Analytics, also known as Business Intelligence, business analytics, and decision support most commonly, is a field that involves mathematics, statistics, predictive modelling, and machine learning techniques to find meaningful patterns and insights in data. The discipline is encompassing and multifaceted. Today we add powerful computers to the mix for storing, increasing amounts of data, and running sophisticated software algorithms, producing the fast insights needed to make fact-based decisions. By putting the science of numbers, data, and analytical discovery to work, we can find out if what we think or believe is really true, and produce answers to questions we never thought to ask. That's the power of analytics. From the first known population data collection project, by the Swedish government, in 1749, to Florence Nightingale, documenting and analyzing mortality data in the 1850s to British scholar Richard Doll's, tobacco and lung cancer study in the 1960s, to IBM Watson, helping doctors with diagnosis. The analysis of data has enabled, enhanced, and accelerate knowledge discovery for hundreds of years. We all know that instinct isn't good enough, and it's often wrong resulting in the loss of revenue, or even the loss of lives. Analysis of data can uncover correlations and patterns. There's less need to rely on guesses or intuition, and it can help answer the following types of questions: What happened? How or why did it happen? What's happening now? What's likely to happen next? With faster and more powerful computers and new analytics techniques, opportunity and balance for the use of analytics and big data, and even not so big data. As we will see, whether it's determining credit risk, developing new medicines, finding more efficient ways to deliver products and services, preventing fraud, uncovering cyber-threats, or retaining the most valuable customers, Analytics can help you understand your organization, and the world around it. What can analytics do for us? These are just a few of the benefits and some examples of each. We can discover and take advantage of trends. For example, what kinds of coursework are employers looking for in their new hires? Or what kinds of beauty aids are women, or men, or those in the LGBTQ community, increasingly discussing online? We can predict and forecast occurrences or amounts. For example, how much beer will be consumed in China next month, or what will our companies tax burden be in the coming quarters, or how many more cars can we produce by automating certain procedures. We can also use analytics to find hidden patterns like, why are customers churning, are they leaving you for your competitors? How does retail traffic flow through a shopping mall? We can also identify risks and threats. For example, fraudulent transactions such as insurance claims, or plagiarized term papers, or potential hot-spots for fires in a forest or reducing hospital misdiagnoses. We can also use analytics to identify opportunities, especially for new products and services like customer complaints, or product feature disuse, or how to price a new product, or designing a new credit card including its look in financial terms. We can also use analytics to optimize the performance or quality of processes, people, and machines, such as route optimization for package delivery, or maintaining optimal supermarket refrigeration temperatures, or crowd-sourcing stock-trading algorithms. We can use analytics to improve customer experience, like matching customers with customer care reps, or dynamically displaying web content, tracking eye movements of students, improving the flow of passengers through a subway system, and reducing fraudulent restaurant and hotel reviews. We can also use analytics to digitalize offerings, like tennis rackets with sensors on the handle, and match performance analysis, or virtual grocery stores and subways, or self-driving trucks and delivery vehicles, or even E residency offerings such as they do in Estonia. We can use analytics to develop new business models, like crop insurance for third-world farmers based on data collected on weather, and soil temperatures around the world. We can use it to optimize restaurant menus, we can use it to create machine-generated news reports, or financial audits, and even offering credit based on non-financial indicators of risk. Analytics can also help us monetize data, by selling retail sales and inventory data, or shopping basket analysis, or selling employment related data and analysis, or exchanging data in return for goods and services, or favorable terms and conditions. In the public sector, they're more concerned with using analytics to improve safety, like smart streetlights, and traffic lights, or whether analysis, or reducing off-label prescription drug usage. Then there's an emerging class of analytics called Analytics For Good, like tracking herds and poachers, or thwarting terrorist recruiting efforts, or reducing building energy usage, or tracking the spread of diseases. The importance of analytics is a corporate priority. Nearly 50 percent of corporate strategies, not just IT or business strategies, mentioned the importance of data, and the critical capabilities of analytics. This is likely to grow too close to 80 percent over the next few years. Business analytics means different things to different people, and they use different terminology to describe the same or similar concepts within the discipline. Some use BI analytics, business analytics, reporting decision support queries, statistics data science, et cetera. But at the end of the day, it's all about using data to make better decisions, and take the ideal action be they by humans, applications, or machines. Increasingly, it's the latter.
