July 5, 2023

Top Prescriptive Analytics Tools & Software (2024)

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Updated July 5, 2023

AI-generated images and text are the talk of the town. Depending on who you ask, these new tools are either heralds of the future, or harbingers of professional doom. But while everyone is discussing the ramifications of machine learning on how to work like an artist, it’s being quietly trained how to make decisions like an executive through prescriptive analytics tools, as well.

Assistance in predicting and proactively responding to risks and opportunities can be a major market advantage. Below are seven of the best prescriptive analytics tools to help you forecast the business weather, and prepare for the storms and seasons ahead.

IBM Decision Optimization – Best for machine learning


Pros

  • Advanced optimization algorithms
  • Integration with machine learning
  • Scalability
  • Customizable models

Cons

  • Limited documentation
  • Inflexible licensing
  • Requires expertise
  • Prescriptive analytics: IBM Decision Optimization uses mathematical and computational sciences to suggest decision options to advantage businesses, enhance decision-making, and increase operational efficiency.
  • Mixed-integer programming (MIP): This feature enables users to model and solve problems where the decision variables are a mix of continuous and integer variables.
  • Constraint programming: Helps solve complex combinatorial problems by specifying the constraints that need to be satisfied, offering an alternative to traditional mathematical programming.
  • Heuristic methods: For complex problems where exact methods might be too slow, IBM Decision Optimization provides fast, high-quality heuristic solutions.
  • Scenario analysis: Allows businesses to consider a range of outcomes and conditions for multiple scenarios to better manage risks and uncertainties.

Free plan available

Developer: $199/user/month


IBM Decision Optimization emerged as the top choice for machine learning based prescriptive analytics due to its powerful capabilities and seamless integration with IBM’s machine learning suite. It facilitates intricate decision-making with a robust, advanced programming approach, and provides a unique blend of heuristic, metaheuristic, and mathematical programming techniques. With a capacity for handling enormous data sets, it caters excellently to complex, real-world business challenges. Lastly, its ability to integrate models into applications and operational systems made it a standout choice for actionable insights.

 

IBM has been a major player in computer technologies for decades, but have long shifted away from producing hardware and devices. Instead, they’ve been making their mark in developing cutting-edge machine learning systems, and those efforts have placed them at the forefront of business intelligence and prescriptive analytics.

While Watson has received most of the attention in the news, IBM’s more impressive project has largely gone unnoticed. IBM Decision Optimization is an entire suite of BI tools that allow large-scale enterprises to turn their current operational data into a powerful optimization tool. 

Alteryx – Best for end-user experience


Pros

  • Intuitive workflow
  • Data blending capabilities
  • Advanced analytics
  • Data visualization

Cons

  • Complex for beginners
  • Limited collaboration features
  • Cost
  • Self-service data analytics: Alteryx enables quick and precise insights delivery with an end-to-end platform for data discovery, blending, and analysis.
  • Drag-and-drop workflow: This feature ensures easy creation and alteration of analytical workflows through an intuitive user interface.
  • Predictive analytics: With over 60 pre-built tools, Alteryx allows the harnessing of advanced analytics for spatial and statistical analysis, and predictive modeling, without any coding required.
  • Data connectors: Alteryx boasts native data connectors to numerous sources such as SQL, Oracle, Excel, Access, and supports cloud-based data from AWS, Google Analytics, Salesforce, etc.

Free trial available

Designer Cloud: Starting at $4,950/user/year

Designer Desktop: $5,195/user/year

Alteryx’s intuitive drag-and-drop interface simplifies complex data workflows, making data analysis accessible even to non-coders. This feature, coupled with a comprehensive suite of pre-built analytic models and an extensive library of connectors, allows users to derive actionable insights seamlessly. Alteryx’s commitment to user training through Alteryx Academy further enhances its usability. Finally, the availability of Alteryx Community, a platform for peer support and learning, underlines why it is our top choice for best end-user experience.

