By Drew Robb
Predictive analytics is the use of data, statistical algorithms, and artificial intelligence (AI) and machine learning (ML) techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened and assess what will happen.
Predictive analytics is being used far more in the enterprise. However, there is some confusion around it. Let’s examine what it is, how it differs from other areas of data analytics, and how it is used in the real world.
Ajay Khanna, CEO and Founder of Tellius, gives an example of inventory management during the peak holiday season. By applying predictive analytics models to in-house data over a certain time period, retailers can better understand consumer behavior, such as buying patterns, likelihood to return, and in-store foot traffic. This level of insight can help them forecast product demand, improve customer experience, and reduce operational costs through better staff and resource management.
“Retailers can reach guests with personalized offers based on past data and reliably predict and anticipate future purchases,” said Khanna.
To dig deeper into predictive analytics, jump ahead:
- Predictive Analytics History and Growth
- How Does Predictive Analytics Work?
- Predictive Analytics Models
- Predictive Analytics: Forward Looking
- Benefits of Predictive Analytics
- Business Use Cases for Predictive Analytics
- Use Cases by Sector
- Developing Predictive Analytics Capabilities
- Challenges and Limitations of Predictive Analytics
- Future of Predictive Analytics
Predictive analytics arguably began in the 1940’s with early, manual versions of computers. Notable innovations were accomplished within government agencies, like Alan Turing’s Bombe machine and the Manhattan Project’s Monte Carlo simulation to predict the behavior of atoms during a chain reaction. When computers came to the fore in the 1950’s, research organizations were able to make predictions about weather patterns and product lifetimes.
Predictive analytics, then, has been around for decades. But more organizations are now turning to it to improve their bottom line and competitive advantage. Why now? Computing power has increased dramatically, analytics software is more interactive and easier to use, and the embrace of the cloud has put analytics in the hands of more people at all skill levels. As a result, predictive analytics is no longer the exclusive domain of quantitative experts, statisticians, and data scientists.
“Now analysts, line-of-business experts, and front-line workers are applying predictive analytics to improve efficiency and effectiveness,” said Peterson. “With increased competition and challenging economic conditions, organizations across industries are looking to transform data into better, faster business decisions.”
Predictive analytics has emerged as a powerful tool for organizations large and small. The ability to apply machine learning to large volumes of data and uncover hidden patterns is increasingly valuable in fields as diverse as agriculture, manufacturing, transportation, financial services, healthcare, retail, and cybersecurity.
Of course, businesses have always used data to forecast events and make business decisions. However, the volume and complexity of today’s data has changed the equation. Machine learning and artificial intelligence can spot patterns that fly below human perception and processing. As a result, predictive analytics is increasingly viewed as a competitive differentiator.
According to a report from online research service Statistica, the global predictive analytics market is projected to grow from $5.29 billion USD in 2020 to nearly $42 billion USD in 2028. Organizations use predictive for a wide range of purposes, but some of the leading use cases include: analyzing consumer behavior, managing supply chains, cutting costs, and making strategic decisions about business operations, including financial forecasting.
A variety of vendors offer predictive analytics solutions, either as stand-alone software or built into enterprise applications, including enterprise resource planning (ERP) and customer relationship management (CRM) platforms. Some are available on the desktop and others in the cloud as software as a service (SaaS). This includes the likes of AWS, Google, IBM, Microsoft, Oracle, Salesforce, SAP, SAS, Tableau, Teradata, TIBCO, and ThoughtSpot. While these solutions vary greatly, the common denominator is to extract actionable results from data.
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Predictive analytics represents a distinctly different category of analytics than data mining, business intelligence, and more conventional analytics methods. It ventures beyond basic data sorting and reporting and enters the realm of analysis through statistical methods, machine learning, and deep learning. In its most advanced form, it moves into the category of prescriptive analytics, which offers highly specific outcomes and recommendations based on different decisions or scenarios.
Essentially, algorithms tap statistical methods to parse through different types of structured and unstructured data. This may consist of historical records such as point of sale (POS) or purchase histories or human or network behavior. It can also include social media, online browsing patterns, and other data.
Gartner notes that there are five primary components to predictive analytics:
- An emphasis on prediction rather than description, classification, or clustering.
- Rapid analysis measured in hours or days rather than the usual months of traditional data mining and BI.
- An emphasis on the business relevance of insights.
- A focus on ease of use, thus making tools more accessible to line-of-business users.
