Predictive analytics definition

Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. The science of predictive analytics can generate future insights with a significant degree of precision. With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast trends and behaviors milliseconds, days, or years into the future.

Predictive analytics has captured the support of wide range of organizations, with a global market size of $12.49 billion in 2022, according to a research study published by The Insight Partners in August 2022. The report projects the market will reach $38 billion by 2028, growing at a compound annual growth rate (CAGR) of about 20.4% from 2022 to 2028.

Predictive analytics in business

Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning, and assorted mathematical processes. Organizations use predictive analytics to sift through current and historical data to detect trends and forecast events and conditions that should occur at a specific time, based on supplied parameters.

With predictive analytics, organizations can find and exploit patterns contained within data in order to detect risks and opportunities. Models can be designed, for instance, to discover relationships between various behavior factors. Such models enable the assessment of either the promise or risk presented by a particular set of conditions, guiding informed decision-making across various categories of supply chain and procurement events.

For tips on how to effectively harness the power of predictive analytics, see “7 secrets of predictive analytics success.”

Benefits of predictive analytics

Predictive analytics makes looking into the future more accurate and reliable than previous tools. As such it can help adopters find ways to save and earn money. Retailers often use predictive models to forecast inventory requirements, manage shipping schedules, and configure store layouts to maximize sales. Airlines frequently use predictive analytics to set ticket prices reflecting past travel trends. Hotels, restaurants, and other hospitality industry players can use the technology to forecast the number of guests on any given night in order to maximize occupancy and revenue.

By optimizing marketing campaigns with predictive analytics, organizations can also generate new customer responses or purchases, as well as promote cross-sell opportunities. Predictive models can help businesses attract, retain, and nurture their most valued customers.

Predictive analytics can also be used to detect and halt various types of criminal behavior before any serious damage is inflected. By using predictive analytics to study user behaviors and actions, an organization can detect activities that are out of the ordinary, ranging from credit card fraud to corporate spying to cyberattacks.

Predictive analytics use cases

Organizations today use predictive analytics in a virtually endless number of ways. The technology helps adopters in fields as diverse as finance, healthcare, retailing, hospitality, pharmaceuticals, automotive, aerospace, and manufacturing.

Here are a few ways organizations are making use of predictive analytics:

Aerospace: Predict the impact of specific maintenance operations on aircraft reliability, fuel use, availability, and uptime.Automotive: Incorporate records of component sturdiness and failure into upcoming vehicle manufacturing plans. Study driver behavior to develop better driver assistance technologies and, eventually, autonomous vehicles.Energy: Forecast long-term price and demand ratios. Determine the impact of weather events, equipment failure, regulations, and other variables on service costs.Financial services: Develop credit risk models. Forecast financial market trends. Predict the impact of new policies, laws, and regulations on businesses and markets.Manufacturing: Predict the location and rate of machine failures. Optimize raw material deliveries based on projected future demands.Law enforcement: Use crime trend data to define neighborhoods that may need additional protection at certain times of the year.Retail: Follow an online customer in real-time to determine whether providing additional product information or incentives will increase the likelihood of a completed transaction.

Predictive analytics examples

Organizations across all industries leverage predictive analytics to make their services more efficient, optimize maintenance, find potential threats, and even save lives. Here are three examples:

Rolls-Royce optimizes maintenance schedules and reduces carbon footprint

Rolls-Royce, one of the world’s largest manufacturers of aircraft engines, has deployed predictive analytics to help dramatically reduce the amount of carbon its engines product while also optimizing maintenance to help customers keep their planes in the air longer.

DC Water drives down water loss

The District of Columbia Water and Sewer Authority (DC Water) is using predictive analytics to drive down water loss in its system. Its flagship tool, Pipe Sleuth, uses an advanced, deep learning neural network model to do image analysis of small diameter sewer pipes, classify them, and then create a condition assessment report.

PepsiCo tackles supply chain with predictive analytics

PepsiCo is transforming its ecommerce sales and field sales teams with predictive analytics to help it know when a retailer is about to be out of stock. The company has created the Sales Intelligence Platform, which combines retailer data with PepsiCo’s supply chain data to predict out-of-stocks and alert users to reorder.

Predictive analytics tools

Predictive analytics tools give users deep, real-time insights into an almost endless array of business activities. Tools can be used to predict various types of behavior and patterns, such as how to allocate resources at particular times, when to replenish stock or the best moment to launch a marketing campaign, basing predictions on an analysis of data collected over a period of time.

Some of the top predictive analytics software platforms and solutions include:

Alteryx Analytics Automation PlatformAmazon SageMakerH20 AI CloudIBM SPSSRapidMinerSAP Analytics CloudSAS ViyaTIBCO

For more on the tools that drive predictive analysis, see “Top 8 predictive analytics tools.”

Predictive analytics models

Models are the foundation of predictive analytics — the templates that allow users to turn past and current data into actionable insights, creating positive long-term results. Some typical types of predictive models include:

Customer Lifetime Value Model: Pinpoint customers who are most likely to invest more in products and services.Customer Segmentation Model: Group customers based on similar characteristics and purchasing behaviors.Predictive Maintenance Model: Forecast the chances of essential equipment breaking down.Quality Assurance Model: Spot and prevent defects to avoid disappointments and extra costs when providing products or services to customers.

Predictive modeling techniques

Model users have access to an almost endless range of predictive modeling techniques. Many methods are unique to specific products and services, but a core of generic techniques, such as decision trees, regression — and even neural networks — are now widely supported across a wide range of predictive analytics platforms.

Decision trees, one of the most popular techniques, rely on a schematic, tree-shaped diagram that’s used to determine a course of action or to show a statistical probability. The branching method can also show every possible outcome of a particular decision and how one choice may lead to the next.

