A Brief Thought: Big Data within Higher Education and Corporate Training


The Hypothesis:

Quite often, higher education and corporate training are not very different in terms of their responsiveness to turn information (Big Data) gleaned from implied analytics.

The Question:

What are some conditions that exist which explain these phenomena?

Complexity of the human factor. Big Data is partially defined by its sheer volume, variety, and voracity (Ettinger, 2014). To analyze and contextualize it requires the human factor. The complexity of the human factor is such that it causes one of the biggest challenges with Big Data.

Interpretation. My follow-up question would be, “If data is interpreted wrong, then is not any action taken based on the wrong interpretation, tainted?”

Reticence. Big Data is that which cannot be processed or analyzed (using traditional processes) and is collected and stored in its most raw or unstructured form (Eaton, 2012). Often, key stakeholders in higher education or corporate training are simply not ready for it. They have no idea how to prepare themselves for what to do with it, how to glean value from it, what its shelf life is, or whether it’s even ‘good’ data worth keeping.

Sheer Volume. Big Data is called Big Data for a reason! As an example and a catalyst for change, Big Data can help guide the research when investing in an institutional Learning Management System (LMS); or, it can help identify shifts in economic markets related to student recruitment and retention; or it can assist in the evaluation of trends and current-use segmentations (Macfadyen & Dawson, 2012). Clearly the opportunities of research and the amount of data out there to filter is beyond the capacity of most organizations, if they do not address these existing conditions and move to develop those which contribute to the gap(s) in tactics, as noted below.

The Opposite Question:

What are some conditions that do not exist, which contribute to the gap(s) in these tactics?

Data and statistical literacy. The users and consumers of Big Data require more than an elementary understanding of the nature and context of the data and its analysis. It is imperative that the organization provide the prerequisite PD and are ready to accept the responsibility and challenges of Big Data (Gaskins, 2015)

Transparency. The users and all key stakeholders of Big Data must subscribe to the concept of total transparency and education efforts (Gaskins, 2015). It comes to the following questions:

  1. What is being collected?

  2. Why is it being collected?

  3. by/from Whom will it be collected?

  4. Where/How will it be secured/used?

Clear outcomes and follow-through. (Macfadyen & Dawson, 2012) touched upon a key peril when it comes to Big Data…the lack of communication of the outcomes, and overall follow-through. It is not enough to collect, manage, analyze, and store data. There must be a clearly defined set of outcomes, constant communication, and follow-through. The momentum for, the development of and implementation of smart policy, must be maintained.

Resources:

Eaton, C., et. al. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, p.XV. McGraw-Hill, 2012. Retrieved May 21, 2015, from http://www.ariadne.ac.uk/issue71/charlton-et-al#3.

Etlinger, S. & TedTalks.com (2014). What do we do with all this big data? Retrieved May 21, 2015, from

http://www.ted.com/talks/susan_etlinger_what_do_we_do_with_all_this_big_data/transcript?language=en#t-464124.

Gaskins, L. (2015). Big Data: Promise and Peril in Higher Education. Retrieved May 21, 2015, from

http://education.cioreview.com/cioviewpoint/big-data-promise-and-peril-in-higher-education-nid-4582-cid-27.html.

Macfadyen, L. P., & Dawson, S. (2012). Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan. Educational Technology & Society, 15 (3), 149–163.


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