6 of the best data analytics tools to use today

Data analytics tools are essential in today’s data-driven world, playing a crucial role in every company. Selecting the right tool is critical, as it directly influences the ability to make informed decisions, optimize processes, and achieve strategic goals. The ideal data analytics tool varies based on specific needs, whether in industries like hospitality or healthcare, or departments like HR. Today, data analytics tools are integral across various solutions to drive business success.

What is a data analytics tool?

A data analytics tool or platform is software that helps organizations, businesses, or individuals collect, process, and analyze data to extract insights and inform decisions. These tools offer functionalities like data exploration, statistical analysis, machine learning, and visualization, helping to understand patterns and trends.

They enable data-driven decisions, optimize operations, improve customer experiences, and enhance business performance. As data grows in complexity, these tools are increasingly essential for turning raw data into actionable insights.

Data analytics and reporting

Companies often seek to pick the right tool for visualizing their data, but relying solely on simple reporting tools might not be enough. That’s why integrating robust data analytics with reporting is crucial.

Reporting is about visualizing data, checking metrics, and answering business questions (such as current revenue, last year's ARR, etc.). In contrast, a data analytics platform goes further, handling data integration, processing, and cleaning, offering various types of analytics (like descriptive, prescriptive, or predictive analytics), and supporting visualization, collaboration, and sharing across teams, departments, business units, and external users.

What should you consider when choosing the right data analytics tool?

Before evaluating options, define your goals, identify solutions for various business use cases, and set success metrics.

• User flexibility: Choose a tool that accommodates all user skill levels, offering all-code customization for engineers, low-code options for analysts, and no-code solutions for end-users with AI-driven interactions.
• Customization and embedding: Opt for a platform that supports rich customization and embedding, from simple iframe integrations to fully customizable interfaces using advanced developer tools.
• Self-service visualization: Ensure the tool allows business teams to create and modify visualizations independently, without relying on data analysts, fostering autonomy in decision-making.
• Single source of truth: Look for a tool that offers a single source of truth through a semantic layer and shared metric store, providing consistent and reliable data insights across the organization.
• AI-enhanced features: Select a platform that integrates AI for advanced capabilities like data discovery, forecasting, and clustering, leveraging augmented analytics to enhance user interactions with the data.
• Deployment flexibility: The tool should offer flexible deployment options, including cloud, on-premises, or hybrid setups, to fit various organizational needs (scalability, security, etc.).
• Scalability: Choose a scalable tool that can grow with your business, supporting more users and applications without compromising performance.
• Pricing: Ensure the pricing model is flexible and scalable, avoiding unexpected costs and accommodating changes in user numbers as your business evolves.

Examples of the best analytics tools

With so many data analytics tools out there, it can be tricky to choose the right one for your business. Each tool has its own strengths, like data visualization, governance, reporting, and preparation. As AI continues to shape the industry, it's essential to pick a tool that can keep up with these fast-moving trends. Gartner’s Magic Quadrant is a helpful guide, showing you the top 20 tools and their areas of focus.

The right choice depends on your specific needs — what data you use, who your end users are, and how you plan to share insights.

For a clear comparison of top BI tools, check out Comparing the Best BI Tools: Select the Right Solution for Your Business. If embedded analytics is what you’re after, check out The Best Embedded Analytics Tools: A Detailed Comparison Guide.

To simplify things, we’ve compiled a list of data analytics tools, highlighting the types of businesses and use cases they’re best suited for.

1. GoodData: Best for versatile analytics solutions

GoodData is a versatile analytics platform designed to support a wide array of use cases, including Analytics as Code (AaC), embedded analytics, and data products. The platform enables organizations to build customized data solutions that can be embedded into applications, monetized, or utilized internally to enhance decision-making. With its flexible architecture, GoodData enables developers to create tailored analytics experiences without being limited by traditional BI constraints.

GoodData has earned high praise on review sites like G2 and TrustRadius for its adaptability and seamless integration capabilities. Users appreciate its flexibility, especially in deploying analytics as code and embedded analytics, making it a top choice for companies aiming to scale their data products.

Customers such as Stackless, Zartico, and Fourth leverage GoodData to enhance their data strategies, streamline analytics, and deliver insights that drive business growth.

Core features:

• Supports all abilities: No-code, low-code, and all-code setups for all users.

• Analytics as Code: Code-driven workflows for managing and deploying analytics.