There are plenty of professionals who can benefit from advanced business analytics, but not all of them have the expertise of a data scientist or software engineer. Alteryx is an intelligence suite built to empower those who don’t to make the most of their data.

Designed to be low- or no-code and user-friendly, the Alteryx platform offers robust analytics and prescriptive insights to end users, even if they have minimal technical literacy. The system is built on a foundation of automated machine learning (AutoML), which lets the algorithm do most of the heavy lifting, limiting the amount of oversight required to train it.

Put simply, Alteryx offers the kind of drag-and-drop simplicity that’s usually only found in less sophisticated descriptive or diagnostic analytics. 

KNIME – Best for data science flexibility on a budget


Pros

  • Open source
  • Extensive integration options
  • Extensive analytics capabilities
  • Strong community support

Cons

  • Resource-intensive workflows
  • Limited inbuilt visualizations
  • Complex deployment
  • Visual workflow editor: KNIME provides an intuitive, drag-and-drop style visual interface for building data workflows. This makes the process of data manipulation, analysis, and visualization easy to understand and execute.
  • Extensive integration capabilities: It supports a wide range of data formats and systems including SQL, NoSQL, Hadoop, and various cloud storage options, enabling seamless data integration from diverse sources.
  • Open source and customizable: KNIME is open source, which offers the flexibility to customize the platform according to specific needs. Users can contribute new functionalities via KNIME’s node extension system.
  • Rich analytics tools: KNIME houses a comprehensive set of tools for data mining and machine learning algorithms, statistical functions, and data visualization, serving as a robust platform for data-driven decision making.

Contact KNIME for a customized quote

While KNIME lacks the sleek, push-button UIs that most other BI tools present, this isn’t necessarily a drawback, depending on use case. For those in need of high levels of customization, and the ability to shape the models and learning algorithms to their data pipelines, workflows, and native environments, KNIME has a lot to offer.

Additionally, KNIME is free to use for individual users, and its more DIY structure facilitates lower costs than other solutions when adding to the user base. KNIME’s “data sandbox” is perfect for data teams that want to supercharge their efforts, but don’t need to offer widespread end-user access to the tools themselves.

If Alteryx is a novice analyst’s secret weapon, KNIME is a data scientist’s. Built to maximize a technical expert’s ability to control, organize, and process data, even from a wide variety of sources. Using a node-based, visual programming interface, those with sufficient technical expertise can effectively build their own analytics tool.

In other words, it’s the Lego system of prescriptive analytics.

Looker by Google – Best for data modeling


Pros

  • Built-in IDE for data modeling
  • Versatile data access
  • Enhanced collaboration
  • Integration with R

Cons

  • Dependency on LookML
  • Limited pre-built visualization types
  • Performance scaling issues reported
  • LookML data modeling: Looker’s proprietary language, LookML, offers a code-based approach to defining business logic and data relationships, providing granular control over how data is queried and visualized.
  • Data blocks: Pre-modeled pieces of business logic or whole datasets from third-party sources that can be natively integrated into your existing models.
  • Looker actions: Allows users to take meaningful actions on insights directly from within Looker, like changing data in your database, sending an email, or creating a task in project management software.
  • Embedded analytics: Looker’s Powered by Looker platform enables you to embed real-time analytics and data visualizations directly into your workflows, applications, or portals.

Viewer User: $30/user/month

Standard User: $60/user/month

Developer User: $125/user/month

Choosing Looker as the best option for data modeling is backed by its ability to create powerful, scalable data models using its LookML language. This declarative syntax allows teams to curate and centralize business metrics, fostering better data governance. Plus, its in-database architecture means models can handle large datasets without performance trade-offs. Looker’s versatility and adaptability, including its integration capabilities with SQL and other data sources, make it ideal for businesses desiring a robust and intuitive data modeling platform.

Powered by Looker is a Google Cloud product designed to prioritize data pipelines, data lineage, and data modeling control.

The BI app is web-native (because, well…it’s from Google). It offers embedded analytics features, and can be presented to end users with intuitive interface controls. That’s secondary to its core intent, however.

