Wednesday, November 16, 2016

Smart Data in Business

Amber Lee Dennis wrote a very interesting article on big data titled, “Big Data and Smart Data: Big Drivers for Smart Decision Making”. In this article, she explains how data is only smart if it gives smart decisions, but on its own it is just facts. She explained that big data as a field must be fast, comprehensive, and scalable, and because big data has those characteristics, “big data is smart data”.

In today’s day in age it is pertinent for data to be smart data, as all other data is useless and does not add value to the firm. For example, if a clothing store collects data on their customers’ shopping patterns and habits, looking at this data by itself does nothing for the store, they are simply looking at a set of facts. However, putting this data in context and understanding what it means is the important part. Once a store can analyze the data and understand the meaning, constraints, and uses of the data, then it can be used to benefit the store by predicting when customers will come in, how they move throughout the store, and how much time they spend in the store. Then, this information can be used by the store to make more sales.  

Dennis argues that, “Smart Data is Trusted”, I agree with this statement, but only to a certain extent. The phrase “numbers don’t lie” can be applied to her argument, however, if the data is not collected properly or there are many outliers in the data set then the results will be skewed and will not be as beneficial to the firm. I do agree with Dennis when she says, “Smart Data is Relevant”. In this case relevance is data curation which involves the process of reviewing, sorting, ranking, classifying and recommending the data’s relevance to other users downstream. It is important to be able to sift through the massive amounts of data to determine which parts of a business will benefit from what data, versus just sending all the data to everyone, which would be much more inefficient.


One point that Dennis did not bring up that I feel she missed is that smart data has monetary value. If a company understands their big/smart data, this will lead to increased success for the company. Due to this fact, I believe the data itself has a monetary value, as it increases the value of the firm. The challenge is that it is very hard, nearly impossible, to put a value on the data because there is no simple calculation to see how much improvement in a firm is strictly due to their usage and implementation of policies which stemmed from big/smart data. In the future companies should work towards finding a way to put a monetary value on their data, and until that happens they should continue to gather and analyze big data as a means to improving their business.


1 comment:

  1. I chose to comment on this article and post mainly because of the emphasis placed upon the retail sales environment. Having worked retail for over 3 years, I have actively been involved in pilot programs regarding data aggregation and analysis.

    Meghan discusses the need to provide context for data; this is a crucial step in a successful analysis. But what is contextual at the sales floor-level isn't necessarily clear to individuals looking at the same data "higher-up" in the organization. I can provide a real-life example.

    While working at a small, privately-owned clothing chain in Towson, upper management had deployed store traffic-tracking sensors, and the data amassed from the sensors was, at first, analyzed by upper-level management only (none of which had sales-floor experience on a daily level). After their struggle to interpret the results, our store's managers and many of the associates gathered to discuss the data, and we effectively rearranged the layout of the merchandise based upon heat-maps of the store and our working knowledge of regular customer interests.

    So how does this anecdote relate to the post? In order for data collection and analysis to be effective, the analysts need to have a very keen sense of how, where, and when the data was amassed. The result is more comprehensive and efficient reporting, in turn leading to better results and in the case of sales, top and bottom line growth.

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