What is Predictive Analytics?
Predictive analytics leverages the combined power of algorithms, statistical data, and machine learning to access future needs and outcomes with optimized analytics models, helping companies better use resources and insights.
Historical data drives the future
Encompassing topics like Big Data and data mining, predictive analytics lets enterprises and other organizations better understand future behaviors and identify opportunities. How? By using historical data combined with an assortment of other methods such as deep or machine learning and data modeling, predictive analytics models and predictive analytics techniques become invaluable, letting data scientists uncover correlations that, in turn, can build stronger internal processes and automate IT infrastructure with increased precision.
Interpret and predict in any industry
Predictive analytics algorithms are used by industries of all kinds, from entertainment and healthcare to cybersecurity and weather. For example, in retail, predictive analytics can help interpret and predict buyer behavior, helping stores better manage their inventory or create personalized buyer recommendations. In areas like manufacturing, companies can proactively monitor equipment and maintenance patterns to minimize downtime. And even in sports, predictive analytics models can better forecast the value of players over time using statistics and other data.
Solve any type of problem
In almost any industry, predictive analytics, its associated machine learning, and other data inputs can provide tremendous value when it comes to solving new and pre-existing problems. Data scientists and the companies or organizations they work for can understand people, processes, profits and loss, and any number of future trends.
The history of predictive analytics
Predictive analytics has been in use for decades, but it is only with the rise of less expensive, faster, and more powerful computers that the true potential of predictive analytics could be actualized.
Beginning in the 1940s, progenitors to modern computing, and other achievements like linear programming and computational modeling, helped accelerate governmental interest in predictive analytics’ potential. Most famously, the Manhattan Project, which developed the atomic weapon technology that helped end World War II, used a manual analysis called the Monte Carlo simulation to predict how atoms behave during a nuclear reaction.
In the 1950s, computer development continued with the development of nonlinear programming and computer-based heuristics and the invention of hard disk drives (HDDs), laying the groundwork for other innovations such as floppy disks and database management systems (DBMS).
By the 1970s and 1980s, predictive analytics was being used to predict stock prices, while scientists like E.F. Codd established the theoretical foundation for relational databases and relational database management systems, including application programming interface (API) and Structured Query Language (SQL).
And by the 1990s and 2000s, vast databases of information were being used to personalize and optimize digital and marketing experiences thanks to the rise of machine learning and cloud computing technology.
What are the types of predictive analytics? How do they work?
Predictive analytics is not one standalone technique. It can be divided into several models, each with its own purpose, function, and benefit for any number of use cases. Overall, the data that is found can be used to deepen understanding of historical data points, identify anomalous instances across many datasets, and predict future trends.
By sourcing historical data, this model gathers and sorts data into categories. Businesses across many industries use this model to solve complex problems and discover new opportunities. Its broad applications makes it a common model for determining application approvals, determining likelihood to default on payments, identifying fraudulent transactions, and beyond.
This version of predictive analytics separates data into select groups based on shared criteria. That data can be sorted into hard or soft clusters. Hard clustering is a straight-forward categorization, while soft clustering assigns data probability when clustered. In general, clustering models are often deployed in marketing, where it can help marketers plan strategies for specific audiences.
Forecast models predict the quantifiable future value of an object based on historical numerical data. One of the primary reasons forecast modeling is widely used is because it permits multiple input parameters, such as weather and local events, and so offers greater versatility across many industries. For example, retail stores can extrapolate how many customers or sales to expect on any given week based on past traffic and schedule coverage accordingly.
Like its name suggests, an outliers model identifies data that rests outside the norm within a single dataset or multiple datasets and helps draw conclusions from those anomalous data points. Like other models, multiple factors can be considered, ranging from prices and locations to payment histories. And for these reasons, outliers models are particularly useful in finance and manufacturing, where they can help identify potential fraudulent activities or indicate equipment inefficiencies and malfunctions.
Time Series Models
Unlike other models, time series models use anomaly data rather than historical data, where time is used as the primary input to gain insights into future time periods. Their greatest advantage is that they can determine how specific metrics will change over a specific time based on select variables like weather or past sales, often in conjunction with multiple forecasts, helping businesses map growth or better strategize next steps.
How do enterprises use predictive analytics?
Modern predictive analytics have evolved beyond linear and logistic regressions, with SMBs and enterprises looking for any data-based solution to gain a competitive edge. Today’s predictive analytics help businesses sort through digital mountains of data, harness machine and deep learning, and shine a light on newfound insights: analyzing customer behavior and predicting market fluctuations and where the next big breakthrough will happen. In addition, predictive analytics enable data scientists of all kinds to collaborate in real time more than ever before, using new workflow apps and hybrid, multi-cloud infrastructures to help facilitate data analytics.
And while many industries use data and intelligent infrastructure to different ends, their ultimate benefits largely remain the same.
In retail, predictive analytics aggregate customer data to identify sales trends and deliver more personalized marketing, such as enhanced cross-selling, up-selling, and remarketing campaigns. Data of this kind can even be leveraged into inventory management and future product development.
Similarly, in addition to helping manage and connect large systems of plants and other assets, the energy industry uses data to forecast and plan utility production and demand due to seasonal or adverse weather and even predict outages before they happen.
Manufacturers use predictive analytics to keep watch over their assets as well. However, in these cases, it is more so for maintenance and performance monitoring. Manufacturers can identify any drops in efficiency or predict when potential failures will happen to reduce expensive downtime or repairs.
In insurance, predictive analytics can serve as an extra security measure by detecting potential fraudulent claims through comparison to historical data. Artificial intelligence is also used to personalize insurance quotes and premiums by factoring relevant risks for each applicant and approving or rejecting based on that criteria.
Even governmental agencies can leverage the power of data to inform new policies and public initiatives that can transform daily life in meaningful ways.
HPE and predictive analytics
HPE partners with organizations, SMBs, and enterprises alike to deliver solutions for predictive analytics, providing the intelligent infrastructure and expertise they need to meet demand. With help from offerings like HPE InfoSight, HPE GreenLake, HPE Nimble Storage, and HPE Pointnext, HPE works closely with partners to achieve their unique needs across many industries.
In the case of Basefarm, a Norwegian IT service provider, HPE helped manage the storage needs for the company’s massively expanding customer base and maintain business continuity. By adding a customized infrastructure and capability set, Basefarm achieved new storage speed resolution of virtual machine (VM) issues by 80 percent and sustained an average bandwidth of 22 TB per second.
Elsewhere, with the University of Purdue’s Agronomy Center for Research and
Education (ACRE), HPE helped invigorate their digital farming projects. Together, they are revolutionizing agricultural research with real-time field data automation and technology like the Internet of Things (IoT) to measure, analyze, and adjust plant moisture levels.
Also with Purdue, HPE is aiding researchers at the Center of Global Soundscapes in recording and analyzing biological data in order to accelerate ecological insights around the world using a combination of edge computing and data analytics. As a result, researchers are gaining deeper knowledge into how wildlife communities are impacted by certain environmental factors