Categories   >   Data Analytics Software
Last Reviewed: November 8th, 2024

Best Data Analytics Software Of 2024

What is Data Analytics Software?

Data analytics software is like a translator for complex information. It takes raw data, cleans it, analyzes it, and reveals patterns and trends that would be invisible to the naked eye. Imagine a retail company sifting through mountains of sales data to discover hidden buying habits, like a surge in sneakers on weekends. This solves the problem of information overload, turning data into actionable insights. Its importance is undeniable. By identifying trends and predicting customer behavior, businesses can optimize marketing campaigns, develop targeted products, and ultimately boost their bottom line. These software solutions offer a range of functionalities, from data cleaning and manipulation to statistical analysis and data visualization tools. Emerging features include artificial intelligence and machine learning for more powerful data exploration and predictive modeling. Data analytics software benefits a wide range of users, from data analysts to business managers, and industries like finance, healthcare, and marketing. However, limitations exist, such as the need for clean data and skilled professionals to operate the software effectively. Overall, data analytics software empowers organizations to leverage the power of data, transforming it into strategic advantages.

What Are The Key Benefits of Data Analytics Software?

  • Clean Data Insights
  • Identify Trends
  • Predict Customer Behavior
  • Improved Decision Making
  • Targeted Marketing
  • Product Development
  • Operational Efficiency
  • Risk Management
  • Fraud Detection
Read more

Overall

Based on the latest available data collected by SelectHub for 62 solutions, we determined the following solutions are the best Data Analytics Software overall:

Hadoop
Hadoop
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Why We Picked Hadoop

Hadoop has been making waves in the Big Data Analytics scene, and for good reason. Users rave about its ability to scale like a champ, handling massive datasets that would make other platforms sweat. Its flexibility is another major plus, allowing it to adapt to different data formats and processing needs without breaking a sweat. And let's not forget about reliability – Hadoop is built to keep on chugging even when things get rough. However, it's not all sunshine and rainbows. Some users find Hadoop's complexity a bit daunting, especially if they're new to the Big Data game. The learning curve can be steep, so be prepared to invest some time and effort to get the most out of it.

So, who's the ideal candidate for Hadoop? Companies dealing with mountains of data, that's who. If you're in industries like finance, healthcare, or retail, where data is king, Hadoop can be your secret weapon. It's perfect for tasks like analyzing customer behavior, detecting fraud, or predicting market trends. Just remember, Hadoop is a powerful tool, but it's not a magic wand. You'll need a skilled team to set it up and manage it effectively. But if you're willing to put in the work, Hadoop can help you unlock the true potential of your data.

Pros & Cons

  • Scalability: Hadoop can store and process massive datasets across clusters of commodity hardware, allowing businesses to scale their data infrastructure as needed without significant upfront investments.
  • Cost-Effectiveness: By leveraging open-source software and affordable hardware, Hadoop provides a cost-effective solution for managing large datasets compared to traditional enterprise data warehouse systems.
  • Flexibility: Hadoop's ability to handle various data formats, including structured, semi-structured, and unstructured data, makes it suitable for diverse data analytics tasks.
  • Resilience: Hadoop's distributed architecture ensures fault tolerance. Data is replicated across multiple nodes, preventing data loss in case of hardware failures.
  • Complexity: Hadoop can be challenging to set up and manage, especially for organizations without a dedicated team of experts. Its ecosystem involves numerous components, each requiring configuration and integration.
  • Security Concerns: Hadoop's native security features are limited, often necessitating additional tools and protocols to ensure data protection and compliance with regulations.
  • Performance Bottlenecks: While Hadoop excels at handling large datasets, it may not be the best choice for real-time or low-latency applications due to its batch-oriented architecture.
  • Cost Considerations: Implementing and maintaining a Hadoop infrastructure can be expensive, particularly for smaller organizations or those with limited IT budgets.

Key Features

  • Distributed Computing: Also known as the Hadoop Distributed File System (HDFS), this feature can easily spread computing tasks across multiple nodes, providing faster processing and data redundancy in the event that there’s a critical failure. Hadoop is the industry standard for big data analytics. 
  • Fault Tolerance: Data is replicated across nodes, so even in the event of one node failing, the data is left intact and retrievable. 
  • Scalability: The app is able to run on less robust hardware or scale up to industrial data processing servers with ease. 
  • Integration With Existing Systems: Because Hadoop is so central to so many big data analytics applications, it integrates easily into a number of commercial platforms like Google Analytics and Oracle Big Data SQL or with other Apache software like YARN and MapR. 
  • In-Memory Processing: Hadoop, in conjunction with Apache Spark, is able to quickly parse and process large quantities of data by storing it in-memory. 
  • Hadoop MapR: MapR is a component of Hadoop that combines a number of features like redundancy, POSIX compliance and more into a single, enterprise grade component that looks like a standard file server. 
Tableau Big Data
Tableau Big Data
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Deployment
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Key Features

  • Big Data Integrations: Connects seamlessly to all kinds of data infrastructures and sizes of data sets, including Big Data. Its ecosystem integrates with Hadoop distributions, NoSQL databases and Spark sources with ease. 
  • One Data Interface: Consolidate and democratize data in one place. Invite all members of an organization to explore data, asking more questions and finding more answers.
  • VizQL: Interact with data in real time with a proprietary technology that allows for limitless exploration that’s more accessible to all skill levels than traditional coding.
  • Data Catalog: Take advantage of data governance with a glossary of data sources and standard data definitions, as well as metadata management and standardization of data procedures. Understand data in context and easily find the right data for the job within vast data sets.

Pricing

License/Subscription Cost
  • Based on the number of users
  • Based on an annual subscription model – per user, per month (billed annually)
  • Subscription costs allow users to access Tableau over a set timeframe for either an on-premise or cloud-based/SaaS model
  • Subscription licensing model requires a one-time license fee
  • Provides enterprise subscriptions/enterprise licensing based on the type of SMB and the enterprising plan selected with both the on-premise and cloud-based/SaaS model
Maintenance Cost
  • Cost is included in the price of subscription license. This feature includes ongoing updates, access to Tableau’s ongoing product upgrades and support services at no additional charge
Installation/Implementation Cost
  • Included in the upfront subscription cost
  • No hidden costs
Customization Cost
  • The custom version cannot be requested. Instead, users can do limited customizations on their own, such as:
  • User can change the server name that appears in the browser tab, tooltips and messages
  • User can change the logos that appear in different server page contexts
  • User can control the language used for the server user interface and the locale used for views
  • Custom fonts can be installed for different clients
  • Administrators and project leaders can also add images for projects in thumbnail view
Data Migration Cost/Change Management/Upfront Switching Cost
  • Data Migration is possible in Tableau Servers and can be done with the following tools: Tabcmd Script, REST API, TabMigrate and Enterprise Deployment Tool by InterWorks
  • There is a limit to how much data can be stored within Tableau Online and the storage cost will increase if that threshold is crossed
Training Cost
  • The price will vary depending on the type of training opted for. Options include live online training, classroom training, eLearning and certification programs.
  • Tableau provides free training videos, whitepapers and product demos for streamlining the implementation process.

Two types of training can be obtained:

  • In-person training takes two days, and classroom sessions are available in various global locations
  • Virtual classes are held four times a month and have a duration of 4 to 5 days
  • The cost of training is $1,400 per person for each type of training
  •  Web Authoring training costs $700 per person

eLearning is a year-long subscription that gives users access to two courses:

  • Desktop I: Fundamentals and Desktop II: Intermediate
  • The cost of Tableau Certification varies depending on the type of course that a user takes
  • Tableau Desktop 10: Qualified Associate ($250/exam fee), Certified Professional ($600/exam fee) and Delta Exam ($125/exam fee)
  • Tableau Server 10: Qualified Associate ($250/exam fee), Certified Professional ($800/exam fee) and Delta Exam ($125/exam fee)
Recurring/Renewal Costs
  • Renewal cost is equivalent to the fees paid annually, based on the number of users
  • Regular support services are built into the subscription price. However, professional services, such as on-site consulting, are add-on expenses
Cloudera
Cloudera
Start Price
$833
Per User, Annually
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked Cloudera

Is Cloudera the answer to your data management woes, or is it just a bunch of hot air?

User reviews from the past year paint a mixed picture of Cloudera. While some users praise its flexibility and ability to handle large datasets, others find it cumbersome and expensive. Cloudera's hybrid cloud approach, allowing users to deploy on-premises or in the cloud, is a major selling point for many. However, some users find the platform's complexity a barrier to entry, especially for those without extensive experience in data management. Cloudera's integration with other tools, such as Apache Hadoop, is a key differentiator, but some users report issues with compatibility and performance.

Cloudera is best suited for large enterprises with complex data needs and a dedicated team of data engineers. Its robust features and scalability make it a powerful tool for organizations that require a comprehensive data management solution. However, smaller businesses or those with limited technical resources may find Cloudera's complexity and cost prohibitive.

