Powerful cloud-based analytics software, new predictive techniques, and intuitive user interfaces have made predictive analytics and modeling more accessible to organizations and business users than ever before. Modern self-service BI and data visualization tools make it easier for business analysts and citizen data scientists to leverage big data for analytics and create their own predictive models using automated modeling tools. Developers can use these same tools to build predictive applications that can then be embedded directly into business processes.
Users of all skill levels can now begin to incorporate predictive analytics—and the actionable insights they surface—into their day-to-day workflows and decision making processes. By enabling firms to harness the power of both big data and AI, modern analytics platforms are bringing predictive analytics into virtually every industry and helping organizations reinvent the way they do business.
Predictive analytics is a type of data mining that uses machine learning and advanced statistical modeling to analyze data and make inferences about what is likely to happen in future scenarios. Statistical analysis techniques, analytical queries, and machine learning algorithms are applied to data sets to find patterns and relationships in historical data and create predictive models. These are used to estimate the likelihood of future outcomes.
In predictive analytics, which is a form of data science, variables are manipulated to create forecasts from an organization’s data stores. The results of analysis can be used to identify potential risks and opportunities and act proactively. Advanced decision support systems leverage predictive analytics to predict trends and behavior patterns, determine the best course of action, and accelerate decision-making processes. The benefits of predictive analytics include swifter, smarter decision making, increased competitiveness, greater agility, and improved risk management.
The analytics software landscape has evolved to accommodate non-technical users and offer connections to more types of data sources. As a result, predictive analytics is finding its way into more organizations and is now found in industries ranging from financial services and telecom to retail and travel. It is currently being used for customer behavior analytics, fraud detection, credit scoring, inventory forecasting, resource management, predictive maintenance, and many other use cases.
In healthcare, predictive analytics is now being employed to create risk scores for chronic diseases, prevent patient self-harm, and predict patient utilizations patterns. In retail, it is being used to automate customer segmentation, anticipate consumer demand, optimize promotions and marketing campaigns, and improve inventory management. And in cybersecurity, predictive analytics is helping firms determine the probability of attacks and implement defenses before breaches occur.
In the past, if firms wanted to carry out predictive analytics, they needed data engineers to assemble and prepare the data for analysis, data scientists to build predictive models, and software developers to build visualizations and dashboards. Today with a new generation of robust self-service BI and analytics tools, non-data scientists in enterprises large and small can now build and manipulate their own datasets from a diversity of sources using visual analytics and big data analytics. They can use intuitive data exploration tools to quickly and easily find and prepare the data they need and leverage visual modeling or automated machine-learning modeling software to bring predictive analytics into their workflows. And with a highly scalable, data analytics platform, the power to visually explore predictive analytics can be put directly in the hands of all types of users.
Qlik Sense, the industry-leading BI and analytics platform, is setting the benchmark for a new generation of AI-powered analytics solution. Featuring a one-of-a-kind Associative Engine, sophisticated AI, and a highly scalable, multi-cloud architecture, Qlik Sense is the most complete analytics offering you’ll find. Qlik Sense lets users tackle every BI use case they can think of—from interactive dashboards and KPI reports to embedded, mobile, and conversational analytics.
Qlik combines enterprise-grade data governance with AI-assisted data visualization and data exploration, advanced analytics, and intuitive, self-service data preparation capabilities to empower everyone in your organization to make impactful discoveries in your data. Users can easily load, transform, and enrich data—without the need for complex scripting—and leverage best-in-class predictive analytics to support use cases such as fraud detection, sales forecasting, and resource management. Qlik Sense uniquely delivers the power of predictive analytics models to the masses, by supporting real-time interactive exploration through visual dashboards. Qlik’s partnerships with Data Robot and Big Squid, along with R and Python integration, mean users can now access automated machine-learning modeling and on-demand predictions directly from within Qlik Sense, letting them generate predictive insights whenever and wherever they need them.
It is used to find patterns in historical data, create predictive models, and analyze an organization’s data to predict trends and behavior patterns and estimate the likelihood of future outcomes. Predictive analytics can be used to inform and accelerate decision making, mitigate risk, and determine the best course of action in a complicated situation.
Some examples include fraud detection, marketing campaign optimization, customer behavior analytics, sales forecasting, inventory forecasting, predictive maintenance, and resource management. Predictive analytics is used not just in marketing and financial services but also in insurance, retail, travel, healthcare, pharmaceuticals, telecom, and other industries.
The benefits include smarter, faster decision making, increased organizational agility and market competitiveness, reduced uncertainty, enhanced risk mitigation, improved production efficiency, and better resource management.
Data mining and statistics are used to develop models that can be used to examine datasets in order to identify patterns and predict the probability of future outcomes. Predictive modeling software uses processes and techniques like decision trees, regression analysis, and gradient boosting to create simple linear or complex equations or neural networks, which are used to determine the probability of an outcome given a set of circumstances.