Analytics is the ability to see patterns, relationships, and trends within a large pool of data. It’s a critical piece of business intelligence (BI), but also helps solve problems and inform strategy by giving you insights that weren’t previously possible.
Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data. But to realize its true potential as a business tool, it must be democratized. That means anyone with the proper authorization should be able to use it. It should be self-service with point-and-click or drag-and-drop functionality, and offer guided, step-by-step navigation. And it should help business users automatically transform data into visual presentations to quickly build metrics and analyses without requiring specialized training or data science skills.
Traditional analytics solutions and processes require a significant amount of time, money, and resources. They often start with manually prepared spreadsheets that have to be merged before they can be used for analysis. This process of extracting and transferring data can take days to weeks, depending on the size of the datasets and applications involved.
In addition, errors in manual preparation can wreak havoc on analytical results. According to ZDNet, 90 percent of all spreadsheets contain errors that can have serious business implications. These issues can affect everything from marketing reports to financial forecasts.
The COVID-19 pandemic has accelerated the adoption of analytics technologies and pushed businesses to explore new ways to make money, cut costs, and navigate the turbulent “next normal” of business. As a result, analytics is one of the fastest-growing markets in enterprise software.
Descriptive analytics is a simple, surface-level type of analysis that looks at historical data and simply identifies the “what.” For example, annual revenue reports are examples of descriptive analysis, as are social media and Google Analytics tools, which summarize data into certain groups based on basic counts like clicks or likes.
Predictive analytics is a deeper analysis that attempts to establish cause-and-effect relationships. It’s the type of analysis that a company like Commonwealth Bank uses to predict credit risk or when it alerts customers to an increased risk of fraud. It’s the type of analysis that allows a company to deliver personalized offers and services to customers.
Advanced predictive analytics techniques such as factor analysis, cohort analysis, Monte Carlo simulations and more can be applied to a range of business problems to improve decision-making, forecasting, product development and more. A car manufacturer, for example, applies predictive analytics to sensor data from connected vehicles to create driver assistance and autonomous vehicle algorithms. A plastics and thin film producer saves 50,000 euros per month using predictive maintenance and health monitoring applications. And the Orlando Magic basketball team uses predictive analytics to optimize ticket sales, merchandise inventory, and player rosters.