Understanding Big Data Analytics
Module 5 - Case
Information networks as "enterprise glue": information mobilization and deployment
The core of the case for this module involves your careful assessment of the sources of strategic enterprise information. But before you’re ready to tackle it, you need to get somewhat up to speed on the underlying issues and dynamics. The following two articles are highly suggested as briefing material:
Nobel, C. (2010) How IT Shapes Top-Down and Bottom-Up Decision Making. Working Knowledge: Harvard Business School. November 1. Retrieved November 25, 2010, from http://hbswk(dot)hbs(dot)edu/item/6504.html?wknews=110110
Hayles, R.A., (2007) Planning and Executing IT Strategy. IT Professional Magazine. Sep/Oct. 9(5):12-20.
Now, one of the hottest trends in current enterprise information systems is what's often referred to as "big data" -- that is, giant databases of stuff gathered from customers (e.g., all the information about your supermarket purchases automatically entered each time you swipe your Von's or Safeway card through the checkout to get al those cool discounts), websurfing, suppliers, internal monitoring, etc. Big Data was first enabled through the enormous increases in the availability of low-cost data storage (down to $30 per terabyte at Fry's Electronics, as of today's paper), but it took the development of good data analytic tools to really spark the trend. Here are two interesting summaries of issues involved in Big Data at the moment:
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. and Kruschwitz, N. (2010) Big Data, Analytics and the Path From Insights to Value. MIT Sloan Management Review. December. Retrieved September 16, 2011, from http://sloanreview(dot)mit(dot)edu/the-magazine/2011-winter/52205/big-data-analytics-and-the-path-from-insights-to-value/ [The ptional Readings contains a link to the full report, if you're interested.]
Webster, J. (2011) Understanding Big Data Analytics. SeaRchStorage.com. Retrieved September 16, 2011, from http://searchstorage(dot)techtarget(dot)com/feature/Understanding-Big-Data-analytics
There's a lot more out there in the optional and supplemental readings as well as the wide wonderful world of the Internet to give you a feel for whether or not this all makes any sense; the more widely you can spread your own information gathering net, the more effective your analysis is likely to be.
So the question for discussion basically is to what degree ought organizational decision making be driven by "evidence" derived from analysis of trends in Big Data? Are such data reliable? How much power might the analyst have over the results? What other kinds of information, if any, might be used for decision making? Big Data's not going away -- in fact, "Huge Data" may be just around the corner -- so how can we best harness this new horse to the enterprise so that it doesn't run away with everything?
Your task is pretty simple: write an effective short paper on the topic:
"To what degree should organizations depend on the analysis of large databases and other IT resources to formulate basic strategy?"
This could even be fun!
Assignment Expectations (50 points total)
Length: Minimum 5–7 pages excluding cover page and references (since a page is about 300 words, this is approximately 1,500–2,100 words).
Assignment-driven criteria (25 points): Demonstrates clear understanding of the subject and addresses all key elements of the assignment.
Critical thinking (10 points): Demonstrates mastery conceptualizing the problem. Shows analysis, synthesis, and evaluation of required material.
Scholarly writing (5 points): Demonstrates writing proficiency at the academic level of the course; addresses the Learning Outcomes of the assignment.
Quality of references (4 points) and assignment organization (3 points): Uses relevant and credible sources to support assertions. Assignment is well organized and follows the structure of a well-written paper.
Citing sources (3 points): Uses in-text citations and properly formats references in APA style.
Understanding Big Data Analytics
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Understanding big data analytics
Introduction
Understanding how best and to what extent executives can rely on big data analysis when formulating business strategies is very important (Palem, 2014). There is no doubt that big data sets help analyze terabytes of data within a short period. Most executives find it a worthy investment as compared to traditional methods of data analysis. However, it seems good enough to understand the level to which one can depend on it. It is clear that companies collect data of their activities and keep them in databases. This may include information about how many customers are exchanging ideas concerning their brandonline and even the number of customers an organization has. Generally, big data is defined as large sets of data which common software tools find it difficult to manage and process within the required period of time. Challenges showing up in all aspects of data management such as sharing, capture, curation, transfer and analysis need to be considered when deriving a business strategy. This paper thus provides an insight aimed at making executives understand when and to which levels should they depend on big data analysis for formulating business marketing strategies.
Understanding the concept
It is clear that big data analytics is a field on the rise and enjoys a great deal of diversity. This thus leaves its definition alone not quite helpful enough. Any company wishing to fully utilize it must master the underlying concept correctly (Webster, 2010). There are numeroustechnologies associated with Big Data analytics from which one can draw conclusions on the perfect strategies for a particular company. To slice it down for executives who find it difficult, it is important to look at it from two perspectives; as a storage platform and as a problem solver (Palem, 2014). As a storage platform, it helps stores volumes of data from multiple sources in a reliable manner. For instance, it can be used to keep real-time data from weblogs, sensors and GPS locators for to enable instant access by a large number of users at a given time. From a problem solving perspective, uses a large number of distributed computing machines to lessen the "time- solution" ratio. For example, when exploratory analysis of credit cards data help identify or detect any signs of fraud. Palem opines that this is the main feature that highly differentiates other techniques of data analysis with Big Data. Combining the two, as a storage platform and as a solution enabler, gives what is called Big Data analytics.
With the traditional data warehousing processes being too slow and unable to bring data from structured and unstructured sources together for clear analysis, the Big Data technique seems to be the perfect option.It is only after CEOs delve deep into the field that they can be able to understand when and to which extent Big Data can be useful to the organizations. Researches on how effective data usage can aid in development show that this technique has the capacity to contribute much on organizational development. However, Big Data analysis presents its own unique challenges to the different areas employed. It has become a crucial part of decision making in different areas of development thanks to its cost effective nature. The concept is not that difficult to understand, but the complexity arises when it has to be relied upon in coming up with marketing strategies. An executive can only understand the right stage at which a company requires to embrace Big Data analysis if he/she comes to terms with some of its characteristics.
Characteristics of Big Data
In trying to define Big Data, 2012, Gartner describes it as a high volume of data with high velocity and variety that calls for upgraded forms of processing to provide an insight during the decision making process. Many people can easily relate this to Business intelligence, but there is a clear line between them regarding the manner in which data is used. Big data utilizes inductive form of statistics and frameworks from nonlinear system identification to try and provide relationships in large sets of data aimed at predicting outcomes. To add on the 3Vs (velocity, volume and variety),complexity and variability are the other characteristics of Big Data.
These features are what prompt a CEO to ditch the so termed ineffective traditional tools to try out Big Data analytics. There is no doubt that the effectiveness of the process in managing and acting as a compass in basic decision making has crowned it corporate agenda in most organizations and even governmental sectors (Barton,&Court, 2012). A Harvard Business Review by the two clearly opines that many CEOs admire the manner in which Google, IBM, Amazon and even Hewlett-Packard eclipse their competitors with their strange ability to analyze Big Data for betterment. This trend has generated plenty of hype as every executive wants to pay attention. A study by Erick Brynjofsson and Andrew McAfee of MIT revealed that companies that subscribe to the whole tide of Big Data analytics record more than 6% higher profitability and productivity rates than their competitors in the same field. Though many of them opting for it, there is need to understand the degree to which this can impact on the decision making process of any company. This makes it important to consider its characteristics before defining the level to which marketing or other business strategies are drawn based on predicted outcomes.
General approach to analytics
Many executives are aware of the approaches to analytics, but most of them have to deal w...