Overview
Predictive analytics is an advanced analytics discipline that uses a combination of current and historical data to find patterns indicative of likely future events, trends, or business outcomes.
It's among the most highly valued branches of analytics for enterprise-scale organizations across multiple industries, from finance and logistics to telecommunications and manufacturing.
Like all forms of data analytics, predictive analytics rests on a foundation of massive datasets. Three of the five Vs of big data—volume, variety, and veracity—apply here, as it's not feasible for even a cutting-edge analytics engine to generate realistic predictions without a great deal of information as its baseline. The data in some of these sets can be weeks, months, years, or decades old; other sets may include key performance indicators (KPIs) generated as recently as several hours ago.
Parsing these vast swaths of data requires the careful use of various mathematical, statistical, and data science techniques. Some of these principles, like linear and logistic regression analysis, are based on centuries-old—but still sound—mathematics, while others, like decision tree modeling and data mining, are considerably more modern. Coupled with parameters—variables that address the entirety of specific categories under examination within data sets—these methods help pinpoint patterns to predict occurrences and situations that will likely occur. Sometimes—though not always—predictive insights include roughly specified times at which various future outcomes will unfold.
In a way, predictive analytics is a fascinating fusion of the classical and the cutting edge—primarily because it wouldn't be possible without state-of-the-art technologies. This stems most obviously from the sheer volume of data, which is too great for human data scientists and analysts to contend with manually. But the pace, efficiency, and efficacy that even smaller enterprises require to gain actionable insights and tangible business value from analytics also can't be left to human manual analysis. Database technologies and business intelligence (BI) software solutions were among the earliest tools used to help automate and streamline data analysis at scale, and these days, few tech trends have greater influence on analytics than artificial intelligence (AI) and machine learning (ML).
Today, it's common to see the predictive analytics process compared and contrasted with prescriptive analytics. But this shouldn't be viewed as a matter of the former being better or worse than the latter.
Rather, prescriptive analytics is an extension of its predictive predecessor: It recommends or prescribes specific actions when certain conditions are met or information states reached. Descriptive analytics, diagnostic analytics, and potential events postulated via predictive modeling all factor into prescriptive analytics' findings. The practice uses algorithms (often ML-based), mathematical techniques, and business rules to choose among several actions aligned to an objective—such as improving overall enterprise performance—and recognize various requirements or constraints.
In a nutshell, prescriptive analytics maps out potential paths one can take to either realize—or avoid—outcomes projected by predictive models, document past events, or both. While there will be specific circumstances in which one method makes more sense to use than the other, prescriptive and predictive analytics are, overall, most effective as complementary enterprise data analysis tools.
Reliability and accuracy set modern predictive analytics apart from past methods used to forecast sales, inventory, scheduling, occupancy, revenue, and a wide variety of other critical business KPIs. Predictive analysis is especially useful for helping companies find and take advantage of patterns within data—whether to highlight risks and opportunities, examine relationships with third-party partners, or optimize supply chain management.
Arguably, the most important benefit of predictive analytics—regardless of specific application—is its potential to majorly improve enterprise decision-making.
Company leaders and the heads of various business units can't rely solely on direct knowledge, training, and intuition to make the right calls in critical situations—or small ones that can snowball into larger issues. These turning points may affect any or all of the following: bottom-line margins, jobs, key business operations, the customer experience—and even, in fields like healthcare, patients' lives. Only with the foundation of sound data, experienced data professionals, and a predictive analytics solution capable of leveraging past events to envision future possibilities can enterprise-scale organizations enable consistent, reliably strong decision-making.
Other noteworthy benefits of predictive analysis are tied to decision-making in one way or another. Risk mitigation, demand forecasting, and predictive maintenance all exemplify this fact.
Ultimately, human choice—likely an alliance of key stakeholders—will determine how each of these organizations proceed from the findings of their models. But regardless of outcome, predictive analytics gave enterprise leaders the support they need to make via actionable insights in all of these situations.
Let's take a closer look at notable application- or industry-specific uses of predictive data analysis:
Organizations in virtually any sector can maximize marketing campaigns using predictive analytics. Looking at past consumer behavior, larger trends affecting consumer demographics, industry performance indicators, records of customer service interactions, and various other data points allows marketers to craft more targeted and effective marketing.
