Looking for best options for affordable tools for successful data forecasting in 2021? Read on to see our top 3 picks!
Developments in AI and machine learning have made data analytics platforms more powerful than ever.
The following are some of the best, and most affordable, tools on the market for data scientists wanting to build predictive models or create forecasts.
Not only are these tools affordable, but they also support a wide variety of features that make them a good fit – whether you need visualization tools, an easy-to-configure ML pipeline or a platform that can quickly connect to existing data sources…
Introducing the 3 most affordable tools for successful data forecasting in 2021:
- A complete data analytics platform. General-purpose complement to the company’s business intelligence platform, QlikView.
- Features include its proprietary associative analytics engine, a suite of data visualization tools and AI technology.
- The platform offers cloud support, but can also be used offline with a local device.
- One plan. Price is $30 per month per user, billed annually. A free trial is available.
Qlik Sense is a complete data analytics platform, built to be the general-purpose complement to the company’s business intelligence platform, QlikView.
Features of Qlik Sense include a proprietary associative analytics engine, a suite of data visualization tools and AI technology.
The platform offers cloud support, but can also be used offline with a local copy of the software.
Qlik Sense has two main editions — the Business edition for small teams and the Enterprise edition for organizations with multiple departments — and several pricing plans.
The Business edition has one plan, which costs $30 per month per user, billed annually. There are two plans for the Enterprise edition — Analyzer, which costs $40 per month per user, and Professional, which costs $70 per month per user.
Enterprise plans offer a few additional features not in the Business edition, including data alerts, managed app access and larger app sizes.
For businesses and individuals that want to test the software before committing to a subscription, a free trial is available.
- RapidMiner is an open and extensible cloud-based data science platform.
- The platform is best for data scientists who want to use ML algorithms to analyze data and create predictive models.
- Like Qlik Sense, it also comes with a suite of visualization tools.
- The platform is designed to be accessible to both data scientists and general users.
- For individuals, RapidMiner is highly afford
able. The browser-based RapidMiner Go costs $10 per month. The offline RapidMiner Studio is free, and comes with a 30-day trial of RapidMiner Studio Enterprise. - Enterprise plans, however, are significantly more expensive, starting at $5,000 per month per user, according to Capterra data.
RapidMiner is an open and extensible cloud-based data science platform.
The platform is best for data scientists who want to use ML algorithms to analyze data and create predictive models, but is built to be accessible to both experienced data scientists and general users. If you want a data forecasting and analytics tool with a gentle learning curve, RapidMiner will likely be a good fit.
Like Qlik Sense, it also comes with a suite of visualization tools, which will help users create graphs, charts and reports that break down the insights they’ve uncovered.
Other features will help you streamline some of the more frustrating parts of data analysis, For example, TurboPrep is RapidMiner’s tool for handling the task of data cleaning — which is necessary for ensuring data sets are ready for analysis.
For individuals, RapidMiner is highly affordable. The browser-based RapidMiner Go costs $10 per month. The offline RapidMiner Studio is free, and comes with a 30-day trial of RapidMiner Studio Enterprise.
Enterprise plans, however, are significantly more expensive, starting at $5,000 per month per user, according to user data from Capterra.
- ML and AI analytics tool designed for data scientists and general-purpose users.
- Primarily built for business analytics.
- Features help users put analytics into practice quickly. For example — a visual environment allows you to design an ML pipeline with minimal to no coding. Easy exporting of visuals and analysis to third-party tools, like Slack.
- Four pricing tiers. Free is free, Individual is $50 per month billed annually, Team is $100 per month billed annually and Enterprise plan pricing varies based on need.
- Because prices are consistent no matter the number of users you’ll have, this product is likely best for individuals who want a free solution, or mid-size teams that can’t afford per-user billing.
Datagran is a machine learning and AI analytics tool designed for data scientists and general purpose users.
The platform is primarily built for business analytics, meaning that it may not be as useful for individuals outside the business world.
Part of what makes the tool unique is its features designed to help users put new analytics pipelines into practice quickly.
For example, the platform includes a visual environment that allows you to design an ML pipeline with minimal to no coding.
The visual environment also includes tools for easy exporting of visuals and analysis to third-party apps, like Slack and Twilio, and data formats like CSV.
The platform has four pricing tiers — Free, Individual, Team and Enterprise. Free is free, Individual is $50 per month billed annually, Team is $100 per month billed annually and Enterprise plan pricing varies based on need.
These prices are consistent no matter the number of users you’ll have, meaning that this product is likely best for individuals who want a free solution, or mid-size teams that can’t afford per-user billing.
The Best Affordable Tools for Data Forecasting
These tools are some of the best available if you need an affordable solution for data analytics and forecasting. Some, like Qlik Sense and Datagran, will be better for business applications, while RapidMiner will be a good fit for anyone who needs an easy-to-learn tool for general use.
A Guest Post By...
This blog post was generously contributed to Data-Mania by Shannon Flynn. Shannon Flynn is a freelance blogger who covers business, cybersecurity and IoT topics.
