The volume, variety, and velocity that characterizes big data are what make it challenging to analyze. Using big data for analytics requires the use of specialized techniques and sophisticated platforms and architecture to manage it and transform it into business-ready data from which value can be extracted. Firms are collecting large quantities of data from e-commerce and traditional point-of-sale systems, mobile apps, social networks, websites, connected devices and sensors, and many other sites and sources. Organizing and integrating mostly unstructured data and incompatible data formats necessitates innovative forms of information processing. A modern big data management and analytics platform can make it easier for firms to synthesize and leverage distributed, diverse datasets—and finally use big data for analytics at the pace at which it’s being generated.
Firms in industries ranging from finance and banking to healthcare and entertainment are using big data for analytics: to mitigate risk, be more productive and effective, and make strategic decisions based on the insights obtained from massive, multi-dimensional datasets. Harnessing the power of big data using data exploration and advanced analytics tools including predictive analytics, organizations can uncover meaningful patterns and trends in their data streams and use the knowledge obtained to better understand the forces and factors affecting their business and make smarter decisions. They are in a better position to identify potential opportunities early, adapt to customer needs fast, develop novel products and services, and increase operational efficiency. Companies that invest in and effectively utilize big data for analytics are more agile, innovative, and competitive.
As a result of the increased availability and accessibility of analytics tools, affordable cloud storage, and ephemeral cloud computing servers, more businesses can now use big data for analytics to enhance decision making and strategic planning at all levels in and for various functions of the organization. Customer relations and marketing departments are leveraging internal datasets alongside data from social networking services, mobile apps, and web analytics to gauge customer wants and needs, deliver tailored promotions, offer personalized services to customers, and improve the customer experience on all channels. Operations management can use similar datasets to develop and design new products and services that will appeal to customers and use other types of data, from sensors and equipment logs, for example, to automate production processes, predict product fulfillment needs, implement predictive maintenance, and drive supply-chain efficiencies.
There are challenges, however, that many firms face in using big data for analytics. There is the difficulty of integrating data from legacy systems and ensuring that information is accurate and in the proper format for analysis, i.e. analytics-ready. There is a lack of data literacy among large parts of the workforce and a need for skilled data analysts. Firms struggle to make big data useful and actionable for employees that aren’t comfortable approaching the data. And finally, there are increasing concerns about data privacy and security. Now more than ever before, companies are under pressure to ensure that they are following industry standards and government requirements when collecting, handling, and storing consumer personal and sensitive data.
Qlik’s big data management and analytics platform helps firms overcome these challenges by accelerating the many steps needed to create clean, well-documented data, helping you transform raw, unruly data into organized, analytics-ready information assets. A mature data integration and transformation layer enables users to load, ingest, and combine data from multiple sources including legacy applications while advanced profiling suggests associations and automatically processes different data types. And this can all be done within a governed environment that adheres to strict security policies and is compliant with regulations like GDPR to ensure that data is protected from data ingest to delivery.
With Qlik, using big data for analytics is simpler. Qlik Catalog® is a modern data management solution that lets you quickly build a secure, metadata driven catalog of all your data. Data validation, data profiling, and quality measures document the content and quality of each source, helping ensure that trustworthy data is available and accessible for analytics initiatives. A smart data catalog makes each data element understandable and actionable, enabling users to “shop” for the data they need and reuse and share previously created assets. A role-based, secure shopping environment facilitates fast access to trusted data while ensuring that sensitive data is protected, letting you increase the value of your big data investment by making sure everyone in your organization can use it.
With Qlik Sense®, featuring Qlik’s unique Associative Engine, users of all skill levels can search for the data they need and spin up rich visualizations and analytics applications fast using drag-and-drop functionality. With smart visual data profiling, they can instantly see the relationships among tables and how they would associate, allowing them to create a data model* manually or use the built-in Qlik Cognitive Engine to automatically make the associations for you and get the job done faster.
Qlik’s Cognitive Engine lets you combine the power of big data, AI, and advanced analytics, providing users with context-aware insight suggestions and personalized automation aligned with each user’s selections. Non-technical users can get smart chart suggestions, letting AI render the best type of visualizations for them based on their data. Users spend less time finding and preparing data and more time making impactful discoveries. Qlik Sense® is the most complete BI and analytics solution on the market, supporting many BI uses cases. Whether it’s routine reporting, including KPI reporting dashboards and standard KPI reports, or more advanced analytical challenges—Qlik has you covered.
*What is data modeling? It is a formal representation of how data elements are related. It shows the data needed and created by business processes, determines how data is exposed to the end-user, and sets the stage for analytics.
It’s a critical asset for firms because it helps them gain valuable insights into their customers, operations, supply chains, and other factors affecting business performance. It also helps business users make predictions about future outcomes, identify and take advantage of emerging opportunities early, and get more complete answers to the questions they have.
It is characterized by its scale, diversity, and velocity. Big data refers to rapidly changing, massive, and complex datasets. The data is relatively unstructured, collected from a wide variety of sources, and exists in a variety of formats. Managing and analyzing big data necessitates the use of advanced techniques and technologies and innovative forms of information processing.
It is composed of transaction data, application data, machine data, social data, and enterprise data and is generated from a wide variety of sources including traditional point-of-sale systems, e-commerce websites, social networking services, mobile apps, connected devices and sensors, medical records, photo archives, equipment logs, and more.
It is used to optimize operations and supply chains, improve marketing and sales strategies, personalize the customer experience, analyze customer behavior and preferences, anticipate consumer demand, enhance products and services, minimize exposure to fraud and cybersecurity risks, manage financial risk, and obtain competitive intelligence.