Pros & Cons

  • Scalability: Cloudera can handle massive datasets and complex queries, making it suitable for large-scale data analysis and reporting.
  • Security: Cloudera offers robust security features, including data encryption and access control, ensuring sensitive data is protected.
  • Performance: Cloudera's optimized architecture and distributed processing capabilities deliver fast query execution and efficient data processing.
  • Integration: Cloudera integrates seamlessly with various data sources and tools, enabling users to connect and analyze data from different systems.
  • Community Support: Cloudera has a large and active community, providing access to resources, support, and best practices.
  • Steep Learning Curve: New users often find Cloudera's interface and complex architecture challenging to navigate, requiring significant time and effort to master. This can be especially problematic for teams with limited technical expertise.
  • Costly Implementation: Cloudera's pricing model can be expensive, particularly for large deployments. The cost of hardware, software licenses, and ongoing support can be a significant barrier for some organizations.
  • Limited Scalability: While Cloudera offers scalability, some users have reported challenges scaling their deployments to meet rapidly growing data volumes. This can lead to performance bottlenecks and slow query execution times.
  • Complex Management: Managing a Cloudera cluster can be complex, requiring specialized skills and knowledge. This can be a burden for organizations with limited IT resources.

Key Features

  • Data Science Workbench: Through a unified workflow, collaboratively experiment with data, share research between teams and get straight to production without having to recode. Create and deploy custom machine learning models and reproduce them confidently and consistently.
  • Real-Time Streaming Analytics: With edge-to-enterprise governance, Cloudera DataFlow continuously ingests, prioritizes and analyzes data for actionable insights in real-time. Develop workflows to move data from on-premises to the cloud or vice-versa, and monitor edge applications and streaming sources.
  • Machine Learning: Enable enterprise data science in the cloud with self-service access to governed data. Deploys machine learning workspaces with adjustable auto-suspending resource consumption guardrails that can provide end-to-end machine learning tools in one cohesive environment.
  • Data Warehouse: Merges data from unstructured, structured and edge sources. The auto-scaling data warehouse returns queries almost instantly and has an optimized infrastructure that moves workloads across platforms to prepare vast amounts of data for analysis.
  • Operational Database: The operational database promises both high concurrency and low latency, processing large loads of data simultaneously without delay. It can extract real-time insights and enable scalable data-driven applications. 
  • Open-Source Platform: Access the Apache-based source code for the program and make adjustments, customizations and updates as desired. 
  • Data Security and Governance: Reduce risk by setting data security and governance policies. The Cloudera Shared Data Experience (SDX) then automatically enforces these protocols across the entire platform, ensuring sensitive information consistently remains secure without disruption to business processes.
  • Hybrid Deployment: Leverage the deployment flexibility and accessibility to work on data wherever it lives. Read and write directly to cloud or on-premises storage environments. With a hybrid cloud-based architecture, choose between a PaaS offering or opt for more control via IaaS, private cloud, multi-cloud or on-premises deployment.
Domo
Domo
Start Price
$15
Per User, Monthly
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked Domo

Domo has everything data teams could wish for — self-service ETL, advanced analytics and data science. Its rich set of connectors makes users happy as they praise its robust integration APIs. Its scripting language is similar to Power BI, and knowing SQL will shorten your team's learning curve. The vendor offers daily refreshes, currently capping them at 48.

On the flip side, the interface seemed a bit clunky to me. Dashboards don’t display in the edit mode by default, which was a tad annoying. The Getting Started documentation is dated and doesn’t match the new interface. I could find my way around with help from user forums.

While the vendor earns praise for releasing frequent updates, quite a few users say some much-needed features lack depth. According to our research, Domo offers only 64% of the required functionality out of the box, which is much less than what Power BI and Tableau provide. It also underperforms in data querying, scoring only 53 in our analysis.

Some reviews mention bugs and that performance can lag when handling anything complex than simple visualizations. The slowness could be due to the multitenant SaaS model that provides shared computing. As for the mobile app, it didn’t work in the offline mode for me. I should mention here that I had opted for the trial version. A proof-of-concept will help you check if the issue persists in the paid edition.

Domo’s pay-as-you-go model is great for estimating usage but be prepared to pay more for workload spikes. According to our research, about 89% of users who reviewed the price found Domo’s consumption model expensive. Small organizations working with a lean team might find it challenging to handle billing.

Here’s what’s great about subscribing to Domo. You can create as many reports and dashboards as required — there’s no limit or additional cost. Plus, Domo allows adding an unlimited number of users. Domo accepts external data models from OpenAI, Amazon Bedrock, Hugging Face, Databricks and Jupyter Workspaces.

Despite a competitive market, Domo is an excellent product for organizations seeking data visualization and strong integration. Its flexible pricing model and recent AI updates make it a strong challenger to leading data platforms.

Pros & Cons

  • Source Connectivity: About 86% of users citing data integration said they could connect to their preferred sources easily.
  • Ease of Use: Around 82% of users discussing the interface said options and tabs were straightforward and intuitive.
  • Data Visualization: About 74% of people who reviewed Domo for graphics appreciated the ease of creating and sharing data stories.
  • Functionality: Around 73% of users who mentioned features said Domo performed as expected.
  • Support Services: About 71% of reviews discussing assistance praised the support team for being helpful and responsive.
  • Speed: About 78% of users discussing speed said the platform lagged sometimes.
  • Cost: Around 89% of users discussing price termed the platform as expensive.

Key Features

  • Domo App Studio: Design custom apps for needs Domo might not address out of the box. Build your own functionality without coding knowledge. Create branded visualizations with your trademark colors, logos and fonts. ESPN enhances the fan experience by capturing and analyzing customer data using a Domo Studio app.
  • Analyzer: Save time spent cleaning data manually. Use a special editor to set up checks for data inputs. Keep tabs on which charts and dataflows use a specific dataset with the lineage option. You can choose the best chart to present your data and annotate it. Use the Beast Mode for complex math.
  • DomoGPT: Get answers to data queries using AI Chat (currently in beta). Convert text to SQL or calculations and understand your data using text summaries. Use Domo.AI in a safe, governed space.
  • Personalized Data Permissions: Create custom data views for your users and hide sensitive data. Your regional managers get exclusive views specific to their roles, while senior management can switch between full and filtered views.
  • Domo Mobile: View cards and text summaries on the mobile app. Cards fit within the small screen, giving a great user experience. Domo Buzz allows sharing files to WhatsApp, Gmail, QuickShare and Google Drive. You can even save a screenshot to your phone gallery.
  • Alerts: Stay informed about KPIs that matter to you. Set new alerts and share them with selected users or subscribe to existing ones. Choose where you want to receive the notifications — email, mobile app or SMS.
Hortonworks
Hortonworks
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Cloud On-Premise
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Key Features

  • Multi-Workload Processing: The product is able to handle multiple workloads and other taxing processes such as detailed analysis and report generation — all in parallel processes. 
  • Real-Time Processing: Users can take advantage of processing in real time, without having to wait for their data to finish compiling. 
  • Batch Processing: Batch processing is the processing of large quantities of data in large batches, significantly cutting down the time it takes to process information. 
  • Data Governance: Controlling, managing and distributing data are essential to a modern analytics solution. The software provides a suite of management features for users to take advantage of.  
  • Dataflow: Dataflow is an all-in-one data crunching feature that streams data and insights in real-time. It delivers actionable intelligence and curated data as it’s being processed. 
BIRT
BIRT
Start Price
$0
Open-Source
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked BIRT

Reviews for BIRT paint a picture of a user-friendly report designer with a loyal following, particularly among those new to report building. Its drag-and-drop interface and pre-built templates are praised for streamlining report creation compared to coding-heavy solutions. This is a major advantage for businesses that need reports up and running quickly, without tasking developers.

BIRT shines in its ability to connect to various data sources, eliminating the need for complex data extraction steps. This is a big win for teams working with data scattered across spreadsheets, databases, and even flat files. While BIRT offers strong core functionality, some reviewers mention a steeper learning curve for advanced features. For instance, users who need to wrangle massive datasets or create highly customized data visualizations might find BIRT limiting compared to more feature-rich (and often pricier) competitors. Similarly, BIRT's mobile capabilities are seen as less robust than some competing products. This can be a dealbreaker for teams that prioritize mobile reporting for field staff or remote workers who need real-time access to reports.

In conclusion, BIRT is a solid choice for businesses seeking a cost-effective and user-friendly solution for generating basic to moderately complex reports. Its strength lies in its ease of use, data source flexibility, and affordability. However, those working with exceptionally large datasets or requiring a highly customized mobile reporting experience might be better served by exploring feature-rich alternatives.

