Top 10 Predictive Analytics Technologies

Predictive analytics tools can forecast future trends and possibilities
AI Magazine takes a look at the top 10 predictive analytics technologies to help you find ways to strategise in this fast-paced world

In our fast paced world that is subject to constant change through new technological innovations, it can be hard to get any sort of understanding on how to plan and strategies for the future.

Luckily, the same technology that is making everything move at breakneck speed - AI - is also on hand to help us making accurate predictions. 

By employing advanced techniques like machine learning, and using past and present data, predictive analytics tools can forecast future trends and possibilities, uncover hidden patterns and make informed predictions that aid decision-making in various industries.

AI Magazine therefore takes a look at some of the current leading predictive analytics technologies to help businesses decide what’s best for them. 

10. Dataiku

Dataiku is an advanced AI platform designed to help users build and deploy AI solutions. It provides a shared space for coders and non-coders to prepare projects, offering built-in features like charting and statistical analysis. Dataiku's capabilities include machine learning models for forecasting future trends, identifying patterns in data, and making data-driven predictions. It enables users to share analytic dashboards and data products, facilitating data-driven decision-making across the organisation.

9. Akkio

Akkio is a data-driven decision-making tool that enables users to create, deploy, and manage machine learning models without requiring data science knowledge. Its drag-and-drop coding interface and pre-built algorithms simplify predictive model development. Akkio's key predictive analytics feature is its ability to build models for predicting customer churn, fraud detection, demand forecasting, and other business use cases using a no-code approach. It supports programming languages like Java, Python, and C, democratising predictive modelling and allowing anyone to leverage its capabilities.

8. Adobe Analytics  

Adobe Analytics empowers users to mix, match, and analyse data from any source, delivering in-depth analysis, predictive intelligence, and versatile reporting. Its predictive analytics capabilities include machine learning models for forecasting future trends, identifying customer behaviour patterns, and predicting conversion rates. It offers multiple data collection methods, enabling data-driven decision-making through web and mobile apps based on predictive insights. Users can create and manage alerts based on data anomalies detected by predictive models, ensuring timely responses to critical events.


MATLAB creates a productive environment for engineers and scientists, offering a high-level programming language for complex mathematical operations and algorithms. Its predictive analytics capabilities include advanced statistical modelling, time-series forecasting, and machine learning algorithms for predictive modelling. MATLAB's command-line and graphical user interfaces provide an interactive environment for building and testing predictive models, compatible with multiple operating systems. Its drag-and-drop features, online community support, and data visualisation capabilities enhance the predictive analytics workflow.


TIBCO is a future-proof AI tool for forecasting and trend analysis, unlocking real-time data for smarter and faster decision-making. Its predictive analytics capabilities include time-series forecasting, predictive modelling, and machine learning algorithms for identifying patterns and making data-driven predictions. TIBCO offers heterogeneous metadata management, data provisioning, and data discovery features to support predictive analytics workflows. Its dynamic dashboards, interactive visualisation, and mobile access enhance the usability of predictive insights, while its integration with other software facilitates data derivation for predictive modelling.

5. Sisense

Sisense is a machine learning predictive analytics tool that allows users to visualise and analyse data from different sources. Its predictive analytics capabilities include advanced statistical modelling, machine learning algorithms for forecasting, and automated insights generation. Sisense can connect to various data sources, offers data preparation tools, and provides features for data security and governance to support predictive analytics workflows. Its chip methodology delivers fast data analysis, and its business intelligence capabilities make data visualisation of predictive insights easy.

4. IBM Watson Studio

IBM became a leading predictive analytics tools vendor with the acquisition of Statistical Package for the Social Sciences (SPSS) in 2009. IBM has since continued to innovate the vendor's core capabilities and integrated them into its more modern Watson Studio on the IBM Cloud Pak for Data platform. The core capabilities of its modern Watson Studio offering, which resides on the IBM Cloud Pak for Data platform, combines a broad range of analytics functions, including descriptive analytics for summarising historical data, diagnostic analytics for identifying root causes, predictive analytics leveraging techniques like regression analysis, neural networks, and time series forecasting to predict future outcomes, and prescriptive analytics for recommending actions based on predictive insights. 

3. Qlik Sense

Qlik Sense integrates AI into predictive analytics, enabling curiosity-driven exploration at any skill level. Its predictive analytics capabilities include data discovery, mining, ad-hoc analysis, and predictive modelling using machine learning algorithms and statistical techniques. Users can add narratives and context to predictive analytics insights, while guided analytics, custom applications, and self-service visualisation cater to diverse needs. With a free 30-day trial and affordable pricing, Qlik Sense makes predictive analytics accessible to a wide range of users.

2. Microsoft Azure Machine Learning

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Microsoft Azure Machine Learning accelerates and manages the machine learning project lifecycle, making it a powerful platform for predictive analytics. It provides built-in security, compliance, and governance for machine learning workloads used in predictive modelling. Azure Machine Learning offers quick ML model deployment, a centralised platform, and easy experiment creation, streamlining machine learning operations (MLOps) for predictive analytics. Its data-driven decisions foster transparency and accountability, enabling informed decision-making based on predictive insights. Azure Machine Learning's key strengths lie in its ability to deploy web services efficiently, its centralised platform for machine learning, and its user-friendly experiment creation process.

1. Google Cloud BigQuery

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Google BigQuery is a cutting-edge, fully managed data analytics platform designed to handle massive datasets with unparalleled speed and scalability for predictive analytics. Its serverless architecture ensures seamless scalability, allowing organisations to analyse petabytes of data in minutes using machine learning models and advanced analytics techniques for predictive modelling. BigQuery seamlessly integrates with other Google services and offers advanced analytics and machine learning capabilities tailored for predictive analytics use cases. It follows a flexible, pay-as-you-go pricing model and provides robust security and compliance features, making it a powerful solution for data-driven predictive decision-making. BigQuery's key strengths include its ability to fetch data quickly by automatically optimising queries, efficiently manage data across multiple databases, and provide an intuitive interface for analysing new queries through its editor and query builder.


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