Data is a critical business asset. It’s what drives innovation today and enables firms to stay competitive in the global marketplace. And now with the convergence of big data and AI, companies can more easily leverage advanced analytics capabilities like predictive analytics and more efficiently surface actionable insights from their vast stores of data. With big data and AI-powered analytics, firms can empower their users with the intuitive tools and robust technologies they need to extract high-value insights from data, fostering data literacy across the organization while reaping the benefits of becoming a truly data-driven organization.
Big data and AI have a synergistic relationship. Data is the fuel that powers AI. The massive, complex, and rapidly evolving datasets referred to as big data make it possible for machine learning applications to do what they were built to do: learn and acquire skills. Big data supplies AI algorithms with the information necessary for developing and improving features and pattern recognition capabilities. Without large quantities of high-quality data, it wouldn’t be possible to develop and train the intelligent algorithms, neural networks, and predictive models that make AI a game-changing technology.
AI, in turn, helps users make sense of sprawling, diverse datasets and sort through unstructured data that can’t be organized into neat rows and columns. AI enables firms to use big data for analytics by making advanced analytics tools more powerful and accessible, helping users discover surprising insights in data that was once locked away in enterprise information silos. Leveraging big data, AI, and advanced analytics, companies can provide their decision-makers with greater clarity and understanding of the many factors influencing their business while encouraging creative, intuitive exploration of large-scale, multi-dimensional datasets.
By bringing together big data and AI technology, companies can improve business performance and efficiency by:
AI can assist users in all phases of the big data cycle, or the processes involved in the aggregation, storage, and retrieval of diverse types of data from various sources. These include data management, pattern management, context management, decision management, action management, goal management, and risk management.
AI can identify data types, find possible connections among datasets, and recognize knowledge using natural language processing. It can be used to automate and accelerate data preparation tasks, including the generation of data models*, and assist in data exploration. It can learn common human error patterns, detecting and resolving potential flaws in information. And it can learn by watching how the user interacts with an analytics program, surfacing unexpected insights from massive datasets fast. AI can also learn subtle differences in meaning, or context-specific nuances, in order to help users better understand numeric data sources. And it can alert users to anomalies or unexpected patterns in data, actively monitoring events and identifying potential threats from system logs or social networking data, for example.
*What is data modeling? It is a formal representation of the content and structure of and relationships in a dataset used for reporting, analytics, or other use cases. It helps reduce data redundancy and ensures that the data will be compatible and meet the requirements of the end-user.
Qlik Sense® is a robust BI and analytics platform that is putting the power of data science, of big data, AI, and advanced analytics in the hands of regular business users. Non-technical users can now work with large, highly complex datasets, gaining insight into the people, events, issues, and trends affecting their organization. They can dive into big data through self-service, interactive dashboards and natural language searches and let Qlik Sense automatically process and visualize data for them, accelerating the time it takes to get from raw data to actionable insights.
Using a unique Associative Engine to index the data, Qlik helps users navigate massive datasets by automatically finding relationships between records and exposing possible avenues of inquiry. Using a Cognitive Engine that learns from the user’s past selections and preferences, Qlik auto-generates smart data visualizations and context-aware insight recommendations tailored to each user. That’s AI2 (Associative Indexing x Augmented Intelligence) With Qlik Sense, users of all skill levels can quickly load and combine data, create and publish personalized reports including KPI reports and spin up interactive dashboards that fit their specific needs using drag-and-drop functionality, i.e. no coding necessary. Automated data profiling and insight suggestions help users see their data in new ways.
AI makes big data analytics simpler by automating and enhancing data preparation, data visualization, predictive modeling, and other complex analytical tasks that would otherwise be labor-intensive and time-consuming. AI helps users work with, manipulate, and surface actionable insights faster from large, complex datasets.
Big data is the fuel on which artificial intelligence runs. Large amounts of diverse data are what make it possible for machine learning applications to do what they were designed to do: acquire and perfect a skill. The more data available to the AI, the more it can learn and improve its pattern recognition capabilities.
They include machine learning, which refers to the use of algorithms to learn and execute tasks without human intervention, deep learning, which uses neural networks to identify complex patterns in high-volume data, cognitive computing, which is used to simulate the functioning of the human brain to solve complex problems, and natural language processing, which helps computers understand and interpret human language.