Pros & Cons

  • Drag-and-Drop Report Design: BIRT boasts a user-friendly interface that lets you visually design reports using drag-and-drop functionality. No coding required! This streamlines report creation for business users who might not be familiar with programming languages.
  • Flexibility and Customization: BIRT empowers you to tailor reports to your specific needs. From basic tables to complex charts and graphs, BIRT offers a wide range of data visualization options. This ensures your reports effectively communicate insights to a variety of audiences.
  • Integration with Various Data Sources: BIRT seamlessly connects to diverse data sources like databases, spreadsheets, and even flat files. This eliminates the hassle of data extraction and manipulation, allowing you to focus on crafting impactful reports.
  • Scheduling and Automation: BIRT allows you to schedule reports to run automatically at specific intervals. This ensures you always have access to fresh data, saving valuable time and keeping everyone on the same page.
  • Steeper Learning Curve for Advanced Features: While BIRT excels in drag-and-drop simplicity for basic reports, users venturing into complex functionalities like data scripting or advanced data manipulation might find the learning curve steeper. This can be a hurdle for teams without in-house BIRT expertise.
  • Potential Performance Issues with Large Datasets: Some users report performance limitations with exceptionally large datasets. BIRT might not be the ideal choice if you consistently work with massive amounts of data that require real-time report generation.
  • Limited Mobile Capabilities: While BIRT reports can be accessed on mobile devices, the user experience might not be optimal for all report formats. This can be a drawback for teams that heavily rely on mobile reporting for on-the-go decision-making.

Key Features

  • Data Explorer: Build connections to data sources and view them together with business assets and data cubes.
    • Access and blend various datatypes from a range of sources, including POJOs, JDO datastores, SQL Databases, JFire scripting objects, XML and web services. 
    • Pull in more data than before by extending the available sources with Eclipse’s Open Data Access framework. 
    • Accesses Hadoop data using Hive Query Language. Ingests data from distributed databases like Cassandra through APIs. 
    • Pulls data from LDAP, report documents and Java objects. In addition to Salesforce, it can ingest information from LinkedIn, Facebook and GitHub. 
  • Report Designer: Supports a wide range of reports, layouts and formatting, with reuse of designs and elements. Create report layouts, connect to data sources and produce XML-based report designs. 
    • Navigator: Create a new Report Design Project or a new BIRT file within a project through the navigator. View all the projects in the workspace and create multiple file types such as a design file, template file, library file or report document. 
  • Sample Report Viewer: View reports in a sample web application before publishing them online. Generate and export them in HTML, PDF, CSV formats. Print locally or on the server and rerun them when needed with new parameters. 
  • BIRT Report Engine: Query data sources and merge the query results into the report layouts created by the Report Designer. Produce the report output in a wide range of formats that include Microsoft Office, HTML, PDF, postscript and open document formats. This feature is also available in the BIRT Web Viewer. 
  • Data Visualizations: Create data visualizations with task-specific editors, builders and wizards and integrate into web systems. 
    • Charts: Choose from a vast library of pie charts, Gantt charts, pyramid charts, scattergrams and many more, with multiple subtypes, such as a bar chart with options of side-by-side, stacked and percent stacked. Create charts in 2D, 2D with depth and 3D formats. 
    • Crosstabs: Present data in two dimensions (sales or hits) with the cross-tabulation or matrix feature. 
    • Palette: Drag and drop elements from the palette into the visualization layout. Add rich text to the report, including HTML formatting integrated with dynamic data. Aggregate business metrics with more than 30 built-in SQL operators. 
  • Customization: Make report data easy to understand with support for internationalization, including bidirectional text. Easily replace static report elements — report labels, table headers and chart titles — with localized text. 
  • Lists: Present data methodically through simple data lists by grouping related data together. 
Zoomdata
Zoomdata
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked Zoomdata

How can Zoomdata help your business intelligence "zoom" to new heights? Zoomdata (now known as Logi Composer) is a business intelligence tool that garnered a devoted following for its ability to handle large, complex datasets, making data visualization and analysis accessible to a wide range of users. Users rave about its intuitive interface and real-time streaming data analysis capabilities, allowing them to "pause, rewind, and replay" data streams, a feature described by one user as a "game-changer" for identifying patterns and anomalies. This sets it apart from traditional BI tools that often lag in real-time data processing.

However, Zoomdata is not without its drawbacks. Some users have pointed out the absence of built-in predictive analytics features, limiting its ability to forecast future trends and perform more in-depth statistical modeling. For instance, one user noted that while Zoomdata excels at presenting "what is happening," it lacks the robust forecasting tools found in competitors like Tableau, making it less suitable for businesses heavily reliant on predictive modeling.

Overall, Zoomdata (Logi Composer) is best suited for organizations seeking a user-friendly BI tool for real-time data visualization and exploration, particularly those dealing with large, streaming datasets. Its intuitive design and powerful data sharpening capabilities make it ideal for users of all technical levels, empowering them to make data-driven decisions quickly and efficiently. However, businesses requiring advanced predictive analytics or statistical modeling features may need to consider alternative solutions or integrations to supplement Zoomdata's capabilities.

Pros & Cons

  • Fast Visual Analysis: Zoomdata is known for its ability to quickly process and visually analyze large datasets, making it ideal for handling big data.
  • Easy-to-Use Interface: Zoomdata offers a user-friendly platform that allows users with varying technical skills to explore data, identify trends, and generate visualizations without relying on data analysts.
  • Seamless Integration: Zoomdata can be easily integrated with other platforms, such as ClickFox, to enhance customer journey analysis by providing interactive dashboards and actionable insights.
  • Lack of Mobile App: Zoomdata lacks a dedicated mobile application, which can be a drawback for users who need to access data and dashboards on the go.
  • No Version Control: The platform lacks version control features, making it difficult to track changes to dashboards and analyses over time, potentially leading to confusion and difficulty in reverting to previous versions.

Key Features

  • Data Connectors: Users have access to a suite of pre-designed connectors that draw data directly from their selected data source. 
  • Drag-and-Drop Attributes: The simple drag-and-drop interface lets users drag, pinch, zoom, swipe and drop to create dashboards. 
  • Data Exploration: The system allows users to create interactive visualizations, customize dashboards and perform self-service analysis to discover data insights. 
  • Data DVR: By unifying historical data analysis with real-time data into a single interface, the dashboards operate like a video, allowing users to pause, rewind, fast forward and replay data streams. 
  • Microservices: Taking advantage of small, coupled programs that work in tandem with one another, Zoomdata is able to scale with a user’s business needs. Microservices are written to be able to be deployed and restart on-the-fly in the event of an outage. 
Alteryx
Alteryx
Start Price
$99
Per User, Monthly
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked Alteryx

Alteryx is a data science solution that leverages the power of AI and ML to blend, parse, transform and visualize big business data to promote self-serve analysis of business metrics.
Many users who reviewed data analysis said that the tool performs statistical, spatial and predictive analysis in the same workflow. Most of the users who reviewed data processing said that, with a lightweight ETL tool, the platform has strong data manipulation and modeling efficiencies, though some users said that it can be tricky to use SQL queries. Citing integration with Power BI, Tableau and Python, most of the users said that the tool connects seamlessly to data from databases and files, apps, and third-party data sources, among others, to expand the reach of search-based and AI-driven analytics. Most of the users who discussed ease of use said that the tool is intuitive with drag-and-drop functionality and a well-designed interface, though some users said error handling can be challenging for automated workflows. Most of the users who reviewed support said that online communities are helpful in providing answers to queries. Citing automated workflows, many users said that the tool helps save time, though some users said that these can be overly complex and need improvement.
On the flip side, many users who reviewed pricing said that its expensive licenses and add-ons are cost-prohibitive, and cost per core is high for enterprises looking to scale. A majority of users who reviewed its visualization capabilities said that they need to export data to visually stronger applications, such as Tableau or Power BI, to make the reports presentation-worthy. Citing slow runtimes when executing complex workflows, especially with large datasets, many users said that performance-wise, the solution is prone to infrequent crashes. Most of the users who discussed learning said that with documentation not being in sync with latest releases, training is a must to optimally use the tool.
Overall, Alteryx is a data science tool that, with its low-code approach and strong data wrangling capabilities, makes the journey from data acquisition to data insights seamless and promotes data literacy across organizations, though it might be better suited for medium- to large-sized organizations.

