Data Analytics and Data Integration in Qlik

Data Analytics and Data Integration in

Data Analytics and Data Integration in Qlik

Introduction To Qlik Sense:

Qlik Sense is software for data analysis and visualization. It works with the associative QIX engine, which allows the user to link and aggregate data from different sources and perform dynamic searches and selections. Qlik Sense is a data analytics platform for many users, from non-technical to technical users. Unlike QlikView, Qlik Sense is more about data visualization as it has advanced graphics. Whereas in QlikView, you can use ma and manipulate data in many technical ways through scripting. However, if your motive for using Qlik Sense is to display and analyze data in the best possible graphical way, then you have made the right choice.

Thus, Qlik Sense as a tool gives users great flexibility as they can perform independent operations with self-service visualizations and analyses or be guided by automated machine-driven analysis by a cognitive engine. Unlike the traditional query-based model, Qlik Sense uses an associative model in which users can explore vast and complex data structures in any direction and way they way and draw intuitive insights from it. It is also possible to seamlessly integrate large data sets from different sources. Collaboration features also feature in their ability to draw insightful conclusions from data. Client teams can share data applications and reports on a centralized hub, share secure data models, and export data stories to improve and flourish the business. 

Introduction To Qlik Sense

Below are some key features of Qlik Sense.

    • Self-service capabilities
    • Advanced visualizations and smart searches/surveys
    • Associative model
    • Centralized sharing and collaboration
    • Hybrid multi-cloud architecture
    • Interactive analysis
    • Interactive storytelling and reporting
    • Mobility and multi-device support
    • Big and small data integration
    • Business management and scalability
    • Robust security
    • Geographical and advanced analysis
    • Qlik Insight Advisor (AI support)
    • Scalability across on-premise, private, and public cloud environments

The data analysis process is initiated with a business objective, such as identifying promising new markets or weak manufacturing strategies. Once these goals are clearly defined, and the data has been collected, the primary steps in the process involve using a modern data analytics tool to integrate, manage and analyze the data and then share any findings.

  • Data integration and management
  • Data analysis
  1. Data integration and management

Before any data analysis can begin, raw data must be transformed into clean, business-ready information. Integration refers to gathering data from various sources within a business to provide complete, accurate, and up-to-date information for data analysis. It involves data replication, ingestion, and transformation to combine different data types into standardized formats stored in a repository such as a data lake or data warehouse.

Data analysis

  1. Data analysis

Once the raw data is transformed into available information, the analysis phase can begin. The goal here is not just to answer specific questions but to discover new and unexpected insights by examining the data and uncovering meaningful patterns, relationships, and trends. Here are seven key processes and technologies used in this stage of the data analysis process.

Self-service analytics: Enabled by modern data analysis tools, users can quickly analyze their data without writing code. Using an associative engine, the platform will enable users to freely explore data in any direction, recalculate analytics and highlight data relationships with each click.

Augmented analytics: It uses artificial intelligence (AI) and machine learning to enhance human intuition with suggested insights and analytics, task automation, search and natural language interaction, and advanced analytical computing.

Conversational analytics enables users to use text chat and natural language voice in analytics applications to explore data and discover insights. Powered by Artificial intelligence allows them to ask questions in their own words and get answers presented in an easy-to-understand conversational way. In addition, the best data analytics platforms enable users to use text search and interactive selections to filter within charts, tables, and other elements.

Data visualization and dashboards: They are an essential step in the data analysis process to share information and support collaboration across the organization. Visualizing data in tables, graphs, and maps make it easier for stakeholders to understand and interact with them. Likewise, modern dashboards make it easy for all users to dive in and explore the data themselves.

Reporting and alerts: These are also fundamental ways stakeholders can stay informed. Data analysis software should make it easy to create, publish, and share static reports in all popular document formats. The software should also provide real-time data alerts to help users stay on top of their business and take timely action.

Embedded analytics: It integrates analytics capabilities and content within applications, products, portals, workflows, or processes. This enables employees, customers, suppliers, and partners to quickly access data and insights in their workflows instead of switching to a separate analytics application. Embedding data analytics in this way helps people find insights and make better decisions faster.

Mobile analytics: This allows users to share their analytics and collaborate with other stakeholders on any device, even when they are offline. Because of how work is done today, users must be able to access and analyze their data wherever they are.

 

Thus Qlik Sense provides best-in-class analytics that empowers people of all skill levels to make data-driven decisions and take the best possible actions.

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