While companies are now amassing more data than ever before, many firms are struggling to obtain real value from their data. Data-driven innovation remains just an idea for businesses that lack the technology, talent, and know-how to leverage data analytics. Fortunately, new business data analytics tools are making it easier for companies to transform their data into actionable insights—knowledge that can inspire innovation and change. Data intelligence solutions help firms produce and make available trusted, analytics-ready data and provide their employees with the tools to explore, ask questions of, and understand the data. Modern business data analytics solutions allow business users to locate the data they need, generate impactful insights, and achieve meaningful business outcomes. As a result, firms can better understand their customers, foster innovation, and stay agile in the face of rapid change.
Business data analytics refers to sophisticated tools and techniques that are used to explore and investigate an organization’s data—to surface valuable insights from the diverse data companies create and collect. Firms in industries ranging from retail and manufacturing to healthcare and hospitality are using business data analytics technology to:
To leverage business data analytics successfully, firms will need to invest in three critical components: reporting and analytics technology, data governance, and data literacy. Reporting and analytics technology help them turn their data into insights and distribute those insights to the people that can use them most—tools that support user-driven data discovery, visual analytics, augmented analytics, BI reporting, and embedded analytics. Data governance is what allows an organization to protect the integrity and privacy of its data and provide users with data that they can trust in an environment that is secure and compliant with industry standards and applicable laws and regulations. Data literacy is necessary for fostering a culture of data exploration and data-driven decision making, and it starts by helping employees feel comfortable working with data and encouraging them to validate their ideas using analytics. Finding and investing in a platform and vendor that will provide them with these components is a critical first step in becoming an insights-driven organization.
A business data analytics platform supplies you with tools and technology that simplify and expedite data aggregation, data profiling, data modeling, data exploration, data visualization, association, pattern detection, reporting, and other tasks. And because they’re geared towards business users, non-data scientists can explore data using simple interactions like selection, search, and drag-and-drop — without having to learn how to code or build analytical queries.
Natural language processing technology enables users to interact with data conversationally, while AI and machine learning algorithms are used to auto-generate contextualized insights for users and help them find trends and interesting relationships they might otherwise miss. With visual analytics, users can examine and explore data sets and investigate potential connections and correlations with minimal technical training. With the right business data analytics platform, organizations can process large amounts of diverse data fast—using it as a big data analytics tool—to uncover business-critical insights, boost productivity and operational efficiency, and improve and accelerate decision making.
Qlik Sense® is a comprehensive BI and data analytics platform built on Qlik’s one-of-a-kind Associative Engine, that sets the benchmark for a new generation of business intelligence solution. It is the most complete solution you’ll find for business data analytics. Featuring sophisticated AI and a scalable, high-performance architecture, Qlik Sense offers industry-leading analytics technology allowing you to tackle the full range of BI use cases from self-service data discovery and visualization to interactive dashboards and embedded analytics.
With Qlik Sense, businesses get unmatched analytical power and open-ended, curiosity-driven data exploration and search. Because Qlik’s Associative Engine indexes and understands all relationships in your data—no matter where it’s located—users can find, combine, and explore information freely, unbounded by pre-aggregated data or pre-defined queries.
AI-fueled conversational and augmented analytics capabilities in Qlik Sense help you create a data literate workforce by making it easier for users of all skill levels to interact with and surface insights from data. Insight suggestions, accelerated creation, automated data profiling, and smart visualizations let users dive in quickly, find the answers they’re looking for, and take action based on what they discover. And with embedded analytics, you can incorporate business data analytics directly into the mission-critical systems and applications your executives, business managers, and front-line workers regularly use, allowing them to access actionable insights when and where they need them.
Some common types of business analytics are time-series analysis, risk analysis, fraud detection, geospatial analysis, operational analytics, social media analytics, decision support, quality control monitoring, predictive analytics, and IoT analytics.
Business data analytics goes beyond traditional BI, which generally has been more focused on collecting and presenting data. Offering more advanced analytics capabilities, modern data analytics tools for the enterprise enable users to integrate, explore, and analyze data in more sophisticated ways such as using advanced modeling techniques and machine learning algorithms.
No, not necessarily. With modern BI and analytics tools, business users can load and profile data and perform analytical operations without having to write SQL queries or learn how to code.
Big data adds depth to and improves the accuracy of business data analytics, enabling organizations to gain deeper insights and get more accurate and complete answers. It is currently used for social media analytics, in customer relationship management, and for predictive analytics use cases including fraud detection, risk management, and inventory forecasting.