Pros & Cons

  • Data Analysis: All users who reviewed analytics said that the platform adds value to data through features such as statistical modeling and predictive analysis.
  • Data Processing: Around 86% of the users who mentioned data processing said that, with a lightweight ETL tool, the solution excels at data wrangling for further analysis.
  • Data Integration: Citing strong integration with multiple data sources and tools, around 84% of the users said that it works well with big data.
  • Ease of Use: Approximately 83% of the users who mentioned ease of use said that the platform’s low-code approach, with drag-and-drop functionality, makes the interface user-friendly.
  • Online Community: The online community is responsive and helpful, according to around 74% of users who discussed support for the platform.
  • Functionality: With fuzzy matching and join capabilities, the platform is feature-rich and versatile, said approximately 63% of users who discussed functionality.
  • Cost: In addition to the high cost of licenses, the price of add-ons is limiting, said around 89% of the users who reviewed pricing.
  • Data Visualization: Around 75% of users who reviewed its presentation capabilities said that with outdated graphics, the platform lags behind other solutions in data visualization.
  • Performance: The solution is prone to infrequent crashes, especially when processing large amounts of data, as said by 65% of users who discussed performance.
  • Training: Approximately 54% of the users who reviewed learning said that with the documentation not being up to date with latest features, there is a steep learning curve and training is required.

Key Features

  • Internal Data Visualization: Display data insights at each stage of ETL, enabling validation and verification at every step of analysis through its in-platform data visualization solution, Visualytics. 
  • Data Visualization Export: Export to data visualizers like Qlikview and Tableau in several formats seamlessly, if the platform’s in-house visualization capabilities don’t satisfy the business’s needs. 
Spotfire
Spotfire
Start Price
$0.99
Monthly, Quote-based
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked Spotfire

In online reviews, Spotfire emerges as a user-friendly big data platform. Most users found data exploration easy with a drag-and-drop interface. Some users said the UI was dated, though, and said it could use a revamp. Most users praised its interactive visualizations and dashboards, saying they helped them interpret data better. But, a few said they would love to have more visuals to choose from.

A user mentioned they did the calculations in Excel and imported them into Spotfire for visualization. It's a common scenario when a steep learning curve slows down adoption, and teams fall back on Excel. Most users said Spotfire takes time to learn. You might have to opt for a balance of multiple platforms to balance your departmental and enterprise needs.

Spotfire surpasses Excel in data management, especially data prep. Customizable visualizations and custom Mods give you enough freedom to work within the platform.

Though 72% of reviewers were happy with the integrations, Spotfire lacks some standard connectors, such as for Apache Kafka, forcing users to rely on workarounds.

A majority of users found its pricing structure complex, especially as users increased. In such cases, organizations often tend to opt for a cheaper alternative for less advanced use cases while using the pricier platform for the critical ones. We advise doing a deep dive into the vendor's pricing plans to avoid making your tech stack top-heavy.

Ultimately, Spotfire's appeal lies in its balance. It's visually captivating and user-friendly for casual users while offering enough depth for seasoned analysts. However, its pricing and learning curve might deter organizations on a tight budget.

Pros & Cons

  • Data Visualization: About 86% of reviewers were satisfied with the available options when designing dashboards.
  • Support: Around 74% of users praised vendor support for their timely response and helpful attitude.
  • Integration: Almost 72% of users were satisfied that it integrates with their preferred systems.
  • Friendly Interface: Around 68% of reviewers said the platform was easy to use.
  • Functionality: About 64% of users said it had a rich feature set.
  • Cost: Around 96% of the user reviews said it the price was high and licensing complex.
  • Adoption: 90% of reviewers said there was a significant learning curve and users would need specialized knowledge of data science and statistics.

Key Features

  • Spotfire Actions: Decide what to do with and act instantly — no need to switch to your procurement application to pause new orders. This powerful feature allows you to run scripts within analytics workflows. You can also trigger actions in your external system through visualization. Spotfire can set up over 200 commercial connections and has 1800 community connectors.
  • Mods: Build reusable workflows and visualization components, much like apps in Power BI and Qlik Sense. They allow your users to tailor their analytical processes so they don’t have to start from scratch every time. Based on code, they run in a sandbox with limited access to system resources for security. Users can share them through the Spotfire library. Mods improve efficiency and collaboration.
  • Batch Edits: Make similar changes to multiple files in one go. Write custom scripts to call the Spotfire API that’ll make changes to the files. Update the IronPython version to the latest one or embed the Spotfire JQueryUI library instead of its references.
  • Recurring Jobs: Simplify event scheduling to better manage your time and tasks. Improve efficiency and deliver reports at the same time on the same day of the week or month. The latest Spotfire version allows you to set recurring automation jobs to occur every X hours, days, weeks or months.
  • Web Player REST API: Share insight with clients and partners without them needing to sign up for a paid Spotfire account. Engage them via data visualizations on the web browser, thanks to Spotfire Web Player. Update analyses on the web with real-time data in the latest Spotfire version.
  • Roles: Invest wisely — opt for licenses that align with user roles. Choose Spotfire Analyst for data analysts, scientists and power users who need deep-dive analysis. Get the Business Author license for enterprise users, analysts and power users to create and consume insights without deep expertise. Choose consumer licenses for users who’ll interact with and consume data. They include the C-suite and non-technical users within the organization.
  • Information Designer: Prepare fully governed data sources for business users in a dedicated wizard. Set up their preferred data sources and define in advance how Spotfire will query and import data into storage. Specify which columns to load and which filters, joins and aggregations to apply.
  • Audio and Image Processing: Add user feedback from customer calls and videos. Interpret public sentiment about your product by analyzing social media pictures and videos. Spotfire enables writing code to extract text from audio and image files. You can then import the data into the platform for analysis.
  • IoT Analytics: Gain insight at lightning speed; build microservices and deploy them at the edge. With Spotfire, you can add IoT data to your regular data for the complete picture.
BigQuery
BigQuery
Start Price
$6.25
Per TiB, Usage-Based
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
Request Pricing Request Demo

Why We Picked BigQuery

Bigquery is a scalable big data warehouse solution. It enables users to pull correlated data streams using SQL like queries. Queries are executed fast regardless of the size of the datasets. It manages the dynamic distribution of workloads across computational clusters. The easy-to-navigate UI is robust and allows the user to create and execute machine learning models seamlessly. Users liked that it can connect to a variety of data analytics and visualization tools. However, users complained that query optimization is an additional hassle they have to deal with, as the solution is expensive and poorly constructed queries can quickly accumulate charges. It can be overwhelming for the non-technical user, and SQL coding knowledge is required to leverage its data analysis capabilities. Data visualization features are lacking and in need of improvement.

Pros & Cons

  • Performance: The system can execute queries on massive amounts of data with agility, as specified by about 89% of users who mentioned performance.
  • Functionality: About 68% of users who reviewed functionality talked about its robust inbuilt features.
  • Ease of Use: The UI is simple and easy to navigate, according to about 72% of users who talked about user-friendliness.
  • Integration: Approximately 75% of reviewers who talked about integration said that it connects to numerous other tools seamlessly.
  • Scalability: All users who reviewed scalability said that the platform scales to thousands of servers.
  • Cost: Approximately 76% of users who mentioned cost complained that it’s expensive, and charges can rack up quickly if queries aren’t properly constructed.
  • Learning Curve: About 82% of users mentioned that the software has a steep learning curve.
  • Resources: About 89% of users who spoke about resources said that documentation and video tutorials are lacking and need improvement.
  • Visualization: Data visualization capabilities aren’t up to the mark, according to all users who talked about visualization.

Key Features

  • Machine Learning: Comes with machine learning modules that can perform mass-segmentation and recommendations in seconds. These modules can be built and trained within minutes without ingesting data for training. 
  • Cloud Hosted: Handles all the hardware provisioning, warehousing and hardware management from the cloud. 
  • Real-Time Analytics: Large volumes of business data are quickly analyzed and presented to the user to ensure that insights and data discrepancies can be immediately uncovered. 
  • Automated Backups: Data is automatically stored and backed up multiple times a day. Data histories can be easily restored to prevent loss and major changes. 
  • Big Data Ecosystem Integrations: Integrate with other big data products such as Hadoop, Spark and Beam. Data can be directly written from the system into these products. 
  • Data Governance: Features such as access management, filter views, encryption and more are included in the software. The product is compliant with data regulations such as the GDPR. 

