By Samuel Greengard

Understanding complex events in today’s business world is critical. The explosion of data analytics—and the desire for insights and knowledge—represents both an opportunity and a challenge.

At the center of this effort is predictive analytics. The ability to transform raw data into insights and make informed business decisions is crucial. Today, predictive analytics plays a role in almost every corner of the enterprise, from finance and marketing to operations and cybersecurity. It involves pulling data from legacy databases, data lakes, clouds, social media sites, point of sale terminals and IoT on the edge.

It’s critical to select a predictive analytics platform that generates actionable information. For example, financial institutions use predictive analytics to understand loan and credit card applications, and even grant a line of credit on the spot. Operations departments use predictive analytics to understand maintenance and repairs for equipment and vehicles, and marketing and sales use it to gauge interest in a new product.

Top predictive analytics platforms deliver powerful tools for ingesting and exporting data, processing it and delivering reports and visualizations that guide enterprise decision-making. They also support more advanced capabilities, such as machine learning (ML), deep learning (DL), artificial intelligence (AI) and even digital twins. Many solutions also provide robust tools for sharing visualizations and reports. 

Types of Predictive Models

Predictive models are designed to deliver insights that guide enterprise decision-making. Solutions incorporate techniques such as data mining, statistical analysis, machine learning and AI. They are typically used for optimizing marketing, sales and operations; improving profits and reducing costs; and reducing risks related to things like security and climate change.

Also see: Best Data Analytics Tools 

Four major types of predictive models exist:

Regression models

A regression model estimates the relationship between different variables to deliver information and insight about future scenarios and impacts. It is sometimes referred to as “what if” analysis.

For example, a food manufacturer might study how different ingredients impact quality and sales. A clothing manufacturer might analyze how different colors increase or decrease the likelihood of a purchase. Models can incorporate correlations (relationships) and causality (reasons).

Classification models

These predictive models place data and information in categories based on past histories and historical knowledge. The data is labeled, and an algorithm learns the correlations. The model can then be updated with new data, as it arrives. These models are commonly used for fraud detection and to identify cybersecurity attacks.

Clustering models

A cluster model assembles data into groups, based on common attributes and characteristics. It often spots hidden patterns in systems. In a factory, this might mean spotting misplaced supplies and equipment and then using the data to predict where it will be during a typical workday. In retail, a store might send out marketing materials to a specific group, based on a combination of factors such as income, occupation and distance.

Time-Series models

As the name implies, a time-series model looks at data during a given period, such as a day, month or year. Using predictive analytics, it’s possible to estimate what the trend will be in an upcoming period. It can be combined with other methods to understand underlying factors. For instance, a healthcare system might predict when the flu will peak—based on past and current time-series models.

In more advanced scenarios, predictive analytics also uses deep learning techniques that mimic the human brain through artificial neural networks. These methods may incorporate 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 can then respond accordingly.

Also see: Top Business Intelligence Software 

How to Select a Predictive Analytics Platform

All major predictive analytics platforms are capable of producing valuable insights. It’s important to conduct a thorough internal review and understand what platform or platforms are the best fit for an enterprise. All predictive analytics solutions generate reports, charts, graphics and dashboards. The data must also be embedded into automation processes that are driven by other enterprise applications, such as an ERP or CRM system.

Step 1: Understand your needs and requirements

An internal evaluation should include the types of predictive analytics you need and what you want to do with the data. It should also include what type of user—business analyst or data scientist—will use the platform. This typically requires a detailed discussion with different business and technical groups to determine what types of analytics, models, insights and automations are needed and how they will be used.

Step 2: Survey the vendor landscape

The capabilities of today’s predictive analytics platforms is impressive—and companies add features and capabilities all the time. It’s critical to review your requirements and find an excellent match. This includes more than simply extracting value from your data. It’s important to review a vendor’s roadmap, its commitment to updates and security, and what quality assurance standards it has in place. Other factors include mobile support and scalability, Internet of Things (IoT) functionality, APIs to connect data with partners and others in a supply chain, and training requirements to get everyone up to speed.

Step 3: Choose a platform

Critical factors for vendor selection include support for required data formats, strong data import and export capabilities, cleansing features, templates, workflows and embedded analytics capabilities. The latter is critical because predictive analytics data is typically used across applications, websites and companies.

