Managing data has always been a challenge for businesses. With new sources and higher volumes of data coming in all the time, it’s more important than ever to have the right tools in place. Predictive analytics tools and software are the best way to accomplish this task. Data scientists and business leaders must be able to organize data and clean it to get the process started. The next step is analyzing it and sharing the results with colleagues.
Whether you need to up your existing analysis system or establish this capability, take a look at thes round up of predictive analysis tools. You’ll find tools and software for both people who are experts at working with data and people who are not.
The Alteryx Analytic Process Automation Platform specializes in no-code and low-code analytics building blocks to design repeatable workflows. The platform is designed for companies that want to provide self-service analytics and data science for all departments. Alteryx also uses augmented machine learning to help citizen data workers build predictive models. The company’s cloud platform makes it easy to share workflows online, at the desktop and in on-prem data centers and offers built-in integrations with modern cloud ecosystem applications. The Analytic Process Automation Platform puts analytics, data science and process automation in one place by combining data quality and preparation, analytics, data science and automated machine learning and deployment and monitoring into one service. The automation service includes more than 80 natively integrated data sources. Alteryx’s Designer service makes it easy to combine data sets, use code-free and code-friendly tools and produce visual workflows and reports.
Alteryx also provides training and educational information about machine learning on its Data Science Portal.
Alteryx offers a free 30-day license for Designer for business users. For students, educators and career changes, the company offers a free one-year renewable Designer license. Contact the company for detailed pricing information.
Azure Machine Learning
Microsoft’s cloud platform offers business analytics services for the entire machine learning process. This includes preparing data, building and training models, validating and deploying them and managing and monitoring them. According to Microsoft, the platform can increase the ROI of machine learning products, reduce the steps required to train models by 70% and reduce by 90% the amount of lines of code for pipeline. Azure Machine Learning also offers PyTorch Enterprise, a support program for the open-source deep learning framework that allows service providers to develop and offer tailored enterprise-grade support to customers.
Azure ML also offers responsible AI capabilities to make models more transparent and reliable. Features include visualizations, what-if analysis and model explanation graphs. The platform includes algorithms to test models for fairness and an error analysis toolkit to debug errors and improve accuracy.
Microsoft offers 60 compliance certifications as well as beginner and advanced tutorials. There is a free trial for Azure. There is no additional cost to use Azure Machine Learning but users pay for compute as well as other Azure services including Azure Blob Storage, Azure Key Vault, Azure Container Registry and Azure Application Insights. Pricing options can be customized by type of service, region, currency and time frame.
The Lakehouse Platform combines the functionality of a data warehouse and a data lake. Databricks Lakehouse unifies data warehousing and AI use cases on one platform and provides a single data platform across cloud deployments. The warehouse is built on the open source technology Delta Lake which forms the structured transactional layer. According to the company, this open format storage layer delivers reliability, security and performance for both streaming and batch operations and can replace data silos with a single home for structured, semi-structured and unstructured data. Delta Engine is the high performance query engine. The has SQL and performance capabilities, including indexing, caching and MPP processing. The platform also allows direct file access and direct native support for Python, data science and AI frameworks. Cloud partners include AWS, Azure and Google Cloud.
The Databricks Data Science Workspace is a notebook environment that can be used by everyone on the team. Existing notebooks can be imported into a company’s Databricks environment or the free community edition.
Databricks has an academy with numerous role-based learning paths, self-paced learning and instructor-led training. The company also offers speciality badges and certifications for data analysts, data engineers and machine learning scientists. Databricks offers a free trial as well as pay as you go and committed-use discounts options. Contact the company for pricing information.
DataRobot’s AI Cloud Platform supports collaboration for all users from data science and analytics experts to IT and DevOps teams to executives and information workers. The platform includes data engineering, machine learning, MLOps, decision intelligence and trusted AI services. To support decision intelligence, the service has a no-code app builder, AI apps and Decision Flows which create rules to automate decisions. The no-code app builder allows users to turn a model into an AI application without any additional coding. This makes it easier for business users to make AI-driven decisions, according to the company. The apps also include detailed prediction explanations to help users explain any decision made by a model. Users also can use the no-code app builder to perform what-if analysis by changing one or more inputs to create new scenarios and then compare the two results. This transparency allows companies to incorporate feedback from end users and other stakeholders into model revisions.