COMPARE THE BEST Data Analytics Software

Select up to 2 Products from the list below to compare

 
Product
Score
i
SelectHub’s Analyst Rating is based on data from our 400+ point analysis of Data Analytics Software, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Start Price
Free Trial
Company Size
Deployment
Platform
Logo
Hadoop
Undisclosed
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Hadoop
Tableau Big Data
Still gathering data
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Tableau Big Data
Cloudera
$833
Per User, Annually
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Cloudera
Domo
$15
Per User, Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Domo
Hortonworks
Still gathering data
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Hortonworks
BIRT
$0
Open-Source
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
BIRT
Zoomdata
Undisclosed
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Zoomdata
Alteryx
$99
Per User, Monthly
Yes
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Cloud On-Premise
Mac Windows Linux Chromebook Android
Alteryx
Spotfire
$0.99
Monthly, Quote-based
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Spotfire
BigQuery
$6.25
Per TiB, Usage-Based
Yes
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Cloud On-Premise
Mac Windows Linux Chromebook Android
BigQuery

All Data Analytics Software (62 found)

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Airflow

by Apache Software Foundation
Airflow
Airflow is an open-source Python framework that allows authoring, scheduling and monitoring of complex data sourcing tasks for big data pipelines. Aligned with the DevOps mantra of “Configuration as Code,” it allows developers to orchestrate workflows and programmatically handle execution dependencies such as job retries and alerting. Through the use of Directed Acyclic Graphs (DAGs), developers can customize pipeline processes as needed by using multi-step workflows. They can run part of the workflow at any time, even when tasks are being updated in real time. Besides out-of-the-box integrations with MySQL, Microsoft Server and SaaS platforms, it also provides custom connections to plugins. Robust and flexible, it is free to download for all users.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 or less
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Vertica

by Micro Focus - HM - LB
Vertica
Vertica is an analytics and data exploration platform designed to ingest massive quantities of data, parse it, and then return business insights as reports and interactive graphics. Elastically scalable, it provides batch as well as streaming analytics with massively parallel processing, ANSI-compliant SQL querying and ACID transactions. Deployable in the cloud, on-premise, on Apache Hadoop and as a hybrid model, its resource manager enables concurrent job runs with reduced CPU and memory usage and data compression for storage optimization. A serverless setup and advanced data trawling techniques help users store and access their data with ease.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 or less
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Infor Birst

by Infor, Inc.
Infor Birst
Infor Birst is a cloud-based analytics software tool that aims to help users discover insights without the need for analyst input. It unifies IT-managed enterprise data with user-owned data, supporting the blending of both in a top-down and bottom-up manner. It uses consistent business metrics to structure raw data into organized sets and visualizations. It helps users identify patterns and better understand their organization’s KPIs. It offers a seamless, integrated UI that allows users to perform every step of the data analysis process in a single interface, enabling a smooth experience. It can be deployed either from the cloud or self-hosted on-premise. Users can purchase it in three available formats: per-user fee, by department or business unit or by end-customer in embedded scenarios.To explore other popular solutions that Infor offers, visit our Infor company page.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
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KNIME

by KNIME
KNIME
KNIME is an open-source end-to-end data analytics solution. It utilizes visual workflows with drag-and-drop functionality and thousands of nodes to lessen the data analytics learning curve data, with more than 1,800 prebuilt default workflows for streamlined setup. It allows for data ingestion, preparing, cleansing, analyzing and visualizing. It can be scaled for deeper analytics through integrations with sophisticated data modeling capabilities. It can be hosted on-premise or in the cloud through Microsoft Azure.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 or less
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QlikView

by QLIK TECHNOLOGIES INC.
QlikView
QlikView is a data discovery and customer insight platform from Qlik, a leader in the insight and intelligence space. However, it is not available for purchase any longer. Qlik Sense, Qlik’s next-generation offering, is available for new customers. It offers self-service data that can help drive decisions and generate significant ROI for technical skill level users. It’s built from the ground up to be affordable, scalable and adaptable. It can ingest data from diverse sources like big data streams, file-based data, and on-premise or cloud data. It is well-known for its data associations and relationship functionality, keeping data in context automatically. It delivers results quickly via its patented in-memory data processing module, processing data down to as little as 10% of its original size. Pros Intuitive interface Fast data visualization Easy data exploration User-friendly for non-technical users Strong community and support Cons Limited data modeling capabilities Licensing costs can be high Customization can be challenging Version control can be a concern Performance can slow with large datasets
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
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IBM Watson Analytics

by IBM, America
IBM Watson Analytics
IBM Watson Analytics is a software platform that leverages artificial intelligence and machine learning to empower businesses with data-driven insights for improved decision-making. It caters to businesses of all sizes and across various industries seeking to enhance their competitive edge. Benefits include optimized decision-making, increased productivity, cost reduction, and improved customer satisfaction. Popular features encompass predictive and prescriptive analytics, natural language processing, and data visualization. Pricing is determined on a per-user basis, taking into account the number of users, features utilized, and the chosen subscription plan. User experiences suggest that IBM Watson Analytics offers a user-friendly interface and robust capabilities, making it a valuable tool for businesses seeking to harness the power of data analytics. However, it's recommended to explore alternative options and consider factors such as pricing, features, and user reviews to determine the best fit for your specific business needs.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
Company Size
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Platform
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Qlik Sense

by QLIK TECHNOLOGIES INC.
Qlik Sense
Qlik Sense is a self-service analytics solution that supports enterprise decisions and strategy. Its value lies in interactive dashboards and reports for exploring data independently. Embedded analytics lets your users work freely with data within their applications and websites.Like QlikView, Qlik Sense has an associative engine. It links data tables in memory, keeping the information ready for when your users build models. Plus, its cognitive engine uses natural language for data tasks.The Enterprise subscription includes Talend Data Integration and Qlik Advanced Analytics in Qlik Cloud. You can also choose Qlik Data Integration. Qlik Sense Enterprise SaaS includes 5000 automations and 100 reports. It might be a good fit if your data loads are predictable and don't change much. The vendor charges for the data you move or the volumes you analyze; you can choose between the two.Qlik Sense deploys on Windows, Mac, Linux and any phone device. By contrast, Tableau runs on Windows, Mac and mobile, while Power BI is available only on Windows and Android devices.Individual users can choose the desktop edition, though they’ll need a Qlik Cloud account to download it. A 30-day trial period is available, after which it'll cost $30 per user monthly.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 - $100
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Qubole

by Qubole
Qubole
Qubole is a cloud-based data lake management solution that enables fast data lake adoption for businesses. It allows continuous collaboration by ingesting and processing continuously generated data. Connect to a variety of structured and unstructured data sources and perform ad hoc and streaming analytics, build and test machine learning models and explore data.Explore, build, orchestrate and deliver data pipelines with ease while minimizing cost and maximizing performance. Users can choose a data format best suited to their workflow. It includes a centralized workspace, development tools, an inbuilt notebook environment and extensive integrations to provide end-to-end service with near-zero maintenance.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
Company Size
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Deployment
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Webgility

by Webgility
Webgility
Webgility is a data automation suite that enables e-commerce companies to manage multi-channel inventory. It helps online sellers focus on simplifying organizational operations, forecast inventory requirements and manage data. It also assists with tax compliance refunds. Modules include accounting, inventory management, order management, shipping and analytics. This allows users to connect expenses and revenue streams, make smarter decisions, lower expenses, and simplify accounting and bookkeeping. It has partnerships with Intuit, QuickBooks, Xero and NetSuite, along with more than 70 SaaS and hosting providers, payment processors and e-commerce vendors, including Amazon, eBay, PayPal and Shopify Payments.
User Sentiment User satisfaction level icon: fair
Cost Breakdown
$10 - $100
Company Size
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UniCourt

by UniCourt
UniCourt
UniCourt is a legal data-as-a-service (LDaaS) solution that provides real-time access to U.S. federal and state court records for analytics, investigations, underwriting and news reporting. Available on the web and in the cloud, it provides accurate and up-to-date information on legal entities that include lawyers, judges, parties and law firms.Private individuals as well as corporate entities can readily access and download court records either in real time through API integration or from its vast database. With case change tracking through automated alerts, it ensures that subscribers get notified when the case status changes.In addition to exporting legal documents, subscribers can collaborate on cases with clients through shared folders. It’s available for personal and enterprise use through monthly and annual subscriptions.
User Sentiment User satisfaction level icon: fair
Cost Breakdown
$10 - $100
Company Size
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Azure Databricks

by Microsoft
Azure Databricks
Azure Databricks is a unified big data analytics platform that provides data management, machine learning and data science to businesses through integration with Apache Spark. Integrating with a host of data sources, it pulls data from a wide variety of sources, transforms and then analyzes it through visualizations. In addition to setting up ETL flows, it empowers enterprises to create data models for predictive analysis, forecasting and future planning. The vendor offers three workloads based on the stages of analytics workflows — Jobs Compute and Jobs Light Compute for data engineers, and the All-Purpose Compute workload for data scientists.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
Company Size
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Ayasdi

by Ayasdi, Inc.
Ayasdi
Owned by the SymphonyAI Group since 2019, Ayasdi is a machine intelligence platform that leverages statistical calculations and mathematical algorithms to deliver analytics to enterprises. It helps businesses connect the dots by segmenting complex, related datasets into groups through topological data analysis (TDA). Organizations can detect financial risk areas and fraud through automated machine learning workflows, and uncover previously undiscovered patterns and risks.Its sequential data processing and augmented analytics inclusions enable healthcare providers to create consistent patient care strategies. Running on Linux and Hadoop, it can be deployed on premises or in the cloud as a Software-as-a-Service (SaaS).
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
Company Size
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SAS Viya

