The term predictive analytics has gained increased attention as a business intelligence (BI) tool, particularly as BI itself has grown in popularity. Predictive analytics software uses mathematical models and algorithms to analyze an organization’s data and provide users with a forecast of future outcomes and events. There are many vendors on the market today that sell predictive analytics tools, so we put together this buyer’s guide to help you better understand the options available. In this guide, we’ll review:
While traditional BI software usually examines past and present trends within a company, predictive analytics solutions look to the future to help decision makers plan ahead. These systems extract and code a company’s historical information to determine patterns. Armed with these patterns, predictive models are then created and used to forecast possible trends and outcomes.
This is not an exact science, and forecasts do contain a margin of error. But the key advantage of predictive analytics software is that it can highlight upcoming opportunities and potentials for risk to improve the quality of decision-making around those events.
Predictive analytics visualization from SAP
There are four types of analytics users should know about, which can aid a business in different ways:
|Descriptive||Descriptive analytics uses incoming data to identify trends occurring in real-time.
This can help answer the question, “What is going on right now?”
|Diagnostic||Diagnostic analytics uses historical data to determine the cause of an event in the past.
This can help answer, “Why did this happen?”
|Predictive||Predictive analytics uses historical data to find trends and uses them to predict future events.
This can help answer, “What will happen next month?”
|Prescriptive||Prescriptive analytics uses both descriptive and predictive data to determine a specific action to take.
This can help answer, “How can I solve this problem?”
Predictive analytics software features can vary greatly from vendor to vendor—as can how basic or advanced they are—but there are a few features found in many systems:
|Predictive modeling||The cornerstone of any predictive analytics software system, predictive modeling is a statistical technique used to predict certain outcomes and behaviors. Models are created using a company’s historic data, then applied to new data to test their accuracy and revised accordingly.|
|Data mining||Data mining is the process of extracting information from a data set in order to identify patterns that can be used to understand other data sets. Often used in tandem with predictive modeling, data mining provides the relational information needed to score the variables used when creating models.|
|Text analytics||Another feature common to predictive analytics software, text analytics allows users to mine textual sources for information, which is then categorized. Because many data sources are made up of unstructured text, as opposed to predefined numerical data, text analytics can be a valuable resource for uncovering and processing information that may otherwise remain unused.|
|Data visualization||Whereas data mining is used to assign relationships to disparate pieces of information, data visualization is a method for viewing those relationships. In other words, it translates predictive insights into charts, graphs or maps that you can then view on dashboards. While data visualization can be considered a more advanced feature, its rise in popularity across many analytics platforms, including BI suites, has added to its commonality in stand-alone predictive analytics systems.|
|R integration||R is an increasingly popular open-source programming language widely used by data miners and statisticians. While R integration is an advanced feature not found in all predictive analytics software, it’s a powerful analytical tool that can boost the abilities of a system by allowing it to mine large amounts of data faster.|
Before evaluating predictive analytics software, it’s important to determine what you plan to use it for. Some of the most common uses of these systems are found in the following industries:
Banking and financial. Predictive analytics software has been a mainstay of the banking and financial industries, namely to predict credit scores and help with fraud prevention, and continues to find broad application there today. Financial advisors are also finding predictive analytics useful in helping forecast market behavior.
Retail. By providing a forward-looking analysis of customer behavior, predictive analytics can be used in retail to optimize marketing, pricing and product distribution along with sales forecasts and site selection.
Healthcare. Predictive analytics is finding wide use in the healthcare industry, particularly as a means to improve patient care, help with disease prevention and improve hospital management and administration.
As you begin to evaluate predictive analytics software, there are a few important trends to be aware of:
Greater ease-of-use. As traditional BI systems become more user-friendly for business professionals and others lacking IT experience, predictive analytics is closely following suit. Visualizing predictive data has become highly valuable to organizations, and vendors are creating software that allows decision-makers to more easily grasp and act on the insights they provide with everything from charts and bar graphs to heat maps and scatter plots.
Movement to the cloud. As more predictive analytics systems are being offered in the cloud, per-use and subscription-based opportunities are growing. This may be an attractive option for businesses that lack the in-house staff needed to set up and decipher complex statistical modeling or the funds to host an on-premise system.
Increased adoption beyond financial services. While the banking, financial services and insurance industries continue to hold the largest market share for predictive analytics, the growth in customer data being compiled by retailers and manufacturing companies has fueled an increase in more industry-specific software as well. This is evident in the healthcare industry, for example, where predictive analytics systems specifically designed for use in this market are becoming increasingly available.
Overall market growth. The rapid growth in the amount of data gathered by companies combined with technological advances in how it’s able to be processed is fueling a significant expansion in the predictive analytics market. This growth will continue to be a driving factor in the increase of predictive analytics products made available to consumers, along with an increase in the functionality breadth of these products.
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