The company also provides modules for grading existing AI models, setting up policies, rules and controls for production deployments and for generating compliance reports. DataRobot offers options to deploy AI services on any cloud platform, on premise or at the edge.
DataRobot offers a free trial. Contact the company for detailed pricing information.
H2O.ai’s automated machine learning capabilities make it easier to use artificial intelligence with high levels of speed, accuracy and transparency, according to the company. The company’s platform has options for building models and applications as well as monitoring performance and adapting to changing conditions. The services are designed for various roles within a business, including data scientists, developers, machine learning engineers, DevOps and IT professionals and business users.
Services in the platform include data visualization, pre-processing transformers, dataset splitting, outlier detection, feature encoding, per-feature controls and automated validation and cross validation.
Automated machine learning services include:
- Hyperparameter autotuning
- Modeling ensembling
- Automatic label assignment
- Automated model documentation
- Imbalanced dataset handling
- Model leaderboards
- Unsupervised automatic machine learning
The platform also includes a low-code application development framework (Python/R) for user interface creation and machine learning integration.
Services for machine learning operations include a model repository, model deployment and model monitoring.
The company offers fully managed cloud services and hybrid cloud services. H2O.ai offers a free trial of the platform.
IBM’s Statistical Package for the Social Sciences is used for complex statistical data analysis via a library of machine learning algorithms, text analysis, and open source extensibility designed for integration with big data and easy deployment into applications. The package includes a statistics component for ad hoc analysis, a modeler with algorithms and models ready for immediate use and a modeler in cloud pak for data, and a containerized data and AI service for building and running predictive models in the cloud or on premises. Several related products support predictive analytics software for students, teachers and researchers as well as an analytic server to make predictive analytics easier.
Business analysts can use features in the statistics component to:
- Address all facets of the analytical process from data prep and management to analysis and reporting
- Provide automated methods to identify anomalies and statistical transformations to address outliers
- Deliver tables and visualization
- Classify cases into groups and predict values of a target variables based on values of predictor variables
- Enable accurate modeling of linear and non-linear relationships
- Improve forecasts and plans by imputing missing values with expected values using regression and expectation-maximization
IBM recently launched an early access program for beginner and intermediate users to help those groups get started with statistics. The learning modules feature a simplified UI, a guided walk through of the software and a data overview dashboard. This service is in beta and is offered free for 60 days. IBM offers a subscription plan and on-premise licensing editions of SPSS. There are four levels of service: base, standard, professional and premium. Contact IBM for pricing.
IBM Watson Studio
This is IBM’s platform for data science, formerly known as Data Science Experience. The platform includes a workspace and collaboration and open-source tools for data science. Watson Studio is a core offering in Cloud Pak for Data as a Service. The service includes tools to analyze and visualize data, to clean and shape it and to build machine learning models.
The architecture of Watson Studio is built around a project that includes collaborators, assets and tools. Software provided in the studio include:
- Data Refinery: Prepare and visualize data
- Jupyter notebook editor: Code Jupyter notebooks
- RStudio: Code Jupyter notebooks in R and R Shiny apps
- SPSS Modeler: Automate the flow of data through a model with SPSS algorithms
- Decision Optimization model builder: Optimize solving business problem scenarios
Projects integrate with Watson Knowledge Catalog services and Deployment spaces provided by Watson Machine Learning services.
IBM offers a free trial of IBM Watson Studio on Cloud Pak for Data. Contact IBM for pricing of multiple licensing options for IBM Cloud Pak for Data, pay-as-you-go pricing for IBM Cloud Pak for Data as a Service and for IBM Cloud Pak for Data System.