by SAS Institute
SAS Viya
SAS Viya is a cloud-based in-memory analytics engine that provides data visualization, reporting and analytics to businesses for actionable data insights. Powered by AI, it brings together visual analytics, visual statistics and data science for enterprises to achieve end-to-end self-service analytics. It uses a standardized code base with support for programming in R, Python, SAS, Java and Lua. Deployable in the cloud, on-premises and hybrid environments, it integrates with a wide range of business applications through an agile, scalable architecture. The vendor offers an introductory 30-day free trial. Pros Comprehensive features Powerful analytics capabilities User-friendly interface Scalable architecture Strong support from SAS Cons Steep learning curve Limited customization optionsHigh cost of ownership Potential vendor lock-in Resource-intensive
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
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Actian

by Actian Corporation
Actian
Actian is a cloud data management platform that enables data integration with fully managed warehousing, transformation and analytics for enterprises. It integrates with various cloud-based and on-premise technologies and services. With massively parallel processing and data compression on the back end, its embedded analytical engine works in tandem with a robust RDBMS, complementing its computing with built-in user-defined functions to process online transactions at scale.Part of the Cloud Security Alliance, the vendor consistently updates their best practices to ensure the security of its cloud services. It offers a 30-day free trial. Pricing is available on request.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
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1010data

by 1010data, Inc.
1010data
1010data is a market intelligence and enterprise analytics solution that helps track consumer insights and market trends. In addition to vendor-critical insights, it provides brand performance metrics to buy-side entities. Seamlessly embeddable, it can also function as a standalone private-label option. Data scientists and statisticians leverage its integration with R to view and query data tables.It enables analytics development through its QuickApps framework. By tracking consumer spending trends and brand performance, it enables businesses to better position their products in the marketplace.
User Sentiment User satisfaction level icon: good
Cost Breakdown
$1,000 or more
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Essbase

by Oracle America
Essbase
Essbase is a cloud-based enterprise performance management (EPM) solution designed for organizations of all sizes. It helps businesses automate and streamline financial consolidation, budgeting, planning, and forecasting processes. Key benefits include improved data accuracy and consistency, faster time to close, and better decision-making. Popular features include multi-dimensional analysis, workflow management, and integration with other enterprise applications. User reviews highlight its scalability, ease of use, and robust reporting capabilities. Pricing starts at a monthly subscription fee per user, with additional fees depending on features and storage requirements. Pros Scalable Easy to use Robust reporting Fast consolidation Improved data accuracy Cons Limited customization Steep learning curve for advanced features High cost Limited integration with other applications Not ideal for small businesses
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 - $100
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Azure Big Data

by Microsoft Corporation
Azure Big Data
Azure Big Data from Microsoft provides robust analytics and AI services with cloud computing. Build, test, deploy and manage services and applications via public and private cloud data centers managed by Microsoft. It runs both on-premise and through the cloud, using powerful modules and frameworks to create scalable business options. It provides a range of services which include IaaS, PaaS and SaaS.
Cost Breakdown
$100 - $500
Company Size
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Pentaho Data Integration

by Pentaho Corporation
Pentaho Data Integration
Pentaho Data Integration helps organizations access, prepare and analyze all of their data from one place. It connects quickly and easily to any data source, whether it's structured or unstructured. It uses an extract, transform and load (ETL) approach to data management.Organizations can govern their data, ensure data quality and accelerate time to insight. Available on-premise and in the cloud, it’s easy to use and offers a wide range of features.
User Sentiment User satisfaction level icon: good
Cost Breakdown
$500 - $1,000
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IBM Analytics

by IBM
IBM Analytics
IBM Analytics refers to a collection of analysis tools offered by IBM that includes IBM Planning Analytics and IBM Cognos Analytics. It leverages AI to implement business data and unearth insights that help businesses make better decisions.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
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Apache Arrow

by Apache Software Foundation
Apache Arrow
Apache Arrow is an open-source software development framework for building high-performing data analytics applications that process large, complex data sets. With an in-memory columnar format for data storage, it provides a query execution language that helps define the framework for analytics libraries. Language-agnostic due to easy translation, it acts as an interface between a wide range of programming languages and technologies.With a modern CPU architecture based on the single instruction, multiple data (SIMD) taxonomy, it processes data at lightning speeds. Its zero-copy design makes moving data round faster and more efficient.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
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Buyer's Guide

Data Analysis Software Is All About Informed Decision-Making

By Ritinder Kaur, Market Analyst at SelectHub

Data Analytics Software BG Intro

Is your business intelligence system fast enough to analyze information as it arrives? Anything less and you risk being stuck with dated information. Picture this: it feeds your business-critical analytics processes, and the reports don’t add up. What can you do?

If you’re struggling with data management and analytics, this buyer’s guide is for you. It includes a lowdown of what data analysis software means for your business, with handy tips and tricks to succeed at platform selection.

Executive Summary

  • Data analysis software solutions are decision-support systems that derive helpful insight from organizational information.
  • Supply chain, human resources, customer services, marketing automation and accounting are key areas benefiting from data analytics platforms.
  • Data management, visualization and reporting are primary capabilities to consider when purchasing analytics tools.
  • Sophisticated calculations, natural language processing, machine learning and mobile BI are nice-to-have features.
  • User autonomy, data fabric, hybrid cloud, prescriptive analytics and automation are significant system analytics software trends.
  • Prepare a questionnaire to ask vendors about the software and their services.
What This Guide Covers:

What Is Data Analysis Software?

Data analysis software solutions are applications providing decision support to businesses by analyzing their data. Academicians and students use them for economics, social sciences and environmental studies research.

Modern BI systems analyze many data types at speed — it’s a competitive market with reduced time to insight. These tools are undoubtedly more capable and better endowed than traditional spreadsheets and tabular reports.

Big data management, interactivity and self-service BI are ubiquitous data software features. CEOs know that to stay competitive, they must allow data access to more people in the organization.

But, allowing access entails tighter data security and governance.

Which system should you buy? What are your immediate and long-term needs?

There may be issues with your end-of-month processes. Or you’re using a mishmash of tools for tasks that shouldn’t take this much heavy lifting.

Related Categories

And opting for piecemeal functionality might not be an option — it works well until it doesn’t. Scalability issues can put paid to your plans to patch on the desired functionality at a lower cost.

Upgrading to a new platform is a more viable solution, though choosing a tool that plugs existing gaps without breaking the bank can be nerve-wracking. Read on for handy tips.

Primary Benefits

Your organization holds oodles of data with the potential to drive success and growth.

Data Analytics Software Benefits

Manage Production

Every Cheetos piece looks the same — PepsiCo makes sure of it. The food and beverage giant partnered with Microsoft to train AI models that monitor every Cheetos piece for perfect shape, size, curl and crunch, giving you value for your money.

Besides quality assurance, data drives production scheduling, resource allocation and goals tracking. Rich visualization libraries, ready-to-go dashboard templates and robust source connectivity assist in analysis and decision-making.

Streamline Supply Chain

The gut feeling doesn’t work when determining how much inventory to maintain, as a major manufacturer found. Unable to aggregate data from siloed POS (point-of-sale) systems, they were flying blind, being in the dark about how channels drove sales.

Shifting to a POS system that fed data to analytics helped them achieve record sales. But POS isn’t the only data source for inventory management.

Tracking demand and supply requires pulling data from eCommerce and sales systems. Supply chain management systems enable demand forecasting, supplier performance analysis, cost reduction and risk management.

Conduct FP&A

ERP financial management systems give accurate data on demand for financial planning and analysis (FP&A). They generate business-critical financial reports for budget and capital allocation.

Predicting revenue, cash flow and expenses sets you at an advantage — you can harness opportunities and avoid risks. It tells you when to diversify or hire more people and when to take calculated risks.

Watch this case study about how Honda uses BOARD to reduce the financial model building and budgeting cycle time.

Improve Onboarding

Hiring and nurturing talent is effort-intensive, and onboarding and training gaps can cost you when employees leave, and you need to restart the hiring process.

Managers struggle with maintaining high morale due to a lack of visibility into employee trends.

HR management software supports process improvement with hiring and employee performance data. Project management systems consume this data for resource allocation. Employee productivity metrics assist in realistic goal setting.

Boost UX

How full is your inbox with personalized emails asking you to watch this and buy that? They keep coming until one day, you open an email and click on a product that catches your eye. And just like that, you’ve entered the buying funnel.

The customer is king, and your products are only as good as what buyers say. Customer analytics drives companies to success and decides marketing budgets and campaigns. And it leads to innovation — you learn what, when and where to sell.

Calculate Risk

Risk management weighs almost as much on every business owner’s mind as earnings. Data analytics helps identify metrics and events that block your company from achieving its financial, operational and compliance goals.

With predictive analytics, you can forecast the likelihood of an adverse occurrence and the exact impact on your operations. With machine learning models, you can analyze the available data to decide the best way forward.

