MS in Information Technology Course Insight: Three Types of Analytics
MS in Information Technology candidates gain insight from a leading expert.
Big data can offer big opportunity. But to reap the benefits big data can provide, businesses must know precisely how to harness it for their organization.
“A key to deriving value from big data is the use of analytics,” says H.J. Watson in “Big Data Analytics: Concepts, Technologies, and Applications,” published in Communications of the Association for Information Systems. Watson is the author of more than 24 books and 200 scholarly articles about business intelligence and analytics. “Collecting and storing big data creates little value; it is only data infrastructure at this point,” Watson writes. “It must be analyzed, and the results used by decision-makers and organizational processes in order to generate value.”1
Watson’s article is required reading in Introduction to Big Data Analytics, a Walden University course for students earning an MS in Information Technology degree with a Big Data Analytics specialization. Read along with Walden’s online IT degree candidates as Watson identifies the three types of analytics that can turn data into actionable intelligence:1
It is useful to distinguish between three kinds of analytics because the differences have implications for the technologies and architectures used for big data analytics. Some types of analytics are better performed on some platforms than on others.
Descriptive analytics, such as reporting/OLAP, dashboards/scorecards, and data visualization, have been widely used for some time, and are the core applications of traditional business intelligence (BI). Descriptive analytics are backward-looking (like a car’s rearview mirror) and reveal what has occurred. One trend, however, is to include the findings from predictive analytics, such as forecasts of future sales, on dashboards/scorecards.
Predictive analytics suggest what will occur in the future (like looking through a car’s windshield). The methods and algorithms for predictive analytics such as regression analysis, machine learning, and neural networks have existed for some time. Recently, however, software products such as SAS Enterprise Miner have made them much easier to understand and use. They have also been integrated into specific applications, such as for campaign management. Marketing is the target for many predictive analytics applications; here, the goal is to better understand customers and their needs and preferences.
Some people also refer to exploratory or discovery analytics, although these are just other names for predictive analytics. When these terms are used, they normally refer to finding relationships in big data that were not previously known. The ability to analyze new data sources—that is, big data—creates additional opportunities for insights and is especially important for firms with massive amounts of customer data.
Golden path analysis is a new and interesting predictive or discovery analytics technique. It involves the analysis of large quantities of behavioral data (i.e., data associated with the activities or actions of people) to identify patterns of events or activities that foretell customer actions such as not renewing a cell phone contract, closing a checking account, or abandoning an electronic shopping cart. When a company can predict a behavior, it can intercede, perhaps with an offer, and possibly change the anticipated behavior.
Prescriptive analytics suggests what to do (like a car’s GPS instructions). Prescriptive analytics can identify optimal solutions, often for the allocation of scarce resources. It, too, has been researched in academia for a long time but is now finding wider use in practice.
For example, the use of mathematical programming for revenue management is increasingly common for organizations that have “perishable” goods such as rental cars, hotel rooms, and airline seats. For example, Harrah’s Entertainment, a leader in the use of analytics, has been using revenue management for hotel room pricing for many years.
Organizations typically move from descriptive to predictive to prescriptive analytics. Another way of describing this progression is: What happened? Why did it happen? What will happen? How can we make it happen? This progression is normally seen in various BI and analytics maturity models.
Learn More About Big Data Analytics
Earning an information technology degree online can help prepare you for today’s most in-demand computer science jobs.
Walden’s Big Data Analytics specialization can help you gain the knowledge and skills needed to turn big data into actionable intelligence to help drive enterprise productivity and increase operational efficiencies.
When you choose Walden for your IT degree, you’re joining an accredited university known for its information technology expertise. Top IT faculty members will lead you through coursework that can prepare you for jobs in software engineering, big data analytics, cybersecurity, and many other sectors.
If you’ve been considering a master’s in information technology, now may be just the right time to get started. Computer and information technology job opportunities are on the rise, with government and business projected to add 546,200 jobs to the economy between 2018–2028.2 What is driving this growth? The Bureau of Labor Statistics attributes the job expansion to cloud computing, the collection and storage of big data, and information security.2
Catch that wave of opportunity and let an MS in Information Technology prepare you for a rewarding and influential IT career.
Walden University is an accredited institution offering an MS in Information Technology. Expand your career options and earn your degree using a convenient, flexible learning platform that fits your busy life.
Walden University is accredited by The Higher Learning Commission, www.hlcommission.org.