This data science software platform provides an integrated environment for data preparation, machine learning, deep learning, text mining and predictive analytics. It is used for business applications as well as for research, education, training, rapid prototyping and application development. According to the company, the platform is robust enough for data scientists while also being user friendly enough for users in the rest of the company. Features designed for data scientists include:
- 1,500+ native algorithms, data prep and data science functions
- Support for many third party machine learning libraries
- Notebooks and integration with custom Python and R
- Advanced analytics and platform services
Features for business users include:
- Use case templates
- Self-paced online certification by persona
- Full automation options
The RapidMiner AI Cloud service is built for all users with an augmented and guided experience, a visual UI with a minimal learning curve and an explanation of the data and the modeling process.
The company has a RapidMiner Academy and training and certification services. There are also certified global partners for additional support as well as integrations to speed data access and deployment of machine learning models. Contact the company for enterprise pricing information.
Tableau is an end-to-end data and analytics platform that includes security, governance and compliance along with APIs. Tableau creates trust and confidence by establishing controls, rules and repeatable processes across integration, access and oversight, according to the company. Individual components of the platform include services for data prep, CRM analytics, server management and embedded analytics.
Tableau also promises to help customers build a data culture by promoting these values:
- Practicing data-driven behaviors
- Valuing strategic data use
- Encouraging sharing and community
Tableau Blueprint is a methodology for building capabilities required for a data-driven organization that covers strategy, agility and proficiency.
Companies can deploy Tableau via software-as-a-service, Salesforce Hyperforce, public cloud server and containers and on-premises servers. Tableau Creator is $70/user/month billed annually, Explorer is $42/user/month billed annually and Viewer is $15/user/month billed annually. These services are fully hosted by Tableau. For deployments with Tableau server on-premises or in the public cloud, the prices are $70/user/month billed annually for Creator, $35/user/month billed annually for Explorer and $12/user/month billed annually for Viewer. For individuals, access to Tableau Creator is $70/user/month billed annually.
Sisense’s Fusion Platform integrates customized analytics into applications and products to make analytics intuitive and user-friendly, according to the company. The platform has three components for data analysis: Embed, Infusion Apps and Analytics. Embed is an API-first platform that customers can use to build white-labeled analytics into applications and workflows.
Customers can use Infusion Apps to ask questions with natural language queries and conduct analysis in Slack, Google Slides, Microsoft Teams and Salesforce. Analytics has code-first, low-code and no-code options for analyzing and visualizing large volumes of data as well as self-service dashboards and apps. The service also has built-in, code-first statistical and predictive analysis libraries and ML technologies.
Sisense’s Data Connectors integrations cover dozens of other platforms including Airtable, Amazon Redshift, Salesforce Desk.com and Double Click. The company’s marketplace includes add-ons, integrations, data pipelines and infusion apps.
The Sisense Cloud analytics platform provides scalability and agility to analytics operations and encourages collaboration.
Sisense offers a free trial. Contact the company for pricing information.
What is predictive analytics?
Predictive analysis covers statistical techniques for studying data. This includes data mining, predictive modeling and machine learning as methods of making predictions about future events. Predictive analytics has the potential to:
- Spot customers who are likely to cancel a service or not renew it
- Identify transactions that could be fraudulent
- Create a preventive maintenance schedule
Business leaders can use predictive analytics to increase the chances of success for many initiatives or to test a variety of scenarios quickly.
These tools range from no-code tools to data lakes to machine learning algorithms. Businesses can pick a solution that fits the needs and expertise of each department. Some platforms are complete workspaces and others integrate with existing tools. There are options for cloud deployments and on-prem solutions.
Gartner recommends that companies follow these best practices when selecting predictive analysis tools:
- Select individual services or groups of services based on how well these products fit a company’s application needs
- Use automated machine learning services in conjunction with standard language and vision services to add unique attributes to solutions
- Plan for regular enhancements to applications
How does predictive analytics work?
Predictive analytics platforms look at historical data and try to spot patterns. The process relies on data such as customer purchases, weather information or banking habits, statistics such as regression analysis, and assumptions that the future will follow trends from the past.
Some types of predictive analytics software use machine learning to revise algorithms based on learnings from the data collected.
Data experts and business department leaders can use predictive analytics to test new theories and products before committing to these decisions in the marketplace.