Implementation Goals

Clarity on stakeholder expectations at the onset helps you bat for your organization when approaching vendors.

Goal 1

Stay Competitive
  • You want consumer analytics.
  • It would be great to know when to diversify.
  • You desire the primary market share in your industry.
  • You want a data-backed roadmap.

Goal 2

Improve Performance
  • You want to track KPIs (key performance indicators).
  • You hope to plug gaps and remove inefficiencies.
  • You wish to boost morale by highlighting successes.

Goal 3

Boost Productivity
  • You want to hire best-fit employees.
  • Your staff should be able to work independently with data.
  • You want to optimize resource allocation.
  • Tracking employee performance will help realize returns on your investment.

Goal 4

Manage Big Data
  • You want real-time data from all preferred sources.
  • The system should be performant when analyzing large data volumes.

Goal 5

Streamline Operations
  • You want clear visibility into internal processes.
  • Ad hoc reporting should be possible on demand.
  • The system should generate and share focused reports with clients and internal teams.

Basic Features

Start your requirements checklist with these features.

Data Management

Built-in data preprocessing, cleansing, profiling and enrichment free you from manual data prep. Metadata indexing establishes data lineage and helps retrieve data.

Data governance is a must to maintain data integrity. Python-R libraries and statistical visualization techniques enable deep-dive analysis.

Data modeling helps identify trends by establishing source-destination mappings and dataset correlations.
Data Querying

Complex queries allow open-ended exploration of relational, OLAP, CSV, XML and website data.

In-memory analysis, parallel processing and scheduled querying accelerate insights.

Batch updates and incremental refreshes keep dashboard data up-to-date. Live connectivity is excellent if it works with your databases and query systems.

But it can be draining on sources, so some databases don’t open themselves up for a live connection. It’s advisable to ask vendors which databases respond to live queries.
Dashboarding and Data Visualization

Dashboards are single-screen KPI views open to exploration and customization. Can you make them your own by adding brand logos, styles and colors?

Check with the vendor if you can publish them on the web and allow external user access via shareable links. Which advanced features are available in visualizations?

Are data refreshes available on demand or per schedule? There may be limitations on the daily refresh frequency.
Enterprise Reporting

Reports are the best representation of analytics results and should be easy to create. Your data analytics tools should allow downloading and sharing with others as links or PDFs in chats or emails.

Advanced analytics software will let you collaborate live with your teams and clients within your reports. You should be able to conduct meaningful discussions with others by leaving comments, asking questions and tagging them within the reports.

Advanced Features & Functionality

Identify the must-have and nice-to-have features during the requirements phase. Round off your checklist with these high-end capabilities.

Advanced Analytics

Complex calculations, dataset clustering and time series analysis help you do more with data. Model import-export enriches insights beyond what your data platform can do.

Regression and what-if analysis enable anticipating trends with a fair degree of certainty.

Sentiment analysis culls valuable customer insights from feedback and survey forms, support chats and social media platforms.
Augmented Analytics

Machine learning accelerates insights by automatically selecting features for cluster analysis and algorithms to apply to the desired data.

Automation drives reusable workflows — record and run processes with one click or per schedule. Automated data prep takes the grunt work out of getting data analysis ready.

Voice searches can vary from current queries to questions about future trends.
Embedded Analytics

Interactive dashboards, professional reports and in-depth analysis within your app avoid logging in separately to a data solution.

An embedded data app quietly feeds your system, staying in the background behind your brand name.

As a vendor, a multitenant solution allows you to optimally allocate storage and computing resources. Some analytics dashboards allow triggering actions from visualizations.
Geospatial Visualization and Analysis

Mapbox, Google Maps and Bing Maps integrations allow including location data in analysis.

Additionally, web-based map servers assist in analyzing spatial file formats, provided your tool connects to them.

Geographic map searches with forward and reverse geocoding remove the need to type in addresses and coordinates.
Advanced Reporting

Login-free dashboard access allows clients to view results remotely. Extensive BI, reporting, analytics, CRM and ERP software integrations enable comprehensive insights.

Plain text searches allow data querying without SQL skills.

Current & Upcoming Trends

Big data integration drives development trends from user autonomy to cloud software, the data fabric and processing information at the edge. Machine learning and automation make business intelligence accessible and future-ready.

Data Analytics Software Trends

Composable Apps

With big data, analytics moved from an IT task to a core business function.

Another change was the shift from monolithic systems to composable apps. No-code software development kits allowed businesses to design modular task-centric apps they could patch onto their tech stack.

The Bayer team centralized financial insights by building a custom app with Power BI. A single-screen KPI view incorporated up to 13 critical metrics, keeping the display clutter-free with report linking.

One-click root cause analyses helped the finance team get answers without asking for help.

Flexible and scalable, composable architecture is a popular software development trend.

Advanced Data Management

Your software deployment is only as good as the data behind it. The data fabric, hybrid cloud deployments and edge analytics owe their origin to the demand for secure, real-time insight without switching apps.

Gartner recognized the data fabric as a significant data and analytics trend in 2021.

It’s a secure data-sharing network of sources and consumer applications on-premises and in the cloud.

  • A smooth transition was at the top of the mind of Lenovo’s team when scaling to a hybrid cloud architecture. Could they make the shift without impacting performance? The hardware giant developed LUCI Sky, short for Lenovo Unified Customer Intelligence, in partnership with Talend Data Fabric and AWS (Amazon Web Services). The company registered 10% ROI, with LUCI Sky running over 4,000 processes using 800 compute cores.
  • Academy Bank migrated to a hybrid cloud solution without deploying existing integrations from scratch, thanks to the Actian Data Platform. Now, customers can make payments and interact with banking services online.

Big data management technologies will continue to make waves going ahead.

Prescriptive Analytics

Machine learning algorithms can cure analysis paralysis with intelligent recommendations for future action. Techniques include finding the best-fit solution, optimization, and simulation that involves replicating scenarios to study outcomes.

Its applications include guided marketing, selling and pricing. While Netflix matches you with viewing content using a percentage-based score, eCommerce websites suggest products based on browsing and buying behavior.

Internal prescriptive insights help companies design strategy, make line-of-business decisions and position their products better. Thanks to AI and machine learning, it’s a new trend with the potential for exciting innovations.

Automation

Insight to action to new insights is a fast closed loop with many moving parts. Workflows must run like well-oiled parts of a machine, or it all falls apart. Automation is the magic mantra that makes it happen.

From reusable workflows to predictive maintenance, automation is a life-saver in business-critical scenarios. Automated workflows gather information from remote locations and send the results back to them.

Schneider Electric reduced maintenance costs and downtime by creating a predictive IoT solution using Azure Machine Learning Service.

At $20,000 per day, downtime in field maintenance doesn’t come cheap. With automated performance monitoring, Schneider’s team reduced the risks and costs associated with sending technicians to the field.

For an in-depth look at where the industry is headed, check out our Business Intelligence Trends 2023 article.

Software Comparison Strategy

If software selection seems overwhelming, a systematic approach makes it manageable.

  • Start by establishing your needs — project managers, department leaders, budget owners and power users can add value to your requirements checklist. Who are the users?
  • Visit our Jumpstart platform to compare your shortlisted products by feature, scoring them on a scale from zero to 100.
  • Conduct in-depth online research to compare shortlisted products for scalability and compatibility with existing systems. Ease of use and accessibility go a long way in encouraging platform adoption.
  • Full-feature support plans include round-the-clock assistance on business days and, in some cases, over the weekend. A dedicated assistance manager and anytime support with instant response times is worth the cost.
  • Verify if data security and regulatory compliance are available to avoid legal and operational challenges later.
  • Reach out to the topmost vendor on your list for proofs-of-concept and demos.
  • Adjust vendor ranking based on how well the products performed during demos and contact the top vendors for further discussions.

Read our lean selection methodology article to learn how to buy a suitable solution.

Cost & Pricing Considerations

Calculate the total cost of ownership (TCO) by including training and maintenance expenses in licensing fees. Small and mid-sized companies can expect to spend about $10,000 to $25,000 annually on data analytics.

Licensing Costs

Subscription models are billed monthly or annually and may include support, maintenance and updates. A Tableau Desktop subscription costs $70/month per user, while a ThoughtSpot subscription starts at $95/month.

Consumption-Based vs. Capacity-Based Pricing

Usage-based payment models calculate the consumed storage and the number of API calls or transactions, so understanding consumption slabs and charges is essential for bill tracking.

Capacity-based pricing is like a prepaid charge — there are no surprises.

A concurrent user license will allow access to a certain number of simultaneous users, while a named user license is for exclusive use by individuals in the organization.

Open-Source and Free Software

Not all open-source software is free, but integration, customization and support costs remain. Some open-source software may incur charges when bundled with other software.

