Introduction To Data Science And Big Data

November 14, 2016
Posted by avidamiri

One topic that ecommerce entrepreneurs should be cognizant of is the role of data science in the online industry.  The reality is that almost every activity or consumer experience you have online can be actively tracked and that data is actively being collected.

This might include the online stores that you have visited and the very products you may have viewed or the items you may have the placed into an abandoned cart without checking out.  If you visited some review sites prior to making a purchase that is something that is also tracked by various apps and the data collected.

If you then went to some social media sites and posted on Facebook for instance or spent some time investigating the items on Pinterest that is also valuable information for marketers.   Now consider that this data is being gleaned from you but also millions upon millions of other online consumers and we have entered the realm of big data.

Big data itself is of limited value however unless it is organized, filtered and sorted.  This process is know as data science and it is a rapidly emerging field.  At the end of the day, big data is only as good as the data science involved in interpreting this data and that is where the data can be converted into the type of intelligence that is both predictive and influential for marketers.

The goal of course is for marketers (or the data scientist that interpret this data and then inform them) to identify patterns of behavior for consumers such as cross referencing buying patterns, purchasing probabilities, to target advertising campaigns, make strategic new capital investments, build up particular brand, etc.  However, the role of big data and data science has not been limited to marketing based around consumer activity.

This field has now become more far reaching and pervasive. It is  used to track and predict traffic deaths or sickness outbreaks.  There are sophisticated algorithms developed for each that can be extremely useful in identifying trends and preparing for them.  This may include taking preemptive steps to avoid a negative outcome or providing decision makers with necessary information that they would not have identified otherwise so they then can develop other solutions.

On the consumer side, data science and the development of data science algorithms may be especially helpful for existing companies that are looking to expand their product lines into product categories that will generate the highest new revenue.  While a store manager or buyer, may use tools to manually predict market trends this process is now informed by big data and data science in new and highly effective ways.

By collecting and evaluating massive amounts of data including facebook posts to online keyword activity, the data scientist may develop results that demonstrate that consumer preferences are moving away from the items current product mix or the items being carried by that retailer into new or emerging product categories. For example, if a grocery retailer is examining their health and beauty section and has recognized through data science that the current lineup of holistic based toothpaste options is being overshadowing by new consumer interest in whiting toothpaste options, then this is essential information in new purchases and distribution.

Data scientist may cull information for thousands of online posts, blogs, tweets, or even news articles to develop the data to then draw this conclusion.  This can provide the retailer with a definitive edge.


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