Looker’s architecture is built with a dedicated data modeling layer. The flexibility and oversight this facilitates will be well-received by a data science team tasked with implementing the tool. It makes for frictionless integration with the data pipeline, and easily interfaces with the data warehouses an organization already has in place. Additionally, it facilitates robust version control.

With solid security protocols, and RESTful API design, it’s a prescriptive analytics option that’s easy to deploy, and suitably protected.

Tableau – Best for data visualization


Pros

  • User-friendly interface
  • Wide range of visualization options
  • Powerful data handling
  • Strong community and resources

Cons

  • Data connectivity issues
  • Limited data preparation
  • Costly for large teams
  • Data blending: Tableau enables users to blend data from multiple sources, providing a unified view of multiple datasets.
  • Drag-and-drop interface: With Tableau’s intuitive interface, users can create complex visualizations using a simple drag-and-drop mechanism.
  • Real-time data analysis: Tableau’s real-time data analysis allows for up-to-the-minute business insights and decision making.
  • Interactive dashboards: Tableau’s interactive dashboards let users drill down into charts and graphs for more detail.
  • Tableau Public: A free service that allows users to publish data visualizations to the web. These can be embedded into webpages and blogs, shared via social media or email, and made available for download to other users.
  • Mobile-ready dashboards: Tableau’s dashboards are optimized for tablets and smartphones, enabling users to access their data anytime, anywhere.

Free plan available

Tableau Viewer: $15/user/month

Tableau Explorer: $40/user/month

Tableau Creator: $70/user/month

Tableau was chosen as the best option for data visualization due to its vast capabilities in turning complex data into comprehensible visual narratives. Its intuitive, drag-and-drop interface makes it accessible for non-technical users while still offering depth for data experts. The wide array of visualization options, from simple bar graphs to intricate geographical maps, allows for highly customized presentations of data. Furthermore, with robust real-time analytics, mobile-ready dashboards, and secure collaboration tools, Tableau proves an invaluable asset for data-driven decision-making across various business contexts.

Tableau offers a solid blend between the back-end functionality of a KNIME-style sandbox, and the end-user simplicity of platforms like Alteryx. It features some of the smoothest, most aesthetically pleasing dashboards and UIs on the market, living up to artistic implications of the brand name. 

A full-fledged analytics suite, Tableau can be white-labeled and embedded for secondary and tertiary use as needed. But its advanced prescriptive functions can go toe-to-toe with nearly any other vendor in the space.

Best of all, with robust visualization options and intuitive user controls, it’s incredibly easy to build dashboards that offer the most value for every level of access privileges and technical expertise. 

Azure Machine Learning – Best for data privacy


Pros

  • Top-notch security
  • Built-in privacy features
  • Enterprise-level control

Cons

  • Dependency on Microsoft ecosystem
  • Limitations in free tier
  • Enterprise-grade MLOps: With Azure, you can build, deploy, and manage machine learning models efficiently at scale, fostering robust operationalization and lifecycle management of your models.
  • Automated machine learning: This feature makes the selection and tuning of machine learning models hassle-free, increasing productivity and reducing the possibility of errors.
  • On-premises, multi-cloud and at-the-edge deployment: Azure provides the flexibility to deploy your machine learning models wherever you need them, accommodating various business needs and compliance requirements.
  • Explainability and fairness of models: Azure includes built-in features for model interpretability and fairness, helping to ensure your machine learning models are transparent and equitable.
  • Security and compliance: Azure Machine Learning provides advanced security controls and privacy-preserving features, including differential privacy and confidential computing, helping organizations meet compliance requirements.
  • Integrated notebooks: Azure offers Jupyter notebooks as part of the service, enabling streamlined and familiar development workflows.

Free plan available

Studio Standard: $9.99/user/month plus $1 per studio experimentation hour, Azure subscription required. Unlimited modules and storage, experiments can last up to 7 days with a maximum of 24 hours per module.

