Data Platform

A data platform is an integrated software solution that helps organizations govern diverse types of data from across the enterprise and then make it available to business user in order to accelerate analytics efforts. Technology is used to streamline and automate tedious and time-consuming processes like data profiling, data cleansing, data modeling, and data validation, enabling users to spend less time preparing data and more time uncovering valuable insights.

With the right data platform, users across the organization get easy access to high-quality data and a unified view of information from multiple sources while IT maintains control over access and usage permissions. Businesses can enhance data governance and ensure data consistency while improving the availability of data and boosting workforce productivity. And with a high-performance analytics engine built-in, developers and business users can leverage AI and machine learning techniques to develop and deliver rich analytics applications and implement big data and IoT analytics projects.

Not all data platforms are the same, however, and choosing the right solution for your organization can be challenging. Here are a few key features to consider when comparing vendors:

• Connectivity: Does the platform provide you with pre-built connectors to load and combine data from the databases, data warehouses, and other data sources you want to use?

• Deployment: Is the data platform quick to deploy and does it offer flexibility to deploy the way you want—whether on-premises, in the cloud, or a hybrid environment?

• Scalability: Will it maintain good performance as the amount of data and users increases? Can it be used as a big data analytics tool, i.e. as a front end for big data analytics projects? Can the platform connect to your data lake and scale for analysis of massive data sets without sacrificing performance?

• Ease of use/data democratization: Does it provide easy access to data using a data catalog and self-service data discovery tools? Can users combine and manipulate data using an intuitive visual interface?

• Advanced analytics: Can you leverage machine learning to enhance analytics processes and support advanced analytics use cases such as predictive analytics?

• AI: Does the data platform offer built-in AI or ML to automate processes, guide user exploration, and surface actionable insights from large, complex data sets faster?

Qlik, the leaders in user-driven business intelligence, has developed the industry’s only end-to-end data platform combining data sourcing, data preparation, and data analysis tools in one robust toolset. With support for real-time data integration, modern data management, smart data cataloging, and AI-fueled analytics, Qlik helps you bring distributed, diverse data together fast and makes it available to users in a governed environment to transform all your data into real value.

Qlik’s Data Integration layer helps you turn your raw data into analytics-ready information assets by enabling you to:

• Move data at scale in real-time through streaming data pipelines with change data capture

• Automate the design, deployment, and management of data warehouses and data lakes

• Make trusted data easily accessible to users via a secure, enterprise data catalog

Qlik’s Data Analytics layer allows you to turn your data into valuable insights by empowering users with intuitive, AI-powered tools that let them explore your data without boundaries.

Businesses in more than 100 countries around the world have chosen Qlik to help them leverage and lead with data. Qlik Sense®, a next-generation BI and analytics platform, gives Qlik’s customers industry-leading analytics capabilities to support a wide range of use cases—from self-service analytics and interactive dashboards to pixel-perfect business intelligence reporting. What makes Qlik truly unique for users is its Associative Engine, which indexes all relationships in your data. It lets users easily load and combine information from all your data sources, no matter how large or imperfect. There’s no need to fully clean or model data in advance as the Associative Engine makes your data instantly available and ready to be explored. Users can search and explore data in any direction, unhindered by pre-aggregated data or predefined queries.

And now with Qlik’s Cognitive Engine in the mix, users can work faster and smarter and get more creative with data. AI-fueled capabilities like intelligent data preparation, auto-generated visualizations, and context-aware insight suggestion helps users look at their data in new ways and are shown important connections they might otherwise have missed. Qlik Sense enables even non-technical users to access, analyze, and take action with data, helping them build confidence as they make bold discoveries. Qlik is the only data platform that allows for open-ended, curiosity-driven exploration, giving everyone the opportunity to interact with and learn from data.

What is a modern data platform?

It is a software solution that enables organizations to break down information silos and efficiently make diverse, distributed data available to users in a governed self-service environment. Users can easily find, combine, and explore different types of multi-sourced data to carry out analytics and obtain and share actionable insights across the organization.

What is a customer data platform?

It is a software solution that allows a company to aggregate various types of customer-related data in a single, persistent database. It is used to create unified customer profiles and provide marketers with a comprehensive view of the customer.

What is a modern data architecture?

It is the result of a collaborative effort between business and IT. It refers to data architecture that is user-driven, flexible, and elastic, or adapts to changing information needs, while also being governed and secure. It is also automated and smart, leveraging AI and machine learning where possible to boost efficiency.

What is the difference between data governance and data management?

Data management refers to the processes of ingesting, storing, organizing, and maintaining an organization’s data. Its goal is to make sure that data is accurate, consistent, trustworthy, and accessible to the users that need it. Data governance refers to policies and procedures used to ensure effective data management, or to control data access and usage and properly manage the availability, integrity, and security of enterprise data.