A credit card check, for example, must pull data from a credit bureau but also internal and other partner systems. The APIs that run the task are critical. But there are other things to focus on, including the user interface (UI) and usability (UX). This extends to visual dashboards that staff uses to view data and understand events. It’s also vital to look at licensing costs and the level of support a vendor delivers. This might include online resources and communities as well as direct support.

Top Predictive Analytics Platforms

Here are 10 of the top predictive analytics solutions:

Google Looker

Key Insight: The drag-and-drop platform is adept at generating rich visualizations and excellent dashboards. It ranks high on flexibility, with support for almost any type of desired chart or graphics. It can generate valuable data for predictive analytics by connecting to numerous other data sources, including Microsoft SQL and Excel, Snowflake, SAP HANA, Salesforce and Amazon Redshift. It also includes powerful tools for selecting parameters, filtering data, building data-driven workflows and obtaining results. While the platform is part of Google Cloud and it is optimized for use within this environment, it supports custom applications.

Pros

  • The highly flexible platform runs on Windows, Macs and Linux. It offers strong mobile device support.
  • Generates strong data-driven workflows and supports custom applications.
  • Users give the platform high marks for its interface and ease of use.

Cons

  • Lacks some customization features found in other analytics solutions.

IBM SPSS Modeler

Key Insight: The predictive analytics platform is designed to put statistical data to work across a wide array of industries and use cases. It includes powerful data ingestion capabilities, ad hoc reporting, predictive analytics, hypothesis analysis, statistical and geospatial analysis and 30 base machine learning components. IBM SPSS Modeler includes a rich set of tools and features, accessible through sophisticated dashboards.

Pros 

  • Offers excellent open-source extensibility through R and Python, along with support for numerous data sources, including flat files, spreadsheets, major relational databases, IBM Planning Analytics and Hadoop.
  • Visual drag-and-drop interface speeds tasks for data analysts and data scientists.
  • Works on all major operating systems, including Windows, Linux, Unix and Mac OS X.

Cons

  • Not as user friendly as other platform analytics platforms. Typically requires training to use it effectively.

Qlik Sense

Key Insight: Qlik Sense is a cloud-native platform designed specifically for business intelligence and predictive analytics. It delivers a robust set of features and capabilities, available through dashboards and visualizations. Qlik Sense includes AI and machine learning components; embedded analytics that can be used for websites, business applications and commercial software; and strong support for mobile devices. The solution supports hundreds of data types, and a broad array of analytics use cases.

Pros

  • Offers powerful drag-and-drop interface to enable fast modeling.
  • A “Smart Search” feature aids in uncovering and connecting complex data relationships.
  • Provides rich visualizations along with highly interactive and powerful dashboard features.

Cons

  • Can be pricy, particularly with additional add-ons.

Salesforce

Key Insight:  The widely used CRM and sales automation platform includes powerful analytics and business intelligence tools, including features driven by the company’s AI-focused Einstein Analytics. It delivers insights and suggestions through specialized AI agents. A centralized Salesforce dashboard offers charts, graphs and other insights, along with robust reporting capabilities. There’s also deep integration with the Tableau analytics platform, which is owned by Salesforce.

Pros

  • Powerful features and highly customizable views of data.
  • Integrates with more than 1,000 platforms. Strong data ingestion capabilities.
  • A generally intuitive UI and strong UX make the platform powerful yet relatively easy to use.

Cons

  • No trial or free version available.

SAP Analytics Cloud

Key Insight: Formerly known as BusinessObjects for cloud, SAP Analytics Cloud can pull data from a broad array of sources and platforms. It is adept at data discovery, compilation, ad-hoc reporting and predictive analytics. It includes machine learning and AI functions that can guide data analysis and aid in modeling and planning. A dashboard offers flexible options for displaying analytics data in numerous ways.

Pros

  • Accommodates large data sets from a diverse range of sources.
  • Offers highly customizable, interactive charts and other graphics.
  • Includes powerful ML and AI components.

Cons

  • Not as visually rich as other platforms.

SAS Visual Analytics

Key Insight: The low-code cloud solution is designed to serve as a “single application for reporting, data exploration and analytics.” It imports data from numerous sources; supports rich dashboards and visualizations, with strong drill-down features; includes augmented analytics and ML; and includes robust collaboration and data sharing features. The platform also includes natural language chatbots that aid business users and other non-data scientists in content creation and management.