  • Apache Spark has no license fees, but it’s chargeable when bundled with Cloudera, AWS and Azure.
  • KNIME desktop is free to use, but the server is chargeable annually with user and core-based licensing. The personal plan is free, while the Team plan costs 250 euros per month.

Scalability Costs

At the onset, factor in the price of hardware upgrades, additional software licenses, extra storage and network expenses into your TCO as your business expands. 

Support and Maintenance Costs

Don’t try to cut corners here. Go for round-the-clock support and faster response times if it gives you peace of mind and helps you deliver on your promises — it’s worth it.

Discounts

Some vendors sweeten the deal by offering long-term subscriptions at lower prices or free credits. Looker by Google Cloud comes with $300 worth of free credits for new users.

Additional Considerations

Watch out for hidden costs like additional hardware or paid plugins, and don’t underestimate the usefulness of free trials.

The Most Popular Data Analytics Software

Now that you know what data analytics software can do, how do you decide which product is best? Our analysts curated some of the top data analytics systems on the market. Check out our picks below!

Power BI

It’s a data analytics system with live source connectivity for ad hoc querying and reporting. Over 350 transforms are possible with the Power BI Query Editor. AutoML enables regression and predictive modeling.

Power BI relies on Microsoft SSAS for OLAP analysis. Alert setup is possible for sending reports to predefined users when the data changes. The vendor offers Power BI Embedded with Q&A and multitenancy support.

Besides mobile and location data, IoT insights are available with Azure support. You can analyze selected data points using the Quick Insights module.

Power BI

Power BI suggests visualizations for your data queries. Source

Product Overview
User Sentiment Score 88%
Analyst Rating 90
Company Size S | M | L
Pricing Information Power BI Pro is available at $9.99 per month. Refer to our Power BI Pro vs. Premium article for details.
Free Trial Info Power BI Desktop and Service are perpetually free.
Pros and Cons From User Reviews
Pros Cons
All the users reviewing self-service BI praised the platform for in-depth data exploration. About 86% of the users mentioning adoption said there was a learning curve.
Over 98% of the users citing visualization appreciated the platform’s WYSIWYG interface and customization options.  

Oracle Analytics Cloud (OAC)

With corporate dashboards and pixel-perfect reporting, OAC provides a comprehensive business view. Semantic models drive self-service insights, simplifying complex data for technical and non-technical users.

Augmented capabilities include natural language queries, machine-driven data enrichment, one-click explanations and content personalization. You can secure sensitive data with role-based access permissions.

Oracle Analytics Cloud (OAC)

Modern solutions accelerate analytics with one-click data connections.

Product Overview
User Sentiment Score 84%
Analyst Rating 87
Company Size S | M | L
Pricing Information OAC Professional costs $16 per user monthly. Source
Free Trial Info A 30-day free trial is available with a $300 cloud credit. Source
Pros and Cons From User Reviews
Pros Cons
All users discussing augmented analytics highlighted Oracle’s machine learning and automation advantages. Around 74% of the users citing pricing found the platform expensive.
All users reviewing self-service analytics appreciated Oracle’s drill-down and filtering features.  

Qlik Sense

This data analytics software solution has AI-driven data management, visualization and analysis capabilities. Artificial intelligence drives automatic data ingestion, profiling and enrichment.

Charts, graphs and animations enable visual analysis, and master lists support multiple reports. Get fast, accurate results from the Insight Advisor, and control everything centrally using the Qlik Sense Hub.

Establish live source connectivity and upload data in incremental uploads using its DirectDiscovery module. Enhance forecasting by importing predictive models from other platforms and utilizing R plugins. Qlik Sense supports custom app building.

Qlik Sense

Robust code and machine learning algorithms drive modern data analytics.

Product Overview
User Sentiment Score 85%
Analyst Rating 85
Company Size S | M | L
Pricing Information $30 per month per user
Free Trial Info The vendor offers a 30-day free trial.
Pros and Cons From User Reviews
Pros Cons
Around 86% of the users reviewing user-friendliness said the platform is intuitive. Around 86% of the users reviewing customization said they needed more personalization options.
Approximately 81% of the users mentioning deep insights praised its embedded analytics capabilities. About 86% of the users citing cost found it price-heavy with limited licensing options.

Spotfire

TIBCO’s system analytics software supports embedded analytics using a robust JavaScript framework, R packages and open-source libraries. AI enhances data preparation and visual reporting with intelligent recommendations and NLQ.

You can build apps, and advanced features include pattern recognition and anomaly detection. Location data analysis is available, and automated reporting is available. Collaboration is possible by sharing data connections and models via its library.

Spotfire

Location insights add value to analysis with click-and-zoom maps.

Product Overview
User Sentiment Score 84%
Analyst Rating 85
Company Size S | M | L
Pricing Information Available on request.
Free Trial Info A 30-day free trial is available.
Pros and Cons From User Reviews
Pros Cons
Over 91% of the users citing reporting praised the platform for automated insights. All of the users discussing the interface said it could be more intuitive.
Around 78% of users reviewing visualizations appreciated its interactive features for deep-dive analysis.  

Domo

Domo is a cloud solution for advanced analytics with segmentation, cohort analysis, clustering and what-if scenario capabilities. The vendor provides prebuilt apps for sales, marketing and data science.

Domo cards are individual dashboard tiles, and the Beast Mode enables complex calculations. The platform has an active user community, Dojo. A Windows-based workbench allows data integration from on-premise systems to the cloud.

Domo

View performance and forecast sales for the next period on a single dashboard.

Product Overview
User Sentiment Score 87%
Analyst Rating 83
Company Size S | M | L
Pricing Information Available on request.
Free Trial Info A 30-day free trial is available.
Pros and Cons From User Reviews
Pros Cons
All users reviewing functionality praised the platform for KPI monitoring.
 About 95% of the users mentioning pricing found the platform cost-prohibitive.
Over 93% of the users citing visualization appreciated the platform’s data representation capabilities.  

Questions To Ask Yourself

To better understand your company’s requirements, ask these questions internally.

  • What challenges do we face with our current system?
  • Which are the must-have features?
  • Who will use the software?
  • Do we have the technical resources to deploy and maintain the new system?
  • Which system integrations are essential?

Data Analytics Key Questions To Ask

 

 

Questions to Ask Vendors

Ask these questions to learn about the software and vendor.

About the software

  • Is the software customizable?
  • What are its core strengths?
  • Is it regulation-compliant?

About the vendor

  • How many years of expertise do you have in the industry?
  • What onboarding support and training do you offer?
  • How does your support team handle help requests?

Next Steps

Data analytics software offers business-critical decision support beyond reporting to enable open-ended data exploration. Intuitive technologies like machine learning and natural language querying do the rest.

Don’t settle for anything less than what’s best for you — take the next step. Get our requirements template to list your business needs and start your software search systematically.

Product Comparisons

Additional Resources

About The Contributors

The following expert team members are responsible for creating, reviewing, and fact checking the accuracy of this content.

Ritinder
By Ritinder Kaur
Technical Content Writer
Ritinder Kaur is a Senior Technical Content Writer at SelectHub and has eight years of experience writing about B2B software and quality assurance. She has a Masters degree in English language and literature and writes about Business Intelligence and Data Science. Her articles on software testing have been published on Stickyminds.
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Sagardeep
Technical Research By Sagardeep Roy
Senior Analyst
Sagardeep is a Senior Research Analyst at SelectHub, specializing in diverse technical categories. His expertise spans Business Intelligence, Analytics, Big Data, ETL, Cybersecurity, artificial intelligence and machine learning, with additional proficiency in EHR and Medical Billing. Holding a Master of Technology in Data Science from Amity University, Noida, and a Bachelor of Technology in Computer Science from West Bengal University of Technology, his experience across technology, healthcare, and market research extends back to 2016. As a certified Data Science and Business Analytics professional, he approaches complex projects with a results-oriented mindset, prioritizing individual excellence and collaborative success.
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Manan
Technical Review By Manan Roy
Principal Analyst
Manan is a native of Tezpur, Assam (India), who currently lives in Kolkata, West Bengal (India). At SelectHub, he works on categories like CRM, HR, PPM, BI, and EHR. He has a Bachelor of Technology in CSE from The Gandhi Institute of Engineering and Technology, a Master of Technology from The Institute of Engineering and Management IT, and an MBA in Finance from St. Xavier's College. He's published two research papers, one in a conference and the other in a journal, during his Master of Technology.
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Hunter
Edited By Hunter Lowe
Content Editor
Hunter Lowe is a Content Editor, Writer and Market Analyst at SelectHub. His team covers categories that range from ERP and business intelligence to transportation and supply chain management. Hunter is an avid reader and Dungeons and Dragons addict who studied English and Creative Writing through college. In his free time, you'll likely find him devising new dungeons for his players to explore, checking out the latest video games, writing his next horror story or running around with his daughter.
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