Web API Dev/Test: $100.13/user/month, includes 100,000 transactions and 25 compute hours per month. Overage rates are $0.50 per 1,000 transactions and $2 per API compute hour.

Web API Standard S1: $1,000.06/user/month, includes 2,000,000 transactions and 500 compute hours per month. Overage rates are $0.25 per 1,000 transactions and $1.50 per API compute hour.

Web API Standard S2: $9,999.98/user/month, includes 50,000,000 transactions and 12,500 compute hours per month. Overage rates are $0.10 per 1,000 transactions and $1 per API compute hour.

As part of the Azure environment, it (and by extension its users) benefits from all of the security features used to protect the cloud service at large. The same way Office 365 enables increased controls regarding access privileges, data storage and sharing, and identity management, Azure Machine Learning makes safeguarding connected data pipelines and workflows. 

Partnering with third party vendors often means sacrificing some peace of mind. As one of the most trusted names in the tech industry, Microsoft’s Azure platform means having your analytics cake and eating it too.

Microsoft’s Azure prescriptive business analytics solution brings to the table a lot of the best features found in other options on this list. For starters, it boasts impressive amounts of training data for their AI tools, just as IBM does. It provides predefined ML algorithms to facilitate simpler, faster implementation. And offers more customizable options for those with the expertise to make use of it. 

What drives most organizations to choose it, however, is the sense of security and confidence in data privacy it offers.

RapidMiner Studio – Best for data mining and aggregation


Pros

  • Excellent data processing capabilities
  • Model validation mechanisms
  • Parallel processing support

Cons

  • Scripting limitations
  • Memory consumption
  • Complex advanced features may be overwhelming for learners
  • Automated data science: RapidMiner’s automated data science feature simplifies complex data transformation, model selection, and validation tasks.
  • Multi-threaded execution: Capitalizing on your machine’s computational capabilities, RapidMiner offers multi-threaded execution for faster data processing and model building.
  • Rich data preprocessing tools: It provides a vast range of preprocessing operators, allowing users to clean, transform, and enrich their data efficiently.
  • Predictive modeling: RapidMiner supports numerous machine learning algorithms, enabling users to create advanced predictive models.
  • Visual workflow designer: Its drag-and-drop visual interface lets users design complex data workflows with ease, minimizing the need for code.

Professional: $7,500/user/month

Enterprise: $15,000/user/month

AI Hub: $54,000/user/month

The most compelling attribute of this vendor is the level of nuance it provides during data discovery. ETL processes can be defined with a number of granular modifications, making the process of importing and scrubbing data a lot easier. Even messy, unstructured, or poorly organized data can be quickly parsed and processed once the correct automations are in place.

Data almost always has value, but for humans to be able to leverage it in any real way, it has to be formatted in a way that makes sense (both for us, and for the AI tools). This is RapidMiner’s strong suit: turning convoluted piles of information and turning them into visualizations, dashboards, and prescriptive insights. 

Wrapping up our list is RapidMiner Studio. RapidMiner shares a lot in common with KNIME, including a node-based GUI and a “build your own analytics”-style implementation. And while the interfaces are a bit of aesthetic upgrade from KNIME’s, setting up and deploying RapidMiner requires similar levels of technical expertise to navigate.

RapidMiner, unlike other options in this list, is on-prem only at present. A drawback for some use cases, but a major security advantage for others.

Prescriptive analytics

A quick breakdown of the four common functions of business intelligence:

Descriptive Analytics The “What” Used to organize data, parse it, and visualize it to identify trends.
Diagnostic Analytics The “Why” Used to analyze trends, examine their progress over time, and establish causality.
Predictive Analytics The “When” Used to compile trend and causality data, and extrapolate upcoming changes to anticipate outcomes.
Prescriptive Analytics The “How” Used to predict possible scenarios, test possible strategies for ROI or loss potential, and recommend actions.

Prescriptive analytics is among the most advanced business applications for machine learning and data science. It requires a significant amount of AI processing, and depends on large volumes of reliable data. More importantly, like a human employee, it can be trained to respond to inputs and scenarios over time, improving the recommendations it outputs.

