that are most meaningful to a business, then interpret the data to make meaningful, effective leadership decisions.
The Future of Predictive Analytics
As technology continues to advance and become increasingly accessible, predictive analytics is poised for complete integration in business, nonprofit, and government operations. Every day, new advancements in artificial intelligence, cloud computing, and real-time data processing are enhancing predictive analytics processes and outputs. Businesses that aren’t currently using predictive analytics may consider doing so in order to remain competitive and relevant.
Predictive Analytics: Key Takeaways
- Predictive analytics uses automated tools, predictive models, and large data sets to generate forecasts and predict outcomes based on historical and current data.
- Powered by statistics, mathematics, and computer science, predictive analytics technology leverages machine learning algorithms, regression analysis, and time series analysis to generate increasingly accurate models and predicted outcomes.
- Predictive analytics has practical applications in nearly every industry and sector — especially business, finance, healthcare, medicine, supply chains, and logistics.
- Predictive analytics has the potential to improve decision-making, mitigate risk, and enhance customer experience.
- Several career opportunities exist in the field of predictive analytics — including data analysts, data scientists, machine learning engineers, and business intelligence specialists.
- Ask any predictive analytics model what the future of predictive analytics looks like, and it will tell you it’s bright!
Do You See a Future for Yourself in Predictive Analytics? Explore the Field With JWU
If the possibilities of predictive analytics are compelling to you, then we encourage you to explore the possibilities and consider developing your skills by enrolling in the Master of Science in Data Analytics program at Johnson & Wales University (JWU) Online. In this online program, you’ll have the opportunity to study topics like statistical analysis, data mining, data visualization, forecasting, and predictive modeling while learning all about predictive analytics methods alongside practical, real-world applications.
For more information about completing your degree online, complete the Request Info Form, call 855-JWU-1881, or email [email protected]. We look forward to answering your questions about JWU Online.