Companies are finding themselves swimming in more data than ever before, and are looking for ways to use this data: to mine it for meaningful information, obtain actionable insights into their business and customers, and unlock the value of their growing stores of data.
Data analytics is the means through which organizations in all industries—financial services, insurance, energy, healthcare, manufacturing, travel, and others—extract valuable information from their datasets. But what is data analytics? There’s a lot of talk about using it but not enough discussion about what it actually means. Without an adequate understanding of what data analytics is, how can organizations feel prepared to make smart decisions about investing in it?
Data analytics refers to the use of processes and technology to combine and examine datasets, identify meaningful patterns, correlations, and trends in them, and most importantly, extract valuable insights. Analytics software, or business analytics software in enterprise use cases, is used to transform, organize, and model data; develop visualizations and dashboards that help users better interpret and understand the data; and, create reports and presentations essential for communicating the insights obtained from data analysis to other organizational stakeholders. What data analytics is, when it comes to software, is using technology to help people make sense of and interpret the information contained in an organization’s datasets.
Data analytics is typically categorized into four types: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics is the most common type of data analytics used by organizations today. Introductory and retrospective, it is used to evaluate and summarize historical data or explain what happened over a certain period of time. In a BI solution, descriptive analytics is used to provide information about historical performance via KPI dashboards or monthly sales reports, for example.
Diagnostic analytics, as the name suggests, helps in diagnosing, or finding the source of, a problem or determining why something occurred. Like descriptive analytics, it is retrospective, but diagnostic analytics requires taking an in-depth approach to analytics and may involve the use of data mining techniques. In business data analysis, it is used to evaluate the impact of online marketing campaigns, for example.
Predictive analytics is a more advanced type of data analytics and is used often in big data analysis. It is used to identify patterns in data to project the probability of outcomes or forecast trends based on current and/or historical data. Using statistical techniques, predictive modeling, data mining, and machine learning, predicted analytics helps identify potential risks and opportunities. In the business world, it is used for fraud detection, sales forecasting, credit risk assessment, online product suggestions, and other use cases.
Prescriptive analytics is the least common type of data analytics because few organizations are equipped for its implementation. It is used to suggest an optimal course of action to take based on an assessment of possible scenarios and typically requires the expertise of data scientists, or those with experience working with advanced modeling techniques. With recent advances in artificial intelligence and machine learning however, more users may soon be able to leverage this type of analytics by using sophisticated data analytics software.
As you can see, what data analytics is and what it can do varies depending on how it is used and the tools available. Today it is used by tech companies, online retailers, media companies, law enforcement, governmental agencies, manufacturing companies, healthcare providers, and many other types of organizations. As mentioned above, it can be used to detect and prevent fraud, manage credit risks, and for making product recommendations. It is also used for predicting service disruptions, managing inventory, detecting market trends, anticipating customer behavior, reducing compliance risks, and other initiatives. User experiences range from self-service analytics to interactive dashboards, mobile analytics, conversational analytics and reporting. With data analytics, companies can make smarter, faster insights-driven decisions, improve operational efficiency, seize critical opportunities, and add more value to their business.
With the right software solution in place, firms can leverage data analytics to get deep insights into all aspects of their business—finance, sales, marketing, product development, HR, logistics, and more. They can quickly find and eliminate non-essential time-consuming and costly processes, optimize existing systems and practices, better understand their customers’ needs and preferences, deliver improved products and services, achieve greater marketing ROI, and discover new opportunities to grow their business. What data analytics is, ultimately, is a way for companies to be more efficient, more competitive, and more agile. Data analytics helps firms stay resilient in the face of rapid changes in the marketplace and technology.
With Qlik Sense®, a modern AI-powered data analytics platform from Qlik, organizations get access to a new generation of business intelligence software. Featuring a unique associative analytics engine, sophisticated AI capabilities, and scalable multi-cloud architecture, Qlik Sense is the most complete solution for enterprise data analytics. By augmenting your workforce’s existing skill sets—and human intuition—with AI-fueled insight suggestions and automation, Qlik Sense empowers everyone in your organization with the tools they need to make better decisions and tackle even most complex analytics projects.
Qlik Sense supports all the major analytics users and use cases in an organization, including self-service, dashboards, conversational analytics, mobile analytics, reporting and more. Because Qlik Sense indexes and understands every relationship in your data, users can freely search and explore datasets to uncover insights they might not otherwise find with traditional query-based BI tools. From one robust platform, they can easily combine and load data from diverse sources, create smart visualizations, and work with interactive charts, tables, and objects, leveraging visual analytics to pinpoint outliers and reveal important patterns and correlations. And with support for conversational analytics and natural language processing, users can even ask questions directly to Qlik Sense. What data analytics is, with Qlik, is an accessible, easy way to extract meaningful insights from your data and make your employees smarter, more productive, and truly data-driven.
Data analytics is used to find patterns, relationships, and anomalies in data and make data more understandable to humans. It is used to evaluate and summarize historical data, determine the cause of specific outcomes, forecast trends, predict potential risks, and examine and suggest possible actions to take. Data analytics helps organizations improve decision making and strategic planning.
It refers to the use of advanced analytics techniques such as predictive modeling, data mining, and machine learning to examine massive amounts of structured, semi-structured, and unstructured data collected by organizations at high velocity. It is used to identify hidden patterns and relationships and surface meaningful insights from high-volume, high-variety data.
It is technology used to convert raw data into actionable business intelligence. Companies leverage data analytics to identify key growth opportunities, detect emerging market trends, analyze and anticipate customer behavior, inform product development, optimize marketing initiatives, enhance operational efficiency, mitigate and manage risk, and inform strategic and tactical decision-making across the enterprise.