- Predictive analytics tools pull data from numerous sources.
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A predictive analytics solution generates predictions using models and methods that often revolve around four core techniques.
The regression model approach is frequently referred to as “what if” analysis. It estimates the relationship between independent variables and then builds a model that can make predictions about future scenarios and impacts. Regression models can incorporate correlations (relationships) and causality (reasons). Manufacturers and retailers often use this method to predict things like demand and fashion trends.
With classification models, data scientists plug in past data and histories. The predictive analytics solution labels the data and then uses an algorithm to identify patterns, including correlations. As new data arrives, it’s added to the system. Fraud detection and cybersecurity typically use classification models.
This technique searches for common attributes and characteristics and then places them in groups. Clustering models are ideal for finding hidden patterns in systems. The technique is frequently used to identify patterns of fraud and theft.
The ability to view data over days, months, or years delivers additional perspective, which can be plugged into a predictive model. The time-series model is frequently used in healthcare and marketing for tasks as varied as optimizing staffing to predicting human behavior based on a complex set of factors.
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Without analytics, data is just a series of zeros and ones. But with analytics comes insights, better decisions and improved outcomes. It turns data into value.
“In general, if you aren’t talking about predictive analytics, you’re talking about descriptive or prescriptive analytics,” said Jerod Johnson, Senior Technology Evangelist, CData. “Descriptive analytics shows what has already happened through data mining, helping you to identify trends and patterns. Predictive analytics adds modeling and machine learning to predict possible future outcomes and probabilities.”
Mathias Golombek, CTO for Exasol, explains prescriptive analytics as a category that takes data and turns it into actionable insights and decisions. You could call it operational BI or analytics, which can be implemented using either traditional SQL or data science languages scripts. The key is to be as real-time relevant as possible and take direct decisions out of data.
“That’s why most of those applications are written in software code and trigger actions across your business chain,” said Golombek. “One example would be to automatically optimize the prices for your e-commerce shop by crunching all kinds of relevant data about your customers, products and logistic chains.”
Predictive analytics, as its name suggests, is forward-looking. “Predictive analytics uses historical data and sophisticated models to predict what will happen next, what the optimal outcomes may be, and where to focus effort and resources,” said Jared Peterson, Senior Vice President of Engineering at SAS.
Small, incremental improvements in a marketing campaign, for example, or in a bank’s fraud detection or a manufacturer’s predictive maintenance can lead to big savings and enhanced operations.
Golombek added that predictive analytics brings AI and ML algorithms to the data, enabling businesses to perform analytical decision making and predictions. It mostly uses script languages such as Python or R and applies statistical models that are trained by existing training data.
The benefits of predictive analytics fall into several categories:
As organizations accumulate data and use it to spot patterns and trends, an enterprise can better understand factors that correlate and cause certain conditions to occur. This data can not only be used by humans to build more effective strategies but also be embedded in automated systems. For the latter, AI and machine learning can act automatically and autonomously when a certain set of conditions occur.
By understanding how certain conditions lead to certain outcomes, it’s possible to eliminate intermediate steps and manual processes that require time, money, and other resources. Predictive maintenance, for example, reduces and sometimes even eliminates the need for humans to test and review results for equipment. An organization knows when it’s an optimal time to service a machine or device.
Risk Reduction and Management
Predictive analytics tools can spot operational, regulatory and cybersecurity risks. It can find gaps, vulnerabilities and weaknesses in business plans, financial models, and IT frameworks. This aids in reducing direct costs as well as possible penalties and fines resulting from a failure to abide by regulations and other controls.
Better Competitive Intelligence
Organizations that use predictive analytics well gain deeper insights into business events, trends, and likely outcomes. This information can guide investments, sourcing, research and development (R&D), sustainability initiatives, supply chain decisions, and much more.
Higher Revenues and Increased Profits
When predictive analytics is used successfully in marketing and sales, for example, it results in higher customer engagement and additional purchases. In a best-case scenario, the technology can dramatically boost brand affinity by making communications and interactions highly relevant for customers. They receive messaging at the right time and in the right place.
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The financial services industry, with huge amounts of data and money at stake, has long embraced predictive analytics to detect and reduce fraud, measure and manage risk, maximize marketing opportunities, and retain customers. Banks of all sizes rely on predictive analytics.
Even traditionally sluggish adopters of new technology like manufacturing and government are becoming proponents of predictive analytics. It helps them to improve operations and boost resiliency in the face of economic disruption.
