Big Data Analytics Tool

As we’re currently seeing in industries ranging from healthcare and manufacturing to insurance and financial services, big data is being used to help people better understand complex, rapidly changing situations, make predictions, and gain important insights to drive smarter decision making and strategic planning. A big data analytics tool provides organizations with the technology necessary for leveraging massive amounts of diverse data.

Firms can only realize the potential value of all the data they’re creating and collecting by extracting useful, timely insights from it. Big data analytics tools make it easier for organizations to synthesize and analyze distributed data and take a data-driven approach to decision-making. Innovative, AI-powered business data analytics tools allow business users and non-data scientists to aggregate and mine high-volume, high-variety data sets, uncover unexpected relationships and patterns, and come up with innovative solutions to difficult problems.

The role of the big data analytics tool

Exploring and analyzing big data requires sophisticated platforms and architecture to handle and process extremely large amounts of data efficiently and effectively. Thanks to affordable cloud storage and ephemeral cloud computing servers, organizations can get fast, on-demand access to the infrastructure and advanced technology they need to tackle big data initiatives. Big data analytics tools are data platforms and applications that leverage scalable, multi-cloud architectures and enable firms to quickly collect, combine, transform, and process massive amounts of structured, semi-structured, and unstructured data. Through data intelligence capabilities, they also help firms give their workforce access to various types of multi-sourced data. Some big data analytics tools automate time-consuming tasks such as data profiling and transformation. They enable efficiency, and depending on the tool, real-time data integration across the enterprise.

Key features and technologies to look for in a big data analytics tool

Finding the right big data analytics tool for your organization can be a challenge, however, due to the variety of solutions on the market today. Here is just a short list of key features to consider when comparing tools:

  • Visualization: Does the tool make it easy for users to create and interact with data visualizations? Does it provide interactive, graphical displays of data enabling non-technical users to explore and examine complex data sets?
  • In-memory processing: Does the big data analytics tool you’re considering allow you to analyze data in-memory, in addition to resident data in data lakes or data warehouses, in order to accelerate analytical processing?
  • Advanced analytics: Does the tool leverage, or let you leverage through integrations, machine learning techniques that can be used to produce precise models for advanced analytics use cases such as predictive analytics?
  • Augmented analytics: Does the tool use AI to suggest relevant insights based on relationships in the data and user input, and automate tasks such as data profiling, data transformation, and analytics creation?
  • Connectivity: Does the tool offer pre-built data connectors allowing you connect to and combine data from your enterprise systems, clickstreams, sensors—a must for IoT analytics—and other data sources?

The advantages of using a big data analytics tool

Robust big data analytics tools not only simplify and accelerate data integration and data preparation but also help users transform raw data into informative graphics and actionable insights. They enable a broad range of business users to prepare, explore, and analyze data without having to learn programming and other data science skills. With a big data analytics tool, non-technical users can leverage an organization’s many stores and streams of data to create interactive dashboards, compelling reports, and rich analytics applications that can be shared across the enterprise. By streamlining the data processing and insights discovery process, today’s big data analytics tools empower more people than ever before to understand and use data to identify meaningful trends and patterns, anticipate risks and opportunities, and discover new ways of doing business in competitive and ever-changing environments.

Qlik: Your partner for big data analytics in the enterprise

Companies in more than 100 countries around the world depend on Qlik to turn their raw data into transformative insights. As the pioneer of user-driven business intelligence solutions, Qlik can help you leverage your existing big data investments and accelerate your ROI with a robust integrated toolset for big data analytics. Qlik Sense®, Qlik’s modern data analytics platform for the enterprise, provides you with out-of-the-box connectors to quickly load and integrate data from Hadoop, NoSQL, Microsoft SQL Server and newer platforms like Amazon Redshift and Google BigQuery. With a modern data integration and transformation layer, your users can combine a variety of data sources fast and prepare data visually, with advanced profiling technology to suggest associations in the data while automatically processing different data types.

With Qlik’s big data analytics tools, you can make massive amounts of powerful data accessible to teams across your organization, democratizing data and fostering a culture of data exploration where people actively consult the data to solve problems and validate their ideas. Dynamic views offer live connections to high-performance data sources, and on-demand app generation creates slices of data for in-memory analysis. Users are empowered with self-service big data discovery, data visualization, dashboard creation, report publishing, and advanced analytics capabilities. Save time on business intelligence reporting while letting your entire workforce explore big data and discover new ways of driving business performance.


What tools and technologies are used for big data analytics?

They include machine learning, which is used to automate data modeling and for advanced analytics use cases such as predictive analytics; data mining, which is used to examine and identify patterns in high-volume data; in-memory analytics, which is used to accelerate data preparation and analytical processing tasks; and, text mining, which uses machine learning and natural language processing technology to analyze and uncover relationships in text-based data sources.

What is analytics in big data?

It is the use of sophisticated technology and techniques to process extremely large, diverse data sets. Advanced analytical techniques are used to uncover meaningful patterns, trends, correlations, and connections and extract useful information from massive and complex data sets.

What are data analytics tools?

They are specialized software solutions used to transform, organize, model, examine, and surface insights from data. They help users discover important patterns, trends, and relationships in data, develop charts and other types of data visualizations, and create presentations and reports to share their insights.

How do companies use big data analytics?

Firms are currently using it to predict market trends, anticipate consumer demand, personalize the customer experience, and create targeted online marketing campaigns. It is also being used to detect and mitigate fraud, create more efficient supply chains, optimize pricing and inventory management, and implement predictive maintenance.

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