Regression techniques are often used in banking, investing, and other finance-oriented models. Regression helps users forecast asset values and comprehend the relationships between variables, such as commodities and stock prices.

On the cutting edge of predictive analytics techniques are neural networks — algorithms designed to identify underlying relationships within a data set by mimicking the way a human mind functions.

Predictive analytics algorithms

Predictive analytics adopters have easy access to a wide range of statistical, data-mining and machine-learning algorithms designed for use in predictive analysis models. Algorithms are generally designed to solve a specific business problem or series of problems, enhance an existing algorithm, or supply some type of unique capability.

Clustering algorithms, for example, are well suited for customer segmentation, community detection, and other social-related tasks. To improve customer retention, or to develop a recommendation system, classification algorithms are typically used. A regression algorithm is typically selected to create a credit scoring system or to predict the outcome of many time-driven events.

Predictive analytics in healthcare

Healthcare organizations have become some of the most enthusiastic predictive analytics adopters for a very simple reason: The technology is helping them save money.

Healthcare organizations use predictive analytics in several ways, including intelligently allocating facility resources based on past trends, optimizing staff schedules, identifying patients at risk for a costly near-term readmission and adding intelligence to pharmaceutical and supply acquisition and management.

Healthcare consortium Kaiser Permanente has used predictive analytics to create a hospital workflow tool that it uses to identify non-intensive care unit (ICU) patients that are likely to rapidly deteriorate within the next 12 hours. NorthShore University HealthSystem has embedded a predictive analytics tool in patients’ electronic medical records (EMRs) that helps it identify which chest pain patients should be admitted for observation and which patients can be sent home.

For a deeper look, see “Healthcare analytics: 4 success stories.”

How should an organization begin with predictive analytics?

While getting started in predictive analytics isn’t a snap, it’s a task that virtually any business can handle as long as one remains committed to the approach and is willing to invest the time and funds necessary to get the project moving. Beginning with a limited-scale pilot project in a critical business area is an excellent way to cap start-up costs while minimizing the time before financial rewards begin rolling in. Once a model is put into action, it generally requires little upkeep as it continues to grind out actionable insights for many years.

For a deeper look, see “How to get started with predictive analytics.”

Predictive analytics salaries

Here are some of the most popular job titles related to predictive analytics and the average salary for each position, according to data from PayScale.

Analytics manager: $72K-$134KDirector of analytics: $84K-$179KBusiness analyst: $49K-$87KChief data scientist: $133K-$290KData analyst: $46K-$89KData scientist: $70K-$137K

More on predictive analytics:

7 projects primed for predictive analytics7 ways predictive analytics can improve customer experienceTop 8 predictive analytics tools comparedIdentifying high-risk patients with predictive analyticsPredictive analytics: 4 success stories
Analytics, Artificial Intelligence, Predictive Analytics

At the Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, scientists, mathematicians, and software developers conduct manufacturing research, working together to gain new insights from machine, product, and manufacturing data. Manufacturers partner with the team at WZL to refine solutions before putting them into production in their own factories. 

Recently, WZL has been looking for ways to help manufacturers analyze changes in processes, monitor output and process quality, then adjust in real-time. Processing data at the point of inception, or the edge, would allow them to modify processes as required while managing large data volumes and IT infrastructure at scale.

Connected devices generate huge volumes of data

According to IDC, the amount of digital data worldwide will grow by 23% through 2025, driven in large part by the rising number of connected devices. Juniper Research found that the total number of IoT connections will reach 83 billion by 2024. This represents a projected 130% growth rate from 35 billion connections in 2020.

WZL is no stranger to this rise in data volume. As part of their manufacturing processes, fine blanking incubators generate massive amounts of data that must first be recorded at the sharp end and processed extremely quickly. Their specialized sensors for vibrations, acoustics and other manufacturing conditions can generate more than 1 million data points per second.

Traditionally, WZL’s engineers have processed small batches of this data in the data center. But this method could take days to weeks to gain insights. They wanted a solution that would enable them to implement and use extremely low-latency streaming models to garner insights in real-time without much in-house development.

Data-driven automation at the edge 

WZL implemented a platform which could ingest, store, and analyze their continuously streaming data as it was created. This system gives organizations access to a single solution for all their data (whether streaming or not) that provides out-of-the box functionality and support for high-speed data ingestion with an open-source and auto-scaling streaming storage solution. 

Now, up to 1,000 characteristic values are recorded every 0.4 milliseconds – nearly 80TB of data every 24 hours. This data is immediately stored and pre-analyzed in real-time at the edge on powerful compact servers, enabling further evaluation using artificial intelligence and machine learning. These characteristic values leverage huge amounts of streaming image, X-ray and IoT data to detect and predict abnormalities throughout the metal stamping process. 

The WZL team found that once the system was implemented, it could be scaled without constraint. “No matter how many sensors we use, once we set up the analytics pipeline and the data streams, we don’t have to address any load-balancing issues,” said Philipp Niemietz, Head of Digital Technologies at WZL. 

With conditions like speed and temperature under constant AI supervision, the machinery is now able to automatically adjust itself to prevent any interruptions. By monitoring the machines in this way, WZL have also enhanced their predictive maintenance capabilities. Learn more about how you can leverage Dell Technologies edge solutions.


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IT Leadership

Anil Bhatt, Global Chief Information Officer at Elevance Health, joins host Maryfran Johnson for this CIO Leadership Live interview, jointly produced by and the CIO Executive Council. They discuss using AI in predictive healthcare, blockchain collaborations, the future of digital healthcare, global innovation trends and more.

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