Introduction—Big Data Analysis
With the advent of modern technology and the advances in science, data analysis has become an important factor for the growth of any organization. Whether it is a small cafe or a well-established franchise, with the right kind of information each business has the potential to grow. However, due to an abundance of information with a multitude of variations, decision making at times becomes difficult. In such cases, a systematic approach needs to be adopted that first allows a proper way of collecting relevant information, organizing them into respective categories, determining irrelevant or ambiguous information, and applying analytical tools to determine patterns, trends, and other useful information.
Enterprises such as Coca-Cola and Netflix are utilizing real-time information to determine what approach to adopt for efficient business communication and better customer satisfaction. To understand how this is possible, one needs to first understand what Big Data is and how it can be utilized to improve efficiency, overall productivity, and simply growing profits.
Big Data and The Five ‘V’ Rules Of Big Data
Big Data is a process to extract useful data from a cluster of information. One would ask what is this cluster of information? Or perhaps how do we know which data is useful? To answer that, we follow a simple five “v” rules i.e. volume, velocity, variety, veracity, and value.
To explain this, let us consider an example of a cafe that sells various edible items like drinks, cakes, pastries, sandwiches, and other food-related items. In this scenario, the price information, the number of customers arriving and purchasing at various times, the time it takes to service each customer, and the quality of service or food items can all be considered a volume of information.
The “volume” is simply a large dataset from which information needs to be extracted. The “velocity” here can be identified as how fast the information is accumulated. This can be through customer surveys, social media reviews, computerized cash counter, and so-on.
The “variety” is the nature of the accumulated data which is further categorized in to structured, semi-structured, and unstructured. The difference between the three categories is how well organized the collected information is. For instance, an example of the organized dataset can be an excel sheet record of item purchases throughout the day while social media reviews, comments, and the popular “tweets” can be considered as an unorganized dataset.
The “veracity” in this is how accurate the information is. While acquiring various reviews, if a person has some form of relationship with the cafe owner, they might be biased in giving a positive review. On the other hand, a disgruntled competitor might give a bad review. Both of these scenarios can be considered as outliers of the data which might deviate the analysis of the actual result.
After understanding the other four rules, the fifth rule, “value”, is perhaps the most important one. Although we are collecting the information, either organized or unorganized, it is of no use if no analysis is performed either statistical or non-statistical. Only after analyzing the cluster of information and extracting trends, patterns or some form of useful information helps in advertising, branding, or maintaining brand loyalty which in turn adds value to a business.
If we consider our cafe scenario, simply knowing that an item is purchased every day is not enough. We need other key identifiers such as at what time the purchase was made and in what quantity. The correlation between the three can help us identify what food items will be needed to be prepared in the morning or at lunchtime with more quantity and better quality to improve sales; consequently, improving customer satisfaction, business profits, or overall image of the cafe.
Technologies In Big Data Analytics
Today, corporations worldwide are starting to converge their marketing strategies by working on Big Data analysis based on their businesses’ respective types. There is not a single technology that can collect, organize, and analyze information and provide a statistical result to the user in one go. Instead, it is a collection of key technologies that helps Big Data analysis to reach its goal.
These technologies include machine learning, data management, data mining, predictive analysis, etc. Using these technologies, businesses or organizations can accumulate data in an organized fashion. Perform various statistical techniques and predict an emerging trend in the data. They can also find a hidden pattern inside the accumulated data and utilize all this information for better decision making and marketing strategies.
Talk about Big Data analysis or AI, if you are looking for any assistance or help in any of the related technologies, please free to reach out to us at vteams. Let’s talk and figure out the best possible outcomes for your business.