For a deeper dive on prescriptive analytics, and where it fits into the data analysis ecosystem, check this article on data analysis software.

“Always tell me the odds”: Why prescriptive analytics matter

Prescriptive analytics isn’t a crystal ball. What it is might be closer in analog to an independent consultant, or a military tactician. It surveys the battlefield, considers numerous scenarios based on likelihood, parameters and circumstantial constraints, intensity of effects on final outcomes, and the options or resources available to the organization.

Then, after simulating the possibilities, and comparing current plans to potential alternatives, it makes recommendations to promote the most positive results. 

In short, it doesn’t remove the uncertainty from business planning, it reduces the level of disruption caused by unanticipated events or a lack of forethought.

Forecasting outcomes like this can be used to achieve a number of important business goals:

  • Preventing or mitigating loss
  • Minimizing or avoiding risk factors
  • Optimizing processes, schedules, and routes
  • Improving resource utilization and limiting downtime
  • Anticipating opportunities

With prescriptive analytics, businesses can work proactively, instead of reactively. It’s reassurance and validation when things go according to plan, and it’s a safety net when things take a turn for the catastrophic. Either way, you’ve explored the possibilities via numerous scenarios and simulations, and you’re as prepared as possible for what the future brings.

Choosing the best prescriptive analytics software

Remember, “crazy prepared” is only a negative until everyone needs what you’ve prepared in advance. Hopefully, this list of prescriptive analytics tools will help you find the solution that positions your business as the Batman of your industry. If not, check out our in-depth embedded analytics guide for more insight on how to choose a provider for your use case.

Looking for the latest in Business Intelligence solutions? Check out our Business Intelligence Software Buyer’s Guide.

Featured partners

1 Yellowfin

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Yellowfin’s intuitive self-service BI options accelerate data discovery and allow anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.

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2 Wyn Enterprise

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Wyn Enterprise is a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, & white-labeling in any internal or commercial app. Built for self-service BI, Wyn offers limitless visual data exploration, creating a data-driven mindset for the everyday user. Wyn's scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.

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3 Zoho Analytics

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Finding it difficult to analyze your data which is present in various files, apps, and databases? Sweat no more. Create stunning data visualizations, and discover hidden insights, all within minutes. Visually analyze your data with cool looking reports and dashboards. Track your KPI metrics. Make your decisions based on hard data. Sign up free for Zoho Analytics.

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Methodology and selection process

At TechnologyAdvice, we assess a wide range of factors before selecting our top choices for a given category. To make our selections, we rely on our extensive research, product information, vendor websites, competitor research and first-hand experience. We then consider what makes a solution best for customer-specific needs. By defining business needs, we can determine the essential BI features organizations in various sectors require, and select platforms that will cover all bases. Reputable providers known for their ease of use and customer satisfaction are added to our compilation list for further analysis. We then evaluate each solution on the list based on the features they offer, considering the platform’s usability, integration capabilities, customization options, mobile access, and any other relevant functionalities. Price plans, hidden fees, customer reviews, and customer support are also assessed in the selection process. Technology Advice writers will often take advantage of free trials and demos to get a first-hand user experience of available software. Finally, we curate a comprehensive list based on the previously stated factors, ensuring readers have the necessary tools to make an informed decision.

FAQs

What is prescriptive analytics?

Prescriptive analytics is the branch of data analytics that uses machine learning and computational modelling to suggest actions for optimal outcomes based on given parameters.

How do I choose the best prescriptive analytics platform for my business?

To choose the best prescriptive analytics platform for your business, assess your specific needs such as data volume, type of analytics required, scalability, user-friendliness, budget, and review the features, integrations, support, and customer reviews of potential platforms.

TechnologyAdvice is able to offer our services for free because some vendors may pay us for web traffic or other sales opportunities. Our mission is to help technology buyers make better purchasing decisions, so we provide you with information for all vendors — even those that don't pay us.
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