The emergence of GenAI has transformed the data analytics industry, making it easier than ever for data leaders to gain insights, predict outcomes, and make data-driven decisions. Yet, research by Accenture shows that only one in five organizations are able to unlock the intrinsic value of their data.
If your current BI tools aren’t helping you leverage the power of AI and predictive analytics, it’s time to search for other alternatives. To help you compare features and make a well-informed decision, we’ve created a list of the best predictive analytics tools. But first, let’s look at some considerations you should know to invest in a solution that generates the highest ROI for your business.
Table of contents:
For business leaders, it’s tough to know if you’re making the right decisions. But it doesn’t have to be that way. Investing in a modern data analytics tool with advanced predictive capabilities helps you better predict customer behaviors and make strategic decisions that drive business value. Here are some factors to consider for a better ROI:
1. Check for accuracy and performance
Accuracy is crucial when it comes to selecting a predictive analytics platform. That’s why it is important to evaluate the tool’s track record in this area. Consider using metrics like confidence scores, confusion matrix, and F1 scores to evaluate the model's accuracy. Additionally, check user reviews from reputable sources like Gartner Peer Insights and G2 for up-to-date information about the platform's capabilities and performance.
2. Opt for a user-friendly interface
If the predictive analytics solution's interface is complicated, it restricts users from fully unlocking the value of their data. Look for features like drag-and-drop interfaces, customizable filters and widgets, and intelligent query builders to reduce time to insight. A user-friendly interface also allows analysts to explore data freely and create interactive visualizations for engaging data storytelling.
3. Focus on scalability
As the volume of data grows exponentially, you need a scalable data analytics solution that can handle complex predictive queries. The tool should seamlessly integrate with fragmented data sources, helping you gain a unified view of your operations and enabling you to quickly train and deploy ML models. Also, look for predictive analytics solutions that offer AI assistance for data preparation.
4. Allows human-in-the-loop feedback
Human oversight is essential for detecting and mitigating biases in AI models. All too often, AI/ML models can predict incorrect or biased outcomes, leading to confusion and misinformed decision-making. The ‘human-in-the-loop feedback’ capability allows you to limit such biases, promoting trust and transparency. Look for solutions that let users fine-tune the model’s parameters to improve its performance.
Alteryx is a data analytics platform that helps analysts prepare, blend, and analyze data. Users can build visualizations, get AI-assisted insights, and monitor real-time data. In addition, users can also leverage a set of predictive analytics tools to anticipate business outcomes. These features use R programming language, letting users investigate data, build different predictive models, run A/B testing, group records, and create forecasts.
Core features:
Integration with cloud data sources
Low-code interface to create dashboards, widgets, and reports
AI-assisted insights
Data preparation and exploration
Advanced data analytics
Qlik Sense is known for its associative data modeling capabilities, which help analysts explore data, create visualizations, and uncover insights. It uses a proprietary modeling language called Qlik AutoML, which allows users to define and manage data analytics models and metrics in a structured way. You can even run ML experiments in the Qlik Cloud Analytics hub, use the output to train a model and create charts to visualize prediction data.
Core features:
Associative data modeling
Real-time data monitoring
Multiple integrations
AI insights and recommendations
Data visualization
Developed by Amazon, QuickSight is a data visualization and analytics platform. It helps data pros collect, analyze, and visualize business data. QuickSight’s features and capabilities also include a predictive analytics tool known as Amazon SageMaker Canvas. This tool assists analysts and data scientists in building ML models using a no-code visual interface. Doing so helps you explore data, understand relationships, and make accurate predictions.
Core features:
Interactive dashboards
Serverless architecture
QuickSight Q (AI-assistance)
Amazon SageMaker Canvas
Available on mobile devices
SAP Analytics Cloud is an enterprise planning and predictive data analytics platform with features like data blending, data modeling, and data visualization. It connects to all SAP applications and offers AI capabilities to help users build dashboards and explore data. Additionally, you can run what-if simulations, create time-series forecasts, and build predictive models to make informed business decisions.
Core features:
Data connectivity with cloud databases
Simulations and forecasting
Interactive dashboards
Collaboration and report sharing
Available on mobile devices
Cognos Analytics is IBM’s data analytics platform that comes with a drag-and-drop interface to help users extract insights and gain data context. This predictive analytics solution is equipped with AI and ML capabilities that allow users to clean and prep data from multiple sources and create ML models to forecast outcomes. Cognos Analytics has recently announced a set of AI features that help users create interactive data visualizations and gain insights from their data.
Core features:
Dashboard creation
AI/ML tools
Multi-cloud connectivity
Data modeling
Forecasting
Available on desktop and mobile devices
MicroStrategy is another predictive analytics solution offering a wide range of reporting and advanced forecasting capabilities. With a unified interface for data discovery and advanced analytics, users can model their data and build and train predictive models to discover relationships and anticipate future outcomes. Although the platform also offers AI capabilities for data preparation and visualization, they are less flexible.
Core features:
Dashboard creation
Augmented analytics
Integration with multiple data sources
AI features
Mobile app availability
While the predictive analytics platforms we’ve explored above offer unique capabilities, they are all built for power users like analysts and data scientists. To become a data-driven organization, you need a predictive analytics solution that supports a true self-service analytics use case.
ThoughtSpot is that partner. With our AI-Powered Analytics, everyone can leverage data and build successful strategies regardless of their technical expertise. Our search-based algorithm allows business users to ask natural language questions about their business data and instantly returns interactive data visualizations. Even power users can model their data to uncover hidden insights.
Whether you are from finance, sales, or product marketing, ThoughtSpot’s AI-powered dashboards empower everyone to swiftly uncover actionable insights.
Schedule a demo today to see how ThoughtSpot puts users in the driver’s seat.