Sales, in turn, can leverage strong marketing material to encourage customer purchases and solicit their feedback. Precise sentiment analysis will then help sales personnel retain the most valuable customers by tailoring offers and promotions even more directly to customers' interests.
Healthcare providers use predictive analytics to determine which patients are most at risk, identify the biggest hazards, and prescribe optimal courses of care. Meanwhile, health insurers can leverage predictive analytics techniques to track adherence to prescribed care. They can also project demand for the various voluntary benefit offerings that have become increasingly prevalent in recent years.
Airlines, cruise lines, hotels, and restaurants can use insights from predictive analytics regarding customers' travel, lodging, and dining habits to determine pricing and promotion strategies. While factors such as procurement, overhead costs, and market demand all play a part here as well, there aren't many businesses that live or die by customer satisfaction as much as those in travel and hospitality. The verticals are all quite competitive and organizations need any advantage they can get.
While banks and financial institutions are perhaps the most obvious targets of fraud, it affects many other organizations. Far beyond credit or check fraud, this issue can range from spurious claims filed with insurers to payroll fraud against businesses of all sizes. A predictive model can be a formidable weapon in the anti-fraud trenches, analyzing transactions to find anomalous locations, times, devices, and other indicators of unauthorized activity. Fraud detection is one of the most notable use cases for analytics strategies driven by machine learning algorithms.
While forms of automation and AI have been a part of analytics since fairly early in the discipline's history, the rise of machine learning is nothing short of an analytics game-changer.
Because ML models constantly grow more effective by ingesting new data and being periodically retrained to improve their performance, they make analytics more dynamic than ever. Also, due to ML's enablement of natural language processing (NLP), a business user doesn't have to be a data scientist or analyst to input queries: They can type—or, often as not, use voice recognition—to submit data requests in sentences that they'd regularly type or speak, instead of in snippets of Python or R.
Furthermore, within the ML subcategory of deep learning, predictive analysis using neural networks becomes sophisticated enough to be valuable in complex fields like oncology and pharmaceuticals: For example, a convolutional neural network (CNN) can accurately classify skin cancer with the veracity of a dermatologist, while a deep feed-forward neural network (D-FFNN) can predict drug toxicity more effectively than other deep learning models. Although deep learning requires a great deal of computing power and the technology is still in development, the evidence for its potential thus far is quite promising.
Making predictive analytics work for your organization starts with envisioning a clear objective: You must have at least one intended application for this practice in mind, or it will become a resource drain. It can be relatively straightforward to start—e.g., conducting sentiment analysis regarding a particular product to gauge the likelihood of customers buying similar goods in the future. You can move on to other use cases if this first one is a success.
Next is data collection and preparation, which entails not only identifying every relevant data set but also filtering out redundant or noisy data that will skew results. Model development follows—devising appropriate algorithms and pairing them to the data you wish to predictively analyze. Then comes training, in which the model "learns" the data inside and out, which will eventually enable it to make predictions. Various assessments test the accuracy of the model's initial predictions, and recoding or other tweaks take place as needed. Finally, it's deployed in its working environment—and brought back for retraining and maintenance when necessary.
Carrying all of this out, of course, requires strong enterprise data architecture, dedicated data pipelines, effective data warehousing, object stores to enable data lakes for unstructured data, and more. You'll also need a foundation of cutting-edge technology—an effective cloud deployment, AI/ML solutions, and a powerful data platform.
Predictive analytics uses various models and techniques to forecast future outcomes based on historical data. Main types include:
The importance of different types of data can vary depending on the specific application and industry. Kinds of data used include:
While predictive analytics offers numerous benefits, there are several potential drawbacks and risks to consider:
These risks can be mitigated by ensuring data quality, anonymizing personally identifiable information (PII), regularly checking for biases, and using transparent models.
Teradata VantageCloud enables your enterprise to operationalize predictive analytics in the manner most valuable to the business. The complete cloud and data analytics platform integrates and streamlines data from disparate sources across your enterprise so it's easily accessible to predictive models. With its AI/ML-driven ClearScape AnalyticsTM engine, VantageCloud can help launch numerous predictive models quickly and support analytics operations that generate actionable insights and form the basis of sound strategy.