The concept of compiling data and organizing it for easier analysis is as old as humanity itself. Predictive analytics, or the process of analyzing data in an attempt to predict future events, can be traced back as far as the 17th century.
In the infancy of insurance underwriting, Lloyd’s of London established the first major insurance market, which covered the shipping and trade industry. Financial specialists would analyze known variables, weigh risks, and decide whether to insure a client. If they took the risk, the bankers would sign their names under a list of potential risks, hence the term “underwriting.”
Fast forward to the 20th century. The term “information explosion” is coined in 1944 in reference to the surge of data experienced due to industrialism, communications, and globalization. After several decades (in the 1960s and 1970s), the need for compressing massive amounts of data became apparent.
Since the beginning of the information age, computer scientists and business experts have been in a constant state of adjustment. Increasing amounts of data required better compression and more storage. The more data was stored, the better computers had to be at parsing that data (organizing it into logical components).
By the dawn of the 21st century, data storage and data analytics were too large to ignore. Large corporations began to learn that data gleaned from website statistics, Internet traffic, purchase records, and browsing habits was invaluable to market research. Thus, the field of business analytics and an opportunity for students seeking an online masters in business analytics or an online masters in data analytics degree was born.
What is Data Science And Analytics?
The term “analytics” refers to the collection, organization, and study of massive amounts of data created by social media activity, browsing and shopping habits, connected devices (Internet of Things, or IoT), global positioning records, and any other human activity that can be logged, stored, and accessed for analysis.
“Basically, analytics refers to our ability to collect and use data to generate insights that inform fact-based decision-making,” says Advanced Performance Institute’s data expert Bernard Marr in his 2013 “What the Heck is… Analytics?” LinkedIn article, “Advances in information technology and a complete datafication of our world mean that we have (or will soon have) data and insights on everything.”
Datafication involves the process by which every activity and interaction is tracked and logged in a way that provides valuable social, economic, and even political insight. More devices are adding to the process of datafication each day. Every business that implements a new loyalty reward program instantly begins to collect personalized data on its customers that can be used to track shopping habits, product preferences, likes, and dislikes.
All of this datafication, spread out over years, has created a multi-billion dollar industry around data itself. We refer to this recently spawned industry as “big data.” Data scientists are those who study analytics and create new ways to link data sets, predict trends, and visualize complicated data comparisons.
Big Data, Big Opportunity
The big data and analytics industry has become synonymous with marketing and advertising in the world of business. Everyone has become familiar with “suggested recommendations” while surfing the web and engaging in e-commerce. Recommendations are analytics, chosen for us automatically by comparing our own habits and purchases with the habits and purchases of millions of other users.
Retailers have become reliant on big data for optimizing inventories, targeting customers, and developing new products. Through analytics, businesses can hone in on exactly what customers are looking for.
For example, customers who buy baby clothes are offered deals on diapers and formula, and king-sized servings of a popular candy bar are manufactured after analytics notices that the candy is often purchased in pairs.
Several support fields are also growing at an increasing rate, hand in hand with big data. Cloud solutions, iterative machine learning (artificial intelligence), software development, marketing firms, and R&D have all experienced surges in business over the past few decades, all in response to big data. Support fields either contribute to how data is collected and analyzed or take advantage of analytics insights in order to optimize business practices.
Human interaction is still a key part of big data and analytics, and every field continues to rely on humans to make sense of analytics. Jonathon Shaw, managing editor of Harvard Magazine, in his 2015 article “Why ‘Big Data’ Is A Big Deal” says, “[Data science] aims to create systems that let humans combine what they are good at – asking the right questions and interpreting the results – with what machines are good at: computation, analysis, and statistics using large datasets.”
The Growing Need For Analytics Professionals
Companies that have already implemented analytics into their business model are well aware of how fast analytics is growing and how beneficial it is to making informed decisions.
Companies that have not yet embraced business analytics, on the other hand, are finding out how necessary analytics will become in years to come. Soon, staying competitive will be nearly impossible without the ability to harness the power of big data.
“Technical skills, business skills, and soft skills are critical organizational success factors related to BA (business analytics) implementation,” say academic researchers Rachida F. Parks and Ravi Thambusamy in their paper “Understanding Business Analytics Success and Impact: A Qualitative Study” for the 2016 Information Systems & Computing Academic Professionals, Inc. EDSIG Conference, “There is a lack of appropriate talent in BA. The market growth for BA is driving demand for BA talent.”
Analytics talent will become increasingly sought after in the coming years, especially in the retail and wholesale, banking, media and entertainment, education, e-commerce, and transportation industries.
Specialties within each industry could involve data mining (the initial collection of data), data warehousing (the storage of data), visualization (organizing and presenting statistics on data), and predictive modeling (analyzing trends and forecasting future events).
Almost every current economic prediction model (which, by the way, are all largely based on analytics) points to a substantial increase in the demand for business analytics talent in the near future. “Big data and business analytics worldwide revenues will grow from nearly $122 billion in 2015 to more than $187 billion in 2019, an increase of more than 50 percent over the five-year forecast period,” according to Louis Columbus, director of Global Cloud Product Management at Ingram Cloud in his 2016 Forbes article “Roundup Of Analytics, Big Data & BI Forecasts And Market Estimates, 2016.”
Maryville University – Online Master’s in Business Data Analytics
The skyrocketing demand for business analytics experts lies at the heart of Maryville University’s online Master’s of Science in Business Data Analytics degree. At Maryville University, students learn how to handle massive amounts of critical business data using advanced analytical tools, making them invaluable to business decision-makers and corporations.
History of Predictive Analytics: Since 1689 – http://canworksmart.com/history-of-predictive-analytics/
A Very Short History Of Big Data – https://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/#28851f5f65a1
What the Heck is… Analytics?” – https://www.linkedin.com/pulse/20130624053353-64875646-what-the-hell-is-analytics
Why “Big Data” Is A Big Deal – http://harvardmagazine.com/2014/03/why-big-data-is-a-big-deal
Understanding Business Analytics Success and Impact: A Qualitative Study – http://proc.iscap.info/2016/pdf/4027.pdf
Roundup Of Analytics, Big Data & BI Forecasts And Market Estimates, 2016 – https://www.forbes.com/sites/louiscolumbus/2016/08/20/roundup-of-analytics-big-data-bi-forecasts-and-market-estimates-2016/#652005906f21