• Monetization: Create and monetize tailored analytics products.

• Headless BI: Scalable, customizable analytics without front-end constraints.

• Embedding: Integrate insights into apps for enhanced engagement and revenue.

• Developer-friendly: Suite of tools for custom solutions incl. API and SDK support.

• Scalability: Scalable governance and security for large data operations.

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2. Power BI: Best for business reporting

Power BI (or even MS Excel) is widely used in business analytics, providing comprehensive data visualization and reporting capabilities. Power BI integrates seamlessly with Excel, allowing users to import data, create visualizations, and develop dashboards that provide real-time insights. With a user-friendly drag-and-drop interface, Power BI enables both technical and non-technical users to explore data, making it a popular choice for organizations of all sizes.

Power BI’s strong integration with Microsoft products makes it a go-to for companies already embedded in the Microsoft ecosystem. Users can leverage Excel's familiar features like pivot tables for data exploration and Power BI for more advanced, interactive dashboards.

Core features:

• Advanced data visualization and real-time dashboards.
• Seamless integration with Microsoft Excel and other Microsoft tools.
• AI-driven insights and natural language queries.
• Data modeling and transformation capabilities.

Check out our Power BI Alternative paper for an in-depth comparison.

3. Tableau: Best for interactive visualizations

Tableau is renowned for its sophisticated and user-friendly visualizations, allowing users to create interactive dashboards with ease. With Tableau, analysts can connect to multiple data sources, explore data without extensive coding, and build customized visualizations that bring data stories to life. Tableau’s drag-and-drop interface and rich library of visualizations make it ideal for technical users who need to uncover complex insights quickly.

Despite its steep learning curve for non-technical users, Tableau’s advanced analytics capabilities have made it popular among data teams, particularly for creating pixel-perfect visualizations and performing statistical analyses.

Core features:

• Interactive, customizable dashboards and visualizations.
• Extensive data connectivity, including cloud and on-premises sources.
• Advanced analytics, including statistical and predictive modeling.

Check out our Tableau Alternative paper for an in-depth comparison.

4. Sigma: Best for spreadsheet-based analytics

Sigma offers a unique twist on data analytics by combining the familiarity of a spreadsheet with the power of a modern BI platform. Sigma’s spreadsheet-like interface allows business users to work directly with data, perform ad-hoc analysis, and create dashboards without needing advanced technical skills. With Sigma, data stays in the cloud, and all queries run in the background, making it scalable and secure.

Sigma’s approach makes data exploration more accessible to those who are comfortable with Excel but want the advanced capabilities of a BI tool. Companies use Sigma to enable non-technical users to explore data and create custom reports, reducing reliance on the data team.

Core features:

• Spreadsheet-style interface familiar to Excel.
• Live connection to cloud data warehouses, ensuring up-to-date insights.
• Drag-and-drop functionality for data modeling and visualization.

5. SAS: Best for advanced analytics and statistical modeling

SAS is a tool for data manipulation, advanced analytics, and statistical modeling. SAS’s platform is built for data scientists, statisticians, and analysts who need to perform complex analyses, from predictive modeling to machine learning. The software offers a robust programming environment that supports various data sources and integrates with other data tools.

SAS’s strength lies in its advanced analytical capabilities, which are widely used in industries such as finance, healthcare, and government. Organizations rely on SAS to process large datasets, perform rigorous statistical analyses, and drive data-driven decisions.

Core features:

• Advanced statistical analysis and data mining tools
• Machine learning and AI capabilities for predictive analytics
• High-performance data processing for large-scale datasets

6. Looker: Best for data modeling and governance

Looker, a part of Google Cloud, offers a powerful BI platform designed to provide governed access to data with a focus on the semantic layer. Looker enables data teams to build reusable data models that can be shared across the organization, ensuring consistency and a single source of truth. With Looker, business users can explore data and create custom dashboards without writing SQL, making it accessible and scalable.

Looker’s integration with Google’s ecosystem further enhances its appeal, offering connectivity with Google BigQuery and other cloud data platforms. Its semantic modeling capabilities set it apart, allowing data teams to manage and maintain data definitions centrally.

Core features:

• Semantic layer for building reusable, governed data models
• Seamless integration with Google Cloud and other databases
• Self-service analytics with natural language processing

Check out our Looker Alternative paper for an in-depth comparison.

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