Week One

Big-Data, Web 2.0, Business-Intelligence, etc. More industry buzz-terms. But, do they really mean anything? After all, Web 2.0 really was nothing more than a buzz-term to describe a collection of practices with no actual specification. Thus, when the terms "B.I." and "Big-Data" were introduced several years ago, I had the jaded idea that they were nothing more than another buzz-term a la Web 2.0.

However, when introduced to the term, "Datafication" it really becomes apparent why understanding Big-Data and the ideas that it encompasses is so important. Sitting back and pondering about datafication is also what makes Big-Data so interesting. Through society's adoption of computer and internet-based technology, data is becoming essentially infinite. The idea that we now have enough data to virtually skip modeling and extrapolation is exciting and awe-inspiring. Add to this the term "velocity." In physics, the term velocity describes speed and also direction. When applied to Big-Data, it is apparent that this term fits, as the realm of Big-Data is fast-moving and multi-dimensional.

Business-Intelligence (BI) has a more obvious meaning, and it has been a relevant term in the industry for at least ten years. However, now that we have moved into the time of Big-Data, BI takes on a new level of importance and possibilities. Because of the datafication of our current world, developing useful BI is more challenging than ever, yet it also has the possibility when done well to be more useful and impacting than ever.

In application, what I liked about the materials presented on BI was the emphasis on predictive modeling-- it seems to be the function that many organizations overlook. Much effort is placed on building and implementing dashboarding and reporting, but it appears that successful organizations understand first what they need to report on and how to use data to predict their next move within the marketplace. This idea was reinforced by the explanation of the "Data Scientist." It's a great description of what might actually be missing in many organizations. A scientist is someone who knows how to identify a problem, identify a means for taking measurements while controlling aspects or variables within the problem to produce data, and most importantly someone who knows how to analyze and use this data to form reasonable and meaningful predictions and outcomes. Thus, the "Data Scientist" may be exactly what many organizations are looking for-- whether or not they know it yet is what is to be determined.

Many of us have worked in an organization with an emphasis on metrics. I was part of a management team trying to develop the collection of metrics to be used to assess the performance of the organization. It's amazing what occurs when ten or twelve people get together in a conference room for this discussion-- they literally want to begin measuring any and everything. Without understanding the organization's goals, or identify the problem(s) needing to be solved, measurement can become overkill. Having the skills to identify what is actually important for use by an organization is the real power. 

Thus, the sum of what was covered in Week One was encouraging in the sense that I am happy to see that this course and program's emphasis appears to be headed in a good direction. It is good to see that we are looking at these two concepts in ways that nobody really did with the incarnation of the phrase Web 2.0.