- Mack Trucks and Volvo Trucks use AI and IoT analytics to predict maintenance issues in their connected vehicles. This prevents costly breakdowns.
- Georgia-Pacific relies on AI and IoT analytics to optimize its supply chain and shipping logistics, improve manufacturing equipment efficiency, and reduce downtime.
- The Town of Cary, NC uses predictive and IoT analytics and data from sensors in streams to predict and mitigate the effects of inland flooding. This is a problem many municipalities are experiencing with greater frequency.
Search-powered data intelligence platforms can help businesses simplify the process of mining for key metrics. By combining disparate datasets and delivering information in an easy-to-consume format through powerful visualizations and predictive analytics, businesses get unprecedented access to key insights – without requiring advanced data science skills.
In the realm of subscription services and in customer support, too, organizations want to understand which users and customers are likely to upgrade or likely to churn. Customers are scored against many attributes and criteria to assess their customer health. Any organization concerned with the maintenance of high-value items can build predictive models to understand which and when hardware and software products will fail or come out of compliance.
Here are additional business uses cases for predictive analytics:
Resource Planning and Purchasing
Predictive analytics can provide insights into projected raw materials availability and pricing, including when to purchase raw materials and commodities. These systems operate similarly to AI systems that predict airline prices at travel websites. This type of modeling helps lower costs and optimize inventory.
Quality Control and Predictive Maintenance
Another use for the technology is in quality control and predictive maintenance. Predictive analytics can detect when products have likely become spoiled or damaged during shipment, and it can optimize maintenance and repairs for equipment as far ranging as medical devices and jet engines.
Retailers, financial services companies, healthcare providers, and others are using predictive analytics to improve marketing, tweak products and services, and forecast outcomes, including sales and broader market trends.
A retailer might pick up signals that a customer is inclined to purchase a product or upgrade a service, or a healthcare company might use predictive analytics to better understand how various actions and behaviors reduce the risk of a negative outcome, including on an individual basis.
Security and Risk Management
As attacks have become more sophisticated, it’s increasingly difficult to simply blacklist and whitelist malware or attempt to block packets at the edge of the network. Behavioral-based security is an important component in developing a zero-trust security framework and locking down assets and data in a more comprehensive way. Predictive analytics tools—which harness AI and machine learning—can spot issues before they emerge as full-fledged problems.
Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all relevant data. Other risk-related uses include insurance claims and collections.
Predictive analytics can flag questionable transactions and spot potential fraud. Banks and credit card companies use predictive technology—increasingly systems linked to geolocation data provided by an individual’s smartphone—to determine whether a purchase is valid or questionable.
This approach offers benefits for customers, who are no longer subjected to frequent emails and text messages that ask them to call a financial services firm to validate transactions. Suspended accounts and other issues are particularly troublesome when a person is traveling overseas and has to make a long-distance call to the bank to verify transactions.
Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. High-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.
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Healthcare organizations leverage predictive analytics to manage the care of patients by predicting their diagnoses and properly staffing hospitals and clinics for future infections.
Supply chains use predictive analytics to better manage inventory and avoid overstocking, and adjust prices based on predicted demand and availability of component parts.
Predictive analytics helps deploy models to audio recordings between support staff and customers to improve agent performance, reduce call durations, gather additional customer information and elevate the overall customer experience.
To make capacity management more seamless, hotels are applying predictive models to data over a certain period so that they can better forecast, plan for, and improve on guest services while simultaneously reducing operational costs through better staff, inventory, and other resource management.
Deploying predictive algorithms to historic student data can identify early indicators of declining student performance as well as the surrounding factors that may contribute to this. Additionally, predictive models applied to teacher, department or regional metrics expand the possibilities of what data-driven insights can do to improve the performance of education systems.
HR and Recruitment
Organizations tend to hire based on an analysis of the job candidate’s interview performance, job references, network, and formal credentials, which are all historical data points. The process is outdated and subjective. “The expense of a bad hire is at least 30% of their salary, but hiring a person who isn’t the best person for the job also presents significant opportunity costs,” said Satish Kumar, CEO of Glider AI. “A predictive analysis of talent quality is the future; it eliminates hiring based on formal credentials, with a focus on skill and cultural fit, while removing natural hiring biases.”
