INTRODUCTION:
I chose two articles to critique in relation to the book Big Data, Big Analytics (Minelli, Chambers, & Diraj, 2013). One of the articles in this critique analysis is the “Academic Analytics Business Intelligence for Higher education”. This article describes the importance of academic analytics and business intelligence in the present education systems. Educators like Katz and Goldstein describes Academic Intelligence as the Intersection of Technology, Information, Management and Culture. Academic Intelligence is the application of information to manage the academic enterprise. This article gives the details of advance analytics in Higher education. Most of the universities now uses the students SAT GPA scores and other data for the report transaction. When combined this big data with predictive and prescriptive models, data and analytics tools gives educators the insights to help students make better their assessment results, remain in school, stay on track to graduation and improve their learning achievement. For example, using data from our end-of- year assessments, principals and teachers can identify the remediation opportunities that need to be offered in summer school, and jumpstart programs and classroom learning that kicks off the new school year. Teachers/Professors can take spontaneous reactions based on the student data/performance and can adjust their teaching styles and approaching to students in a new and better way thus taking their teaching levels to a bit higher and pushing students understandability levels to the next stage. This way the students get benefited with their increased levels of performance, get motivated to reach their goals and also the teachers/professors also get benefited. This topic also describes about the advanced analytics and how it helps during the enrollment of students by identifying the student’s past grades from the records and their levels of understanding.
“When schools really use the data to identify their weaknesses and then take action to improve instruction, they can turn around test results quickly,” says Michelle Ribant, director of general education for Eastern Upper Peninsula ISD.
The second article I took is “When Big Data Goes Bad,” by Joshua Klien. In this white paper, it was discussed why big data environments are at a greater risk for data quality problems. Data, in the wrong hands, whether malicious, manipulative or naive can be downright dangerous. Indeed, when big data goes bad it can be harmful and we need to ensure that we are ready when it does. The important concept that the article brings is that, the data that is generated are not as accurate as reality, but we rely on that information too much and completely depend on it. Data is essential because it provides us with valuable information that can be useful in our day-to-day lives. The usage of data can also be used to guess all sorts of different information that are very important to us. Having massive amounts of data can also cause of a lot of conflict as well. Data is very beneficial and useful to our society as it provides us various types of information. However, it can also be used in other ways such as, viewing all our activities. The collection that is stored on the behavior of every individual can be unwelcomed and a violation of privacy which reveals all our data. All of these moves make people in our society searched, tracked, and analyzed. Even though technology is beneficial, it can do more harm than good. It is important that companies and industries ensure that the data is used in a meaningful way, and aware of the flow of data without leading them to the wrong hands and risking all the privacy information of people.
CRITICAL ANALYSIS:
With reference to the book “Big Data Big Analytics”, a data scientist (the one’s who bring value to the way how data is being used in an organization) needs to apply multi-disciplinary talent (Math + Technology + Business + behavioral sciences) in order to sustain a culture of decision sciences. The data scientist will have the mathematical, statistical knowledge and analytical skills. It is mentioned in the chapter 2 “Industry Example” by Avinash kaushik “For any business success in the big data world the company needs to invest 10 percentage of technology and 90 percentage of the people who can analyze the data”. This is true in the digital marketing era. Data plays a crucial role in the business. The company needs the right people who can deal with the big data. “Abishek Mehta”, a former Bank of America executive and MIT (Media Lab executive) quoted the old way of data analytics technology stacking with “cross- communicating data” and working on “scale-up” mentioned the process to be very expensive but is very useful without moving the old data. The technology has changed in such a way that it enabled different approaches and made the work easier and more affordable to store, manage and analyze the data. Using this analyzed data one can improve their business.
Another example from the book is “Mu sigma” one of the world largest decision science company, has the different approach for creating the talent. They have the front-end team who studied the applied math and business majors for many years of experience to find and create the analytical talent. They started a program in the university and preparing the people according to their needs of the company’s successful path. In the article “Academic Analytics Business Intelligence for Higher education”, creating plans for the business environment is key attributes to success. This is what exactly explained in the book Big about the talent of decision science needed in all the organizations to set up the team with data scientists having professional traits like “learning over knowing”, “Agility” (able to cope with continuous transformation), “Scale and Convergence”, Multidisciplinary talent”, ‘Innovation” and “Cost effectiveness”. It shows the importance the components of the data are and they must useful in order for the industry to be productive.
In addition, the book, “Big Data, Big Analytics,” relates to the article, “When Big Data Goes Bad,” this book explains that having data is good but is very important to use it correctly. The article goes well with the concepts in the book of the dependent, independent, and interdependent phases within the available data. The process begins from the starting days, when data systems were new and users did not actually know what they wanted. Predictive and prescriptive models today yield useful information, maybe sometimes imperfect and incomplete information. It stepped forward successfully into the recent years when the users began to understand the analytical platform better, and the final phase formed into the big data age, where we can create combining with all the data generated. But the problem now is to get the data into responsible ways and continue to use it to our benefits.
It was also stated in the article, “When Big Data Goes Bad,” that since we have such huge data, the outcome from the information are often erroneous but all we know is very little and thinks that we know everything and simply depend on the computers and systems. This is an interesting concept and it explains the idea that by getting data, we ultimately feel that we know everything. Even though, data can provide a view of the future, it does not mean that it is entirely correct. Much of the information collected within data is true, getting it from accurate resources; such as books and reliable websites and resources. The other parts of the information within data that are produced today are from unreliable sources like the Internet. Depending on unreliable data and unclear data is not good which leads to the exposure of private life, and all sorts of data leading to too many problems in the future.
References
- “Mapping an Effective Location Intelligence Strategy” – Interesting Articles
- “Academic Analytics Business Intelligence for Higher Education” – Interesting Articles
- “Big Data Big Analytics” Michael Minelli, Michele Chambers, Ambiga Dhiraj
- http://www.hmhco.com/~/media/sites/home/teachers/files/hmh-cde_issue%20brief_dataanalytics.pdf?la=en