Today’s businesses rely on data—about their customers, competitors, the overall market and beyond—to remain competitive. With advancements in technology, the number of methods companies use to collect data has grown considerably in recent years. This has led to increasing demand for experts with the education to analyze and interpret that data.
An advanced degree, such as a Master of Science in Analytics, is designed to help interested individuals discover the answers to questions like “How is big data collected?” and learn how today’s businesses use data.
History of Analytics
While the history of analytics in business goes back as far as the 19th century, business analytics as a distinct discipline truly emerged in the 1950s. It was then when tools were developed that could capture information and identify patterns and trends faster than the human mind. IBM’s hard disk, invented in 1956, was particularly consequential for the analytics movement, paving the way for companies to replace physical filing systems with digital ones. These early efforts at business intelligence represented what data analysts often refer to as Analytics 1.0.
Characteristics of this era included small, structured and mostly internal data sources, batch processing operations that could take months, and limited, descriptive reporting. Analysts spent much more time collecting and preparing data than actually analyzing it, and whatever insights could be gained from analysis often came too late to be effective.
The early era of business intelligence lasted about half a century, from the mid-1950s through 2009 when the advent of big data emerged.
Big Data Era
In the mid-2000s, internet and social media giants such as Google and Facebook began identifying, collecting and analyzing a new type of data. While the term “big data” didn’t enter the common lexicon until about 2010, analysts recognized this new information was qualitatively different from the small datasets of the past.
Small data was generated by a company’s internal operations and transactions, but this new data came from external sources, drawn from the internet, public data sources and specific projects such as the Human Genome Project. This signified the switch to Analytics 2.0 and the big data era.
With the arrival of big data, new technologies and processes were developed at warp speed to help companies, both large and small, turn data into business insight that could help generate profit. Leveraging the advantages of big data however, required new processing frameworks such as OLAP and advanced tools like data mining to extract meaningful information. The data analysts of the Analytics 2.0 era were better positioned than their earlier counterparts. By applying the more advanced technology of the era—including automated data management tools—they were able to analyze data, trends and other information to inform strategic business decisions.
Evolution of Data Analytics
The Analytics 2.0 era was followed by 3.0, which lasted from roughly the late 2000s to early 2010s. This era was marked by the introduction of smartphones, the spread of social media as data collection tools, and new customer-facing services that used analytics to provide hyper-personalized user experiences. Many experts in fact believe that a fourth era—Analytics 4.0—has arrived, with the spread of advanced automated decision-making tools that rely on cloud technology.Among the more important developments in the evolution of data analytics have been the many advancements in how big data is collected. Organizations today have many methods for collecting data from their customers and constituents.Websites, social media platforms and customer phone calls, live chats and surveys are some of the most obvious examples of where and how companies gather their data. Other more complex methods also exist, including:
- Location-based advertising: Tracking technology that logs information such as IP addresses helps build personalized profiles of technology users. This information is then used to target each person’s devices with individualized advertising.
- Loyalty programs: These programs offer incentives to customers and allow businesses to craft a detailed profile of the consumer, indicating product preferences and spending habits.
- Online marketing analytics: A driving force in digital marketing, this typically entails a customer filling out an order form, which supplies that business with personal information. The business can then use this information to improve customer service and provide the consumer with a more personalized experience.
New analytics disciplines have emerged to complement descriptive analytics in the analytics portfolio. Predictive and prescriptive analytics—which give insight to the probability an event will occur in the future and recommend possible courses of action—are emerging as key tools for business executives. Analytics are available to support real-time decision making through the use of analytical apps.
This new era of Analytics 4.0 is certainly not the end of the evolutionary tale in business analytics. In fact, it presents new challenges and opportunities for businesses and data analysts alike. Those individuals who are able to both capture data and organize it, as well as analyze and use it to make better business decisions, are and will continue to be in high demand.
A Path to a Business Analytics Career
Effective data analytics are essential for modern-day businesses to grow. Companies need individuals who possess not only the technical expertise to gather, organize and analyze it, but also the business knowledge to understand how to turn that data into action.
An online Master of Science in Analytics from Villanova University can help you develop the skills to become a business analytics expert. Learn more about how the program can help you begin a rewarding career in big data.