Pros

  • Delivers fast performance, even with large data volumes.
  • Supports a wide array of predictive analytics use cases.
  • Includes smart algorithms that accommodate predictive analytics without the need for programming skills.

Cons

  • Expensive, particularly for smaller organizations.

Tableau Desktop

Key Insight: The enormous popularity of Tableau is based on the platform’s powerful features and its ability to generate a wide range of appealing and useful charts, graphs, maps and other visualizations through highly interactive real-time dashboards. The platform offers support for numerous predictive analytics frameworks, including regression models, classification models, clustering models, time-series models. Non data scientists typically find its user interface accommodating and easy-to-learn.

Pros

  • Excellent UI and UX. Most users find it easy to generate useful analytics models and visualizations.
  • Supports flexible, scalable and highly customizable predictive analytics use cases.
  • Tight integration with Salesforce CRM. Generates excellent visualizations.

Cons

  • Sometimes pulls considerable compute resources, including CPUs and RAM. It can also perform slowly on mobile devices.

Teradata Vantage

Key Insight: Teradata Vantage delivers powerful predictive analytics capabilities, including the ability to use data from both on-premises legacy hardware sources and multicloud public frameworks, including AWS, Azure and Google Cloud. The solution also works across virtually any data source, including data lakes and data warehouses. It supports sophisticated AI and ML functionality and includes no-code and low-code drag and drop components for building models and visuals. The company is especially known for its fraud prevention analytics tools, though it offers numerous other predictive tools and capabilities.

Pros

  • Highly flexible. Powerful SQL engine supports broad, deep and complex queries.
  • Accommodates huge workloads, almost any type of query, and delivers fast and efficient data processing.
  • Generates dynamic visualizations and an easy-to-use yet powerful interface.

Cons

  • Some users complain about a lack of documentation and training materials.

TIBCO Spotfire

Key Insight: TIBCO Spotfire offers a powerful platform for performing predictive analytics. It connects to numerous data sources and includes real-time feeds that can be highly filtered and customized. The solution is designed for both business users and data scientists. It includes rich visualizations and supports customizations through R and Python.

Pros:

  • TIBCO offers a strong API management framework.
  • Delivers rich visualizations and immersive interactive visual analytics.
  • Strong support for real-time decision making across multiple industries and use cases.

Cons:

  • Customizations can be time consuming and difficult.

ThoughtSpot

Key Insight: The analytics cloud delivers highly flexible self-service predictive analytics. The query engine is designed to search on virtually any data format and understand complex table structures over billions of rows. It offers powerful search and filtering capabilities that extend to natural language queries. ThoughtSpot also provides a powerful processing engine that generates a range of visualizations, including social media intelligence. The solution includes a machine learning component that ranks the relevancy of results.

 Pros

  • The platform gets high marks from users for its user interface and ease of use.
  • ThoughtSpot delivers a high level of flexibility, including an ability to search Snowflake, BigQuery and other cloud data warehouses in real time.
  • Natural language search reduces the need for complex SQL input.

Cons

  • Users complain that data source connectivity isn’t as robust as other analytics platforms.

Also see: Top Cloud Companies

Predictive Analytics Vendor Comparison Chart

Company Key Product Pros Cons
Google Looker Excellent drag-and-drop interface with appealing visualizations and a high level of flexibility Support is almost entirely online
IBM SPSS Modeler Excellent open-source extensibility; strong drag-and-drop functionality Not as user friendly as other analytics solutions
Qlik Qlik Sense Powerful and versatile platform with strong modeling features Some complaints about customer support
Salesforce Salesforce Highly customizable; strong integration with other platforms and data sources Expensive
SAP Analytics Cloud Supports extremely large datasets and has powerful capabilities Visualizations are not always as appealing as other platforms
SAS Visual Analytics High performance platform that supports numerous data types and visualizations May require customization
Tableau Tableau Desktop Outstanding UI and UX, with deep Salesforce/CRM integration Can drain CPUs and RAM
TIBCO Spotfire Powerful predictive analytics platform with excellent visual output Steep learning curve
Teradata Advantage Highly flexible with a powerful SQL engine; generates rich visualizations Some users complain about an aging UI.
ThoughtSpot ThoughtSpot Flexible, powerful capabilities with a strong UI and UX Visualizations sometimes lag behind competitors

 

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