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More advanced predictive analytics capabilities are also taking shape. For example, organizations are turning to digital twins to simulate complex models and understand how different factors impact real world results. Moreover, wineries are using AI with data to understand how climate change impacts their grape crops, while some vintners are beginning to use these methods to identify land that will be ideally suited for viticulture as climate change unfolds.
Meanwhile, gaming companies use predictive analytics algorithms to render 3D graphics faster by eliminating the need to generate certain pixels on GPUs. The system performs extrapolations, and the technique saves computing cycles and cuts energy consumption.
In fact, the latter example demonstrates how data scientists can combine predictive analytics with deep learning techniques. Neural networks can digest huge volumes of data and spot obscure patterns and trends in video, audio, text, and other forms of unstructured data.
For instance, voice recognition or facial recognition might analyze the tone or expression a person displays, and a system then responds accordingly. An application like Google Mail can predict the next word or phrase a person is likely to use and present it as a choice, and Open AI’s ChatGPT constructs entire paragraphs on almost any topic based on text input.
Although predictive analytics offers many benefits, it isn’t without some caveats and potential pitfalls. There are several factors that organizations must tune into in order to use the technology successfully.
The Role of Predictive Analytics and How it Generates Value
Although predictive analytics delivers visibility into the future, it isn’t a crystal ball. Some factors, such as stock market performance, are far too complex to predict. In other cases, numerous other factors that intersect with predictive analytics impact the results.
For example, a marketing group may possess excellent data about customer behavior but fail in a campaign because it has developed subpar content, developed a haphazard approach, or used the predictive data poorly.
The Need for Accurate and Up-to-Date Data
When organizations use old or irrelevant data they wind up with wildly inaccurate results. In order to extract value from data, it must be current (in many cases real-time), accurate, and assembled in the right way. This usually requires data scientists along with top-notch predictive analytics and machine learning tools.
The Need for Clear Goals Surrounding Objectives
Predictive analytics in the absence of a clear strategy and goals will inevitably result in failure. Building a framework for the use of predictive analytics requires input from business leaders and, in many cases, various departments and groups. The most successful implementations span people, processes, and technologies.
This framework makes it possible to remap workflows and drive strategic, financial, and other gains through an enterprise and beyond.
The Need for Data Science Expertise
Predictive analytics tools are often designed primarily for data scientists. Even those intended for business analysts and others can require some level of technical knowledge. This may include programming skills such as Python or R, or expertise in statistical modeling methods. There are also a variety of technical issues related to data preparation and cleansing, training algorithms, dealing with data inconsistencies, and deploying models in the real world.
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In our increasingly digitalized world, data volumes are expected to almost double in size from 2022 to 2026, according to IDC. Therefore, the above use cases will probably lose their dominance as predictive analytics spreads to other fields.
“Companies across every industry stand to benefit from predictive analytics capabilities and advanced data management tools,” said Golombek. “As we move into the new year, we expect an uptick in the use of predictive and prescriptive analytics to drive continuous process improvements and data-driven decision-making — as well as help companies sell the right products to the right clients and facilitate better matching of resources and smarter recognition of trends.”
Johnson believes the future is data-driven, and access to data is the key to success for predictive analytics. The increase in accessible computational power and advancements in AI and machine learning technologies allows any business to utilize predictive analytics – not just organizations and industries with historically deep pockets.
“Utilizing real-time, no-code data connectivity solutions can further democratize analytics by allowing business users to build holistic analytics processes across multiple applications and systems,” said Johnson.
Predictive analytics will continue to evolve. As more and more sensors and IoT elements are plugged into IT frameworks, larger volumes of data—along with more granular data—will become more prevalent. It’s likely that future systems will deliver far more detailed insights into consumer behavior, health factors, spending patterns, and even sustainability data used for environmental, social, and governance (ESG) reporting. This includes far more detailed carbon accounting methods.
In addition, data visualization models are likely to become more elaborate and intuitive, including the use of more advanced 3D animations and visual simulations. And with no-code and low-code frameworks, predictive analytics solutions are likely to become easier to use. As various machine learning, deep learning, and AI frameworks improve, predictive analytics will almost certainly become more accurate and dependable for longer-range predictions and projections.
In the end, one thing is entirely clear: Predictive analytics is an important part of today’s business world, and the use of the technology will only increase. The ability to spot patterns, trends, and opportunities is a powerful tool for organizations of all shapes and sizes. It’s a key to unlocking value and future gains.
Samuel